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Background: This study aimed to assess the acceptability, feasibility, and preliminary effectiveness of a crowdsourced HIV partner services (PS) intervention among men who have sex with men living with HIV (MLWH) in China., Methods: A pilot 2-arm randomized controlled trial was conducted in 3 HIV testing clinics in China. The control arm received conventional HIV PS, whereas the intervention arm received a crowdsourced intervention, including HIV self-testing kits for secondary distribution (HIVST-SD), digital education materials, and assisted PS. The intervention was developed through 2-phase crowdsourcing events including an open call and a Designathon. The primary outcomes were measured by the 3-month follow-up rate (i.e., the proportion of participants who completed the follow-up survey to report HIV PS outcomes 3 months after enrollment) and the frequency of using intervention components (feasibility), index evaluation of intervention components (acceptability), and the proportion of partners getting HIV testing (preliminary effectiveness)., Results: The study enrolled 121 newly diagnosed MLWH between July 2021 and May 2022. The 3-month follow-up rates were 93% (75 of 81) and 83% (33 of 40) in the intervention and control arms, respectively. Crowdsourced intervention components demonstrated feasibility, with all indexes using digital educational materials, 23 successfully using HIVST-SD, and 6 employing provider-referral to notify 9 sexual partners. Acceptability was high, with HIVST-SD and digital educational materials rated 4.4 and 4.1 out of 5. The proportion of partners receiving HIV testing was 11% higher in the intervention arm than in the control arm (marginal significance with 95% confidence interval, -2% to 24%; 38% vs. 27%)., Conclusions: The crowdsourced HIV PS intervention was acceptable and feasible, suggesting the potential to facilitate partner HIV testing among Chinese MLWH. Further implementation research is recommended to expand HIV PS among key populations in low- and middle-income countries., Clinical Trial Registration Id: NCT04971967 (Protocol ID: 19-0496)., Competing Interests: Conflict of Interest and Sources of Funding: This study received funding support from the US National Institute of Mental Health R34 Project (grant number: MH119963). The authors declare no additional competing interests., (Copyright © 2024 American Sexually Transmitted Diseases Association. All rights reserved.)

Background: Several health institutions developed strategies to improve health content on Wikimedia platforms given their unparalleled reach. The objective of this study was to compare an online volunteer-based Wikimedia outreach campaign with university course Wikipedia assignments (both focused on improving hearing health content in Wikimedia's public digital knowledge archives), in terms of the reach of the contributions and the extent of the participants' input. A secondary objective was to examine the feasibility and the implementation of the different strategies., Methods: The research team partnered for the (1) coordination of improvements in hearing and healthcare content through educational programs using Wikimedia platforms, (2) participation in the global campaign Wiki4WorldHearingDay2023 and (3) evaluation of the proposed strategies. Metrics used in the comparison of the two strategies included the number of articles edited, number of views of the edited articles (as reach) and the extent of edits, captured as the number of words. The feasibility evaluation included assessing recruitment success and the implementation of the proposed plan among faculty, students from various university programs, and volunteers representing different constituencies., Results: The effort increased the availability of quality plain language information on hearing conditions and hearing care. Both strategies demonstrated to be feasible by their success in recruiting participants who contributed to the effort and by measurable outputs as edits. The contribution of content to Wikimedia platforms as part of education activities provided a more robust result. Wiki4WorldHearingDay2023 145 participants (78 from educational programs) contributed 167,000 words, 258 + references and 140 images to 322 Wikipedia articles (283 existing and 39 new ones), which were viewed 16.5 million times. Contributions occurred in six languages. Edits in Portuguese, mainly by those involved in educational programs, led the number of articles (226 or 70.2%) that were expanded or created during the 5-month tracking period., Conclusions: The elements that contributed to the success of the studied strategies include an impact topic, coordination with educational programs, international multidisciplinary collaborations, the dissemination of the initiative in several platforms, connection with a robust local Wikimedia affiliate, and the use of a technical infrastructure that provides metrics and coordination mechanisms., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: Reduction mammaplasty relieves macromastia symptoms while improving breast aesthetics, although the ideal breast aesthetically has been shown to differ culturally in previous crowdsourcing studies. Better understanding these differences can aid in setting postoperative expectations., Objectives: The aim of this study was to characterize the ideal reduction mammaplasty according to demographics such as gender, ethnicity, socioeconomic status, and education., Methods: A crowdsourcing platform was utilized to collect 10,169 deidentified responses. Users completed 1 of 3 surveys, either a preoperative, postoperative, or preoperative and postoperative paired survey. The preoperative and postoperative surveys addressed 10 breast measurements, including upper breast slope, projection proportion, nipple position, breast width, and breast fullness. The paired preoperative and postoperative survey assessed the nipple-areola complex (NAC), chest fit, symmetry improvement, and scarring., Results: Preoperative images were rated more aesthetic than postoperative images. This was consistent across all demographics evaluated. Females, African Americans, Asians, participants ages 55+, and participants with no high school degree or a graduate degree found the most improvement in breast symmetry (P = .001, P = .002, P = .027, P < .001, P = .01). Male and Hispanic participants were most likely to see no change in symmetry (P = .008, P = .04), and South Asian participants found breasts less symmetric postoperatively (P < .001). There were significant demographic differences in aesthetic ratings of the NAC, scarring, and breast fit., Conclusions: Perceived breast aesthetics after reduction mammaplasty vary significantly across demographics, including gender, ethnicity, age, socioeconomic status, and educational achievement. Surgeons should consider demographics when planning each patient's reduction mammaplasty., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Aesthetic Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Atrial fibrillation (AFib) detection via mobile ECG devices is promising, but algorithms often struggle to generalize across diverse datasets and platforms, limiting their real-world applicability. Objective: This study aims to develop a robust, generalizable AFib detection approach for mobile ECG devices using crowdsourced algorithms. Methods: We developed a voting algorithm using random forest, integrating six open-source AFib detection algorithms from the PhysioNet Challenge. The algorithm was trained on an AliveCor dataset and tested on two disjoint AliveCor datasets and one Apple Watch dataset. Results: The voting algorithm outperformed the base algorithms across all metrics: the average of sensitivity (0.884), specificity (0.988), PPV (0.917), NPV (0.985), and F1-score (0.943) on all datasets. It also demonstrated the least variability among datasets, signifying its highest robustness and effectiveness in diverse data environments. Moreover, it surpassed Apple's algorithm on all metrics and showed higher specificity but lower sensitivity than AliveCor's Kardia algorithm. Conclusions: This study demonstrates the potential of crowdsourced, multi-algorithmic strategies in enhancing AFib detection. Our approach shows robust cross-platform performance, addressing key generalization challenges in AI-enabled cardiac monitoring and underlining the potential for collaborative algorithms in wearable monitoring devices.

Carelessness or insufficient effort responding is a widespread problem in online research, with estimates ranging from 3% to almost 50% of participants in online surveys being inattentive. While detecting carelessness has been subject to multiple studies, the factors that reduce or prevent carelessness are not as well understood. Initial evidence suggests that warning statements prior to study participation may reduce carelessness, but there is a lack of conclusive high-powered studies. This preregistered randomized controlled experiment aimed to test the effectiveness of a warning statement and an improved implementation of a warning statement in reducing participant inattention. A study with 812 participants recruited on Amazon Mechanical Turk was conducted. Results suggest that presenting a warning statement is not effective in reducing carelessness. However, requiring participants to actively type the warning statement statistically significantly reduced carelessness as measured with self-reported diligence, even-odd consistency, psychometric synonyms and antonyms, and individual response variability. The active warning statements also led to statistically significantly more attrition and potentially deterred those who were likely to be careless from even participating in this study. We show that the current standard practice of implementing warning statements is ineffective and novel methods to prevent and deter carelessness are needed., (© 2024. The Author(s).)

Purpose: T 1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T 1 mapping technique, using acquisition details from a seminal T 1 mapping paper on a standardized phantom and in human brains., Methods: The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T 1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed., Results: Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org., Conclusion: The T 1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T 1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T 1 variations in vivo., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Background: Infectious disease surveillance is difficult in many low- and middle-income countries. Information market (IM)-based participatory surveillance is a crowdsourcing method that encourages individuals to actively report health symptoms and observed trends by trading web-based virtual "stocks" with payoffs tied to a future event., Objective: This study aims to assess the feasibility and acceptability of a tailored IM surveillance system to monitor population-level COVID-19 outcomes in Accra, Ghana., Methods: We designed and evaluated a prediction markets IM system from October to December 2021 using a mixed methods study approach. Health care workers and community volunteers aged ≥18 years living in Accra participated in the pilot trading. Participants received 10,000 virtual credits to trade on 12 questions on COVID-19-related outcomes. Payoffs were tied to the cost estimation of new and cumulative cases in the region (Greater Accra) and nationwide (Ghana) at specified future time points. Questions included the number of new COVID-19 cases, the number of people likely to get the COVID-19 vaccination, and the total number of COVID-19 cases in Ghana by the end of the year. Phone credits were awarded based on the tally of virtual credits left and the participant's percentile ranking. Data collected included age, occupation, and trading frequency. In-depth interviews explored the reasons and factors associated with participants' user journey experience, barriers to system use, and willingness to use IM systems in the future. Trading frequency was assessed using trend analysis, and ordinary least squares regression analysis was conducted to determine the factors associated with trading at least once., Results: Of the 105 eligible participants invited, 21 (84%) traded at least once on the platform. Questions estimating the national-level number of COVID-19 cases received 13 to 19 trades, and obtaining COVID-19-related information mainly from television and radio was associated with less likelihood of trading (marginal effect: -0.184). Individuals aged <30 years traded 7.5 times more and earned GH ¢134.1 (US $11.7) more in rewards than those aged >30 years (marginal effect: 0.0135). Implementing the IM surveillance was feasible; all 21 participants who traded found using IM for COVID-19 surveillance acceptable. Active trading by friends with communal discussion and a strong onboarding process facilitated participation. The lack of bidirectional communication on social media and technical difficulties were key barriers., Conclusions: Using an IM system for disease surveillance is feasible and acceptable in Ghana. This approach shows promise as a cost-effective source of information on disease trends in low- and middle-income countries where surveillance is underdeveloped, but further studies are needed to optimize its use., (©Gifty Marley, Phyllis Dako-Gyeke, Prajwol Nepal, Rohini Rajgopal, Evelyn Koko, Elizabeth Chen, Kwabena Nuamah, Kingsley Osei, Hubertus Hofkirchner, Michael Marks, Joseph D Tucker, Rosalind Eggo, William Ampofo, Sean Sylvia. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 12.08.2024.)

Crowdsourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time. In these systems, the final answer to the question is determined by summing up the votes obtained from the community. The popularity of these systems has increased by facilitating access for community members through mobile phones and the Internet. One of the issues raised in crowdsourcing is how to choose people and how to collect answers. Usually, users are separated based on their performance in a pre-test. Designing the pre-test for performance calculation is challenging; The pre-test questions should be selected to assess characteristics in individuals that are relevant to the main questions. One of the ways to increase the accuracy of crowdsourcing systems is by considering individuals' cognitive characteristics and decision-making models to form a crowd and improve the estimation of their answer accuracy to questions. People can estimate the correctness of their responses while making a decision. The accuracy of this estimate is determined by a quantity called metacognition ability. Metacoginition is referred to the case where the confidence level is considered along with the answer to increase the accuracy of the solution. In this paper, by both mathematical and experimental analysis, we would answer the following question: Is it possible to improve the performance of a crowdsourcing system by understanding individuals' metacognition and recording and utilizing users' confidence in their answers?, (© 2024. The Author(s).)

One of the issues that can potentially affect the internal validity of interactive online experiments that recruit participants using crowdsourcing platforms is collusion: participants could act upon information shared through channels that are external to the experimental design. Using two experiments, I measure how prevalent collusion is among MTurk workers and whether collusion depends on experimental design choices. Despite having incentives to collude, I find no evidence that MTurk workers collude in the treatments that resembled the design of most other interactive online experiments. This suggests collusion is not a concern for data quality in typical interactive online experiments that recruit participants using crowdsourcing platforms. However, I find that approximately 3% of MTurk workers collude when the payoff of collusion is unusually high. Therefore, collusion should not be overlooked as a possible danger to data validity in interactive experiments that recruit participants using crowdsourcing platforms when participants have strong incentives to engage in such behavior., (© 2023. The Author(s).)

Importance: Residents must prepare for effective communication with patients after medical errors. The video-based communication assessment (VCA) is software that plays video of a patient scenario, asks the physician to record what they would say, engages crowdsourced laypeople to rate audio recordings of physician responses, and presents feedback to physicians., Objective: To evaluate the effectiveness of VCA feedback in resident error disclosure skill training., Design, Setting, and Participants: This single-blinded, randomized clinical trial was conducted from July 2022 to May 2023 at 7 US internal medicine and family medicine residencies (10 total sites). Participants were second-year residents attending required teaching conferences. Data analysis was performed from July to December 2023., Intervention: Residents completed 2 VCA cases at time 1 and were randomized to the intervention, an individual feedback report provided in the VCA application after 2 weeks, or to control, in which feedback was not provided until after time 2. Residents completed 2 additional VCA cases after 4 weeks (time 2)., Main Outcomes and Measures: Panels of crowdsourced laypeople rated recordings of residents disclosing simulated medical errors to create scores on a 5-point scale. Reports included learning points derived from layperson comments. Mean time 2 ratings were compared to test the hypothesis that residents who had access to feedback on their time 1 performance would score higher at time 2 than those without feedback access. Residents were surveyed about demographic characteristics, disclosure experience, and feedback use. The intervention's effect was examined using analysis of covariance., Results: A total of 146 residents (87 [60.0%] aged 25-29 years; 60 female [41.0%]) completed the time 1 VCA, and 103 (70.5%) completed the time 2 VCA (53 randomized to intervention and 50 randomized to control); of those, 28 (54.9%) reported reviewing their feedback. Analysis of covariance found a significant main effect of feedback between intervention and control groups at time 2 (mean [SD] score, 3.26 [0.45] vs 3.14 [0.39]; difference, 0.12; 95% CI, 0.08-0.48; P = .01). In post hoc comparisons restricted to residents without prior disclosure experience, intervention residents scored higher than those in the control group at time 2 (mean [SD] score, 3.33 [0.43] vs 3.09 [0.44]; difference, 0.24; 95% CI, 0.01-0.48; P = .007). Worse performance at time 1 was associated with increased likelihood of dropping out before time 2 (odds ratio, 2.89; 95% CI, 1.06-7.84; P = .04)., Conclusions and Relevance: In this randomized clinical trial, self-directed review of crowdsourced feedback was associated with higher ratings of internal medicine and family medicine residents' error disclosure skill, particularly for those without real-life error disclosure experience, suggesting that such feedback may be an effective way for residency programs to address their requirement to prepare trainees for communicating with patients after medical harm., Trial Registration: ClinicalTrials.gov Identifier: NCT06234085.

Outdoor environments extend living spaces as venues for various activities. Comfortable open public spaces can positively impact citizens' health and well-being, thereby improving the livability and resilience of cities. Considering the visitors' perception of these environments in comfort studies is crucial for ensuring their well-being and promoting the use of these spaces. However, traditional survey methods may be time- and resource-consuming to gather significant sample sizes, usually focusing on selected homogeneous samples. Crowdsourced data, then, has emerged as an alternative for assessing human perception, as it eases the collection of subjective feedback and potentially amplifies impact and inclusivity. This study presents a strategic approach for analyzing publicly available and willingly reported crowdsourced data from a digital mapping platform in outdoor comfort evaluations, aiming to verify whether these data are informative regarding environmental quality perception and to identify the environmental factors that people are most sensitive to. Urban parks located in New York City served as a case study. A multi-source, interdisciplinary information framework combined crowdsourced reviews with environmental data used to determine prevailing thermal conditions. Overall perception of parks was well-rated, revealing that their attractions and activities are probably the most appealing characteristics for park attendance. Regarding environmental perception, acoustic and thermal factors are clearly the most influential. Acoustics were well-rated, while the main aspect regarding the thermal domain is the recognition of shading as a mitigator for hot conditions. Environmental data provided complementary insights, particularly concerning the range of thermal sensations experienced in urban parks. The findings confirm that willingly reported crowdsourced data can provide valuable insights into urban crowd environmental perception, presenting a potentially suitable and effective method to include the human perspective in environmental quality assessments, as well as to evaluate and predict environmental-related risks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality., Objective: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data., Methods: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips., Results: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance., Conclusions: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems., (©Nicole M Duggan, Mike Jin, Maria Alejandra Duran Mendicuti, Stephen Hallisey, Denie Bernier, Lauren A Selame, Ameneh Asgari-Targhi, Chanel E Fischetti, Ruben Lucassen, Anthony E Samir, Erik Duhaime, Tina Kapur, Andrew J Goldsmith. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.07.2024.)

Online participant recruitment ("crowdsourcing") platforms are increasingly being used for research studies. While such platforms can rapidly provide access to large samples, there are concomitant concerns around data quality. Researchers have studied and demonstrated means to reduce the prevalence of low-quality data from crowdsourcing platforms, but approaches to doing so often involve rejecting work and/or denying payment to participants, which can pose ethical dilemmas. We write this essay as an associate professor and two institutional review board (IRB) directors to provide a perspective on the competing interests of participants/workers and researchers and to propose a checklist of steps that we believe may support workers' agency on the platform and lessen instances of unfair consequences to them while enabling researchers to definitively reject lower-quality work that might otherwise reduce the likelihood of their studies producing true results. We encourage further, explicit discussion of these issues among academics and among IRBs., (© 2024 The Authors. Ethics & Human Research published by Wiley Periodicals LLC on behalf of The Hastings Center.)

This study addresses the pervasive challenges of low hepatitis B (HBV) and hepatitis C (HCV) testing rates coupled with the stigma associated with these diseases in low- and middle-income countries (LMICs) with a special focus on Bangladesh. This study aims to introduce an innovative crowdsourcing intervention that involves medical students, a crucial cohort with the potential to shape healthcare attitudes. Through a structured crowdsourcing approach, the study designs and implements a digital intervention to counter stigma and promote testing among medical students in Dhaka, Bangladesh. Participants submitted brief videos or texts aiming to encourage hepatitis testing and reduce stigma. The call, advertised through meetings, emails, and social media, welcomed entries in English or Bengali over 3 weeks. A panel of six judges evaluated each entry based on clarity, impact potential, innovation, feasibility, and sustainability, awarding prizes to students behind the highest-rated submissions. Seventeen videos and four text messages received an average score of 5.5 among 440 surveyed medical students, predominantly 22 years old (16%) and in their fourth year (21%). After viewing, 360 students underwent screening, identifying two previously undiagnosed HBV cases referred for care; no HCV infections were found. Notably, 41% expressed concerns about individuals with HBV working in hospitals or having a doctor living with HBV. In conclusion, this pilot showcases the power of medical students in spearheading campaigns to counter hepatitis stigma and encourage testing. By utilizing crowdsourcing, the study introduces an innovative approach to a persistent issue in LMICs specially in Bangladesh, offering a model that could potentially be adapted by other regions grappling with similar challenges., (© 2024 John Wiley & Sons Ltd.)

Background: Solid organ transplantation improves the quality of life for patients but has significant out-of-pocket expenses for donors and recipients in the USA, leading many to utilize crowdfunding for donations to cover expenses. We sought to characterize crowdfunding for transplant patients and to identify ethical and policy issues., Methods: We obtained newspaper articles that described crowdfunding campaigns for organ transplant patients from Nexis-Uni. Using Nvivo, we identified and analyzed article details, patient characteristics, features of campaigns, additional fundraisers, and policy and ethical issues related to crowdfunding., Results: Most sources were published between 2015 and 2020. Of 231 patients identified, 43% were thoracic organ recipients and 42% were kidney recipients. GoFundMe was the most popular platform. 78% of patients reported at least one intended use of crowdfunding; medical expenses were the most cited reason. Ten percent of articles described at least one ethical or policy consideration related to crowdfunding for organ transplant. Concerns included violations of federal laws prohibiting donors from receiving "valuable consideration" for an organ, taxation of funds, loss of Medicaid or disability benefits, accountability for fund usage, and crowdfunding requirements for organ waiting list placement. In several cases, transplants were delayed due to crowdfunding concerns., Conclusions: Our findings reveal crowdfunding characteristics and financial barriers present among transplant patients. Furthermore, our study suggests that the ethical and policy implications for crowdfunding in the transplant population are not yet adequately assessed. National regulations and transplant center policies may need to be modified to address issues raised by patient crowdfunding., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: Intimate partner violence (IPV) is a pervasive and common form of violence against women. IPV is multifaceted, with physical, sexual, and/or psychological means of perpetration, and has detrimental effects on women's mental health. IPV generally affects women; however, how IPV differentially affects different groups of women is less clear. Women who are socioeconomically vulnerable are often considered at risk for IPV, although women in college are also often the topic of IPV research due both to high rates of IPV and to ease of study recruitment. There is increasing research on the effects of IPV in a third group of women, those recruited through online platforms (i.e., crowdsourcing). How IPV differs across these three samples has yet to be examined., Method: In this study, we examined differences in IPV exposure across three samples of women, at risk ( n = 144), college ( n = 654), and crowdsourced ( n = 168), using a Bayesian approach to general linear modeling., Results: Results indicated that the majority of women in each sample experienced some IPV. Results further suggested that women in the crowdsourced sample had the highest exposure to IPV in general and to physical IPV, sexual IPV, and IPV-related injury in particular, whereas women in the at-risk sample had the highest rates of psychological IPV., Conclusion: These findings highlight the importance of sampling in studies of IPV and thus have ramifications for future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Background: In the digital age, search engines and social media platforms are primary sources for health information, yet their commercial interests-focused algorithms often prioritize irrelevant content. Web-based health applications by reputable sources offer a solution to circumvent these biased algorithms. Despite this advantage, there remains a significant gap in research on the effective integration of content-ranking algorithms within these specialized health applications to ensure the delivery of personalized and relevant health information., Objective: This study introduces a generic methodology designed to facilitate the development and implementation of health information recommendation features within web-based health applications., Methods: We detail our proposed methodology, covering conceptual foundation and practical considerations through the stages of design, development, operation, review, and optimization in the software development life cycle. Using a case study, we demonstrate the practical application of the proposed methodology through the implementation of recommendation functionalities in the EndoZone platform, a platform dedicated to providing targeted health information on endometriosis., Results: Application of the proposed methodology in the EndoZone platform led to the creation of a tailored health information recommendation system known as EndoZone Informatics. Feedback from EndoZone stakeholders as well as insights from the implementation process validate the methodology's utility in enabling advanced recommendation features in health information applications. Preliminary assessments indicate that the system successfully delivers personalized content, adeptly incorporates user feedback, and exhibits considerable flexibility in adjusting its recommendation logic. While certain project-specific design flaws were not caught in the initial stages, these issues were subsequently identified and rectified in the review and optimization stages., Conclusions: We propose a generic methodology to guide the design and implementation of health information recommendation functionality within web-based health information applications. By harnessing user characteristics and feedback for content ranking, this methodology enables the creation of personalized recommendations that align with individual user needs within trusted health applications. The successful application of our methodology in the development of EndoZone Informatics marks a significant progress toward personalized health information delivery at scale, tailored to the specific needs of users., (©Wenhao Li, Rebecca O'Hara, M Louise Hull, Helen Slater, Diksha Sirohi, Melissa A Parker, Niranjan Bidargaddi. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 29.05.2024.)

Per- and polyfluoroalkyl substances (PFAS) are a class of persistent chemicals that have been associated with a diverse array of adverse environmental and human health related effects. In addition to a growing list of health concerns, PFAS are also ubiquitously used and pervasive in our natural and built environments, and they have an innate ability to be highly mobile once released into the environment with an unmatched ability to resist degradation. As such, PFAS have been detected in a wide variety of environmental matrices, including soil, water, and biota; however, the matrix that largely dictates human exposure to PFAS is drinking water, in large part due to their abundance in water sources and our reliance on drinking water. As Florida is heavily reliant upon water and its varying sources, the primary objective of this study was to survey the presence of PFAS in drinking water collected from taps from the state of Florida (United States). In this study, 448 drinking water samples were collected by networking with trained citizen scientists, with at least one sample collected from each of the 67 counties in Florida. Well water, tap water, and bottled water, all sourced from Florida, were extracted and analyzed (31 PFAS) using isotope dilution and ultra-high-performance liquid chromatography - tandem mass spectrometry (UHPLC-MS/MS). Overall, when examining ∑PFAS: the minimum, maximum, median, and mean were ND, 219, 2.90, and 14.06 ng/L, respectively. The data herein allowed for a comparison of PFAS in drinking water geographically within the state of Florida, providing vital baseline concentrations for prospective monitoring and highlighting hotspots that require additional testing and mitigation. By incorporating citizen scientists into the study, we aimed to educate impacted communities regarding water quality issues and solutions., Competing Interests: Declaration of competing interest The authors declare that they have no known financial competing interests or personal relationships that would appear to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Efficient NTDs elimination strategies require effective surveillance and targeted interventions. Traditional methods are costly and time-consuming, often failing to cover entire populations in case of movement restrictions. To address these challenges, a morbidity image-based surveillance system is being developed. This innovative approach which leverages the smartphone technology aims at simultaneous surveillance of multiple NTDs, enhancing cost-efficiency, reliability, and community involvement, particularly in areas with movement constraints. Moreover, it holds promise for post-elimination surveillance., Methodology: The pilot of this method will be conducted across three states in southern Nigeria. It will target people affected by Neglected Tropical Diseases and members of their communities. The new surveillance method will be introduced to target communities in the selected states through community stakeholder's advocacy meetings and awareness campaigns. The pilot which is set to span eighteen months, entails sensitizing NTDs-affected individuals and community members using signposts, posters, and handbills, to capture photos of NTDs manifestations upon notice using smartphones. These images, along with pertinent demographic information, will be transmitted to a dedicated server through WhatsApp or Telegram accounts. The received images will be reviewed and organized at backend and then forwarded to a panel of experts for identification and annotation to specific NTDs. Data generated, along with geocoordinate information, will be used to create NTDs morbidity hotspot maps using ArcGIS. Accompanying metadata will be used to generate geographic and demographic distributions of various NTDs identified. To protect privacy, people will be encouraged to send manifestation photos of the affected body part only without any identifiable features., Evaluation Protocol: NTDs prevalence data obtained using conventional surveillance methods from both the pilot and selected control states during the pilot period will be compared with data from the CIMS-NTDs method to determine its effectiveness., Expected Results and Conclusion: It is expected that an effective, privacy-conscious, population inclusive new method for NTDs surveillance, with the potential to yield real-time data for the identification of morbidity hotspots and distribution patterns of NTDs will be established. The results will provide insights into the effectiveness of the new surveillance method in comparison to traditional approaches, potentially advancing NTDs elimination strategies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Chukwuocha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: This article characterizes the educational needs of parents following fetal or neonatal congenital heart disease (CHD) diagnosis and generates recommendations for meeting these needs., Study Design: Online crowdsourcing methods were used to collect qualitative data from 95 parents of children with CHD regarding their needs for education and preparation following fetal or neonatal diagnosis. Data were analyzed using qualitative methods and themes were organized around the substructure of met and unmet needs., Results: Two themes represented consistently met needs, whereas 10 themes represented needs that were either inconsistently met or consistently unmet. Parents reported needing more information about social, emotional, and financial supports, preparation for long-term care, and guidance toward reputable online resources. Parents also provided recommendations for meeting these needs., Conclusion: Parents' needs for education and preparation following CHD diagnosis are much broader in scope than what they currently receive. Addressing these may support parental coping and active participation in medical decision-making., Key Points: · CHD counseling after diagnosis may provide opportunities to promote parents' mental health.. · Guidelines recommend that this counseling should include emotional and decision-making support, however, it is unclear what parents actually receive.. · This study found that parents' needs for education and preparation following CHD diagnosis are much broader in scope than what they currently receive.., Competing Interests: None declared., (Thieme. All rights reserved.)

Background: Many adolescents and young adults (AYAs; 10-24 years old) are excluded from HIV research because of social, ethical, and legal challenges with informed consent, resulting in limited AYA-focused data. We use a participatory approach to identify strategies for improving AYA consent processes in HIV research in low- and middle-income countries (LMICs)., Methods: We conducted a digital crowdsourcing open call for ideas to improve AYA consent to HIV research in LMICs. Crowdsourcing involves engaging a group of people in problem-solving, then sharing emergent solutions. Submissions were evaluated by 3 independent judges using predefined criteria, with exceptional strategies receiving prizes. Demographic data were collected, and textual data were qualitatively analyzed for emergent themes in barriers and facilitators for improving AYA consent in HIV research, guided by a socioecological model., Results: We received 110 strategies total; 65 were eligible for evaluation, 25 of which were identified as finalists. Fifty-eight participants from 10 LMICs submitted the 65 eligible submissions, of which 30 (52%) were 18 to 24 years old. Thematic analysis identified 10 barriers to AYA consent, including HIV stigma, limited education, and legal/regulatory barriers. Strategies for improving AYA consent processes revealed 7 potential facilitators: enhancing AYA engagement in research, involving parents/guardians, improving education/awareness, improving institutional practices/policy, making research participation more AYA-friendly, enhancing engagement of other key communities of interest, and empowering AYA., Conclusions: Diverse communities of interest in LMICs developed compelling strategies to enhance informed consent that may improve AYA inclusion in HIV research. These data will be used to develop practical guidance on improving AYA consent processes., Competing Interests: Conflict of Interest and Sources of Funding: The authors declare no conflict of interest exists: This work was supported by The Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant number 4UH3HD096929-03). K. M. T. is supported by the National Institute of Allergy and Infectious Diseases (grant number: T32AI007001)., (Copyright © 2024 American Sexually Transmitted Diseases Association. All rights reserved.)

We present VaxConcerns, a taxonomy for vaccine concerns and misinformation. VaxConcerns is an easy-to-teach taxonomy of concerns and misinformation commonly found among online anti-vaccination media and is evaluated to produce high-quality data annotations among crowdsource workers, opening the potential adoption of the framework far beyond just academic or medical communities. The taxonomy shows high agreement among experts and outperforms existing taxonomies for vaccine concerns and misinformation when presented to non-expert users. Our proof-of-concept study on the changes in anti-vaccination content during the COVID-19 pandemic indicate impactful future use cases, such as longitudinal studies of the shift in vaccine concerns over time., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care., (©Peter Washington. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.04.2024.)

Threatened species monitoring can produce enormous quantities of acoustic and visual recordings which must be searched for animal detections. Data coding is extremely time-consuming for humans and even though machine algorithms are emerging as useful tools to tackle this task, they too require large amounts of known detections for training. Citizen scientists are often recruited via crowd-sourcing to assist. However, the results of their coding can be difficult to interpret because citizen scientists lack comprehensive training and typically each codes only a small fraction of the full dataset. Competence may vary between citizen scientists, but without knowing the ground truth of the dataset, it is difficult to identify which citizen scientists are most competent. We used a quantitative cognitive model, cultural consensus theory, to analyze both empirical and simulated data from a crowdsourced analysis of audio recordings of Australian frogs. Several hundred citizen scientists were asked whether the calls of nine frog species were present on 1260 brief audio recordings, though most only coded a fraction of these recordings. Through modeling, characteristics of both the citizen scientist cohort and the recordings were estimated. We then compared the model's output to expert coding of the recordings and found agreement between the cohort's consensus and the expert evaluation. This finding adds to the evidence that crowdsourced analyses can be utilized to understand large-scale datasets, even when the ground truth of the dataset is unknown. The model-based analysis provides a promising tool to screen large datasets prior to investing expert time and resources., (© 2023. The Psychonomic Society, Inc.)

Objective: To describe phalloplasty subunits and determine the preferred crowdsourced esthetics. Esthetic ideals are often used to guide reconstruction, and there has been an increase in the number of gender-affirming surgeries and reconstructive phalloplasties performed. However, there is a paucity of literature describing ideal phalloplasty esthetics., Methods: Phallus esthetic subunits were defined, and a split testing-based survey was used. Subjects were solicited via Craigslist, Amazon Mechanical Turk, and Reddit and distributed among health care co-workers. Computer-generated images with variable ratios of glans, corona, and shaft were provided and respondents were asked to select the most esthetically pleasing photo. Demographic information was gathered. Univariate and multivariate regression were performed., Results: A total of 1029 people responded to the survey request and 909 people (88.3%) completed the entire survey. There were 440 respondents who self-identified as male, 334 female, 92 transgender male, and 25 transgender female. The health care field was the profession for 55.4%. Health care providers had 65.3% higher odds of preferring the longer shaft length-to-width ratio, 30.3% less odds of preferring a bilateral taper of the glans, and 48.4% less odds of preferring an angulated shaft compared to non-health care providers (P = .006, P = .021, P <.001, respectively). When compared to males, transgender females were more than 13 times likely to prefer an angulated glans corona junction (P = .008)., Conclusion: The ideal phallic esthetic varies by individual, and there were statistically significant preferences across age, education, health care status, gender, and sexual orientation. This study can serve as a guide on phalloplasties for patients and gender-affirming surgeons., Competing Interests: Declaration of Competing Interest The authors declare that they have no relevant financial interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Abstract: Crowdsourced registries have been used to quickly gather information, especially during emerging public health concerns. Registries that began during the COVID-19 pandemic were used to rapidly answer key questions on coinfections, experimental treatments, and morbidity and mortality outcomes. Registries are also used more frequently to support clinical trials and track long-term outcomes in patient populations. This article reviews registry methodology, including the collection of data from crowdsourcing and real-world sources, that can be applied to nurse researcher and clinical research nurse skill sets. The authors illustrate a recently reported crowdsourced COVID-19 and cryptococcal disease registry that followed project management strategies and the Agency for Healthcare Research and Quality registry guidelines for planning, execution, and analysis of registries and other research methods., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Since the catapult of online learning during the COVID-19 pandemic, most simulation laboratories are now completed virtually, leaving a gap in skills training and potential for technical skills decay. Acquiring standard, commercially available simulators is prohibitively expensive, but three-dimensional (3D) printing may provide an alternative. This project aimed to develop the theoretical foundations of a crowdsourcing Web-based application (Web app) to fill the gap in health professions simulation training equipment via community-based 3D printing. We aimed to discover how to effectively leverage crowdsourcing with local 3D printers and use these resources to produce simulators via this Web app accessed through computers or smart devices., Methods: First, a scoping literature review was conducted to discover the theoretical underpinnings of crowdsourcing. Second, these review results were ranked by consumer (health field) and producer (3D printing field) groups via modified Delphi method surveys to determine suitable community engagement strategies for the Web app. Third, the results informed different app iteration ideas and were then generalized beyond the app to address scenarios entailing environmental changes and demands., Results: A scoping review revealed 8 crowdsourcing-related theories. Three were deemed most suitable for our context by both participant groups: Motivation Crowding Theory, Social Exchange Theory, and Transaction Cost Theory. Each theory proposed a different crowdsourcing solution that can streamline additive manufacturing within simulation while applicable to multiple contexts., Conclusions: Results will be aggregated to develop this flexible Web app that adapts to stakeholder needs and ultimately solves this gap by delivering home-based simulation via community mobilization., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Society for Simulation in Healthcare.)

We developed a mobile application to promote healthy lifestyles and collect non-communicable disease (NCD) data in Mexico. Its theoretical foundations are supported by a framework-guided literature review. With design sprints, Scrum, Model-View-Controller, and Representational State Transfer architecture, we operationalized evidence-based nutrition/physical activity information into a crowdsourcing- and gamification-based application. The application was piloted for three months to monitor the response of 520 adults. Potential improvements were characterized, considering benchmarking, expert guidance, and standards. Salud Activa (English: Active Health) has two crowdsourcing modules: Nutritional scanner, scanning products' bar codes, providing nutritional data, and allowing new product registry feeding our databases; Surveys, comprising gradually-released NCD questions. Three intervention modules were generated: Drinks diary, a beverage assessment component to receive hydration recommendations; Step counter, monitoring users' steps via Google Fit/Health-iOS; Metabolic Avatar, interconnecting modules and changing as a function of beverage and step records. The 3-month median of Salud Activa use was seven days (IQR = 3-12), up to 35% of participants completed a Survey section, and 157 food products were registered through Nutritional scanner. Better customization might benefit usability and user engagement. Quantitative and qualitative data will enhance Salud Activa's design, user uptake, and efficacy in interventions delivered through this platform., (© 2024. The Author(s).)

In contexts where many people face barriers to accessing gender-affirming care through public systems, some turn to online crowdfunding to fundraise for private care pathways. Crowdfunding platforms invite people to share personal information, stories, and photos publicly, in order to elicit donations. In this article we draw on empirical data from a multimethodological three-year study of medical crowdfunding in Aotearoa New Zealand, with a focus on people crowdfunding for medical transition services. We apply a lens of 'visibility' to analysis of focus groups, interviews, case studies, and campaign pages, presenting findings on who was present and absent (with a focus on binary gender, and whiteness), and who was the assumed or expected audience (with a focus on cis publics). We describe how campaigns were defined by efforts to make trans bodies legible, and campaign requests competitive, through reference to narrow and medicalised frames of dysphoria, suffering, and transformation via medical intervention. We contribute to more comparative work in the literature on crowdfunding by highlighting how these globalised digital technologies are situated in the particular (demographic, cultural, and structural) contexts of Aotearoa New Zealand. We call attention to crowdfunding as a relational practice, in which the public marketisation of the self can have both individual consequences related to privacy and outing, and social consequences, in the reinforcing of trans-normativities. Overall we argue that although crowdfunding represents an adaptive strategy for trans people trying meet their own needs, it ultimately contributes to a type of trans-visibility which is both risky and limiting., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: College student and Amazon's Mechanical TURK (MTURK) samples are regularly utilized in trauma research. Recent literature, however, has criticized these samples for not being generalizable to the general U.S., Population: The purpose of this study was to determine whether college student ( n = 255) and MTURK ( n = 316) samples are invariant on the Posttraumatic Stress Disorder Checklist for DSM-5., Method: Measurement invariance using confirmatory factor analyses was used to determine whether groups are invariant across factor structure, factor loadings, item intercepts, and residual error variances on a given measure of Post-traumatic Stress Disorder (PTSD) symptom severity., Results: Model fit indices indicated the seven-factor Hybrid model was the best-fitting model, but the six-factor Anhedonia model was the most parsimonious model. Both models demonstrated equivalence in factor at the strictest level, indicating MTURK and college student samples are similar in regard to PTSD symptom severity., Conclusions: These findings provide evidence that these groups can be combined in future studies to increase sample size for trauma research. Only the Anhedonia factor exhibited mean differences between groups, which may be related to true differences between college students and MTURK survey-takers. This study provides further evidence that the findings from trauma studies using these populations are generalizable to each other. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Identifying successful approaches for reducing the belief and spread of online misinformation is of great importance. Social media companies currently rely largely on professional fact-checking as their primary mechanism for identifying falsehoods. However, professional fact-checking has notable limitations regarding coverage and speed. In this article, we summarize research suggesting that the "wisdom of crowds" can be harnessed successfully to help identify misinformation at scale. Despite potential concerns about the abilities of laypeople to assess information quality, recent evidence demonstrates that aggregating judgments of groups of laypeople, or crowds, can effectively identify low-quality news sources and inaccurate news posts: Crowd ratings are strongly correlated with fact-checker ratings across a variety of studies using different designs, stimulus sets, and subject pools. We connect these experimental findings with recent attempts to deploy crowdsourced fact-checking in the field, and we close with recommendations and future directions for translating crowdsourced ratings into effective interventions.

Objective: To evaluate the associations between baseline demographics, health conditions, pain management strategies, and health-related quality-of-life (HRQoL) measures with pain management strategies at 3-month follow-up in respondents reporting current low-back pain (LBP). Study design: Cohort study of survey data collected from adults with LBP sampled from Amazon Mechanical Turk crowdsourcing panel. Methods: Demographics, health conditions, and the Patient-Reported Outcomes Measurement Information System (PROMIS)-10 were included in the baseline survey. Respondents reporting LBP completed a more comprehensive survey inquiring about pain management strategies and several HRQoL measures. Bivariate then multivariate logistic regression estimated odds ratios (ORs) with 95% confidence intervals (CIs) for the association between baseline characteristics and pain management utilization at 3-month follow-up. Model fit statistics were evaluated to assess the predictive value. Results: The final cohort included 717 respondents with completed surveys. The most prevalent pain management strategy at follow-up was other care ( n  = 474), followed by no care ( n  = 94), conservative care only ( n  = 76), medical care only ( n  = 51), and medical and conservative care combined ( n  = 22). The conservative care only group had higher (better) mental and physical health PROMIS-10 scores as opposed to the medical care only and combination care groups, which had lower (worse) physical health scores. In multivariate models, estimated ORs (95% CIs) for the association between baseline and follow-up pain management ranged from 4.6 (2.7-7.8) for conservative care only to 16.8 (6.9-40.7) for medical care only. Additional significant baseline predictors included age, income, education, workman's compensation claim, Oswestry Disability Index score, and Global Chronic Pain Scale grade. Conclusions: This study provides important information regarding the association between patient characteristics, HRQoL measures, and LBP-related pain management utilization.

The discourse of political leaders often contains false information that can misguide the public. Fact-checking agencies around the world try to reduce the negative influence of politicians by verifying their words. However, these agencies face a problem of scalability and require innovative solutions to deal with their growing amount of work. While the previous studies have shown that crowdsourcing is a promising approach to fact-check news in a scalable manner, it remains unclear whether crowdsourced judgements are useful to verify the speech of politicians. This article fills that gap by studying the effect of social influence on the accuracy of collective judgements about the veracity of political speech. In this work, we performed two experiments (Study 1: N = 180; Study 2: N = 240) where participants judged the veracity of 20 politically balanced phrases. Then, they were exposed to social information from politically homogeneous or heterogeneous participants. Finally, they provided revised individual judgements. We found that only heterogeneous social influence increased the accuracy of participants compared to a control condition. Overall, our results uncover the effect of social influence on the accuracy of collective judgements about the veracity of political speech and show how interactive crowdsourcing strategies can help fact-checking agencies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Objectives: We provide a guide to conducting a crowdsourcing activity at an international sexually transmitted infection (STI) conference to design public messaging about STI testing and disseminating that messaging via social media., Methods: A speaker gave a presentation at a conference plenary session on the concepts of cocreation, crowdsourcing and designathons, and the application of these participatory approaches in public health research. To illustrate one of these approaches (crowdsourcing), attendees in the audience were asked to take part in a voluntary participatory activity, in which they would pair up with a fellow attendee sitting nearby and write down an idea on a blank notecard. Dyads were given 10 min to create an entry responding to the prompt, 'Write something that inspires gonorrhoea and/or chlamydia testing (eg, picture, jingle, rhyme)'. Each entry was judged by at least four independent judges on a scale of 0 (lowest quality) to 10 (highest quality) based on their innovation and potential to promote chlamydia/gonorrhoea testing. Scores were averaged to determine the finalist entries., Results: We received 32 entries. The average score was 6.41 and scores ranged from 4.5 to 8 (median 6.63, IQR 5.75, 7.06). Half of entries (n=16) were slogans, 15.6% (n=5) were poems/rhymes, 12.5% (n=4) were memes/images, 9.4% (n=3) were programme implementation ideas, 3.1% (n=1) was a song verse, and 3.1% (n=1) was a video idea. One finalist entry was a meme and received 720 impressions, 120 engagements, 27 detail expands, 19 likes, 6 reposts and 1 response on Twitter. The second finalist entry was a slogan and received 242 impressions, 16 engagements, 6 detail expands, 4 likes and 2 reposts., Conclusions: Conducting crowdsourcing activities at future conferences may be an innovative, feasible way to develop and disseminate engaging and important STI and other health messaging to the public in a short period of time., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Non-recurrent traffic congestion arising from traffic incidents is unpredictable but should be addressed efficiently to mitigate its adverse impacts on safety and travel time reliability. Numerous studies have been conducted about incident clearance time, while the recovery time, due to the limitations of data collection, is often inadvertently neglected in assessing incident-induced duration (i.e., the time from incident occurrence to the normal flow of traffic). Overlooking the recovery time is likely to underestimate the total incident-induced impact. Furthermore, the spatiotemporal heterogeneity of observed factors is not adequately captured in incident duration models. To address these gaps, this study specifically investigated traffic crashes as they reflect safety issues and are the primary cause of non-recurrent congestion. The emerging crowdsourced traffic reports were harnessed to estimate crash recovery time, which can complement the blind zone of fixed detectors. A geographically and temporally weighted proportional hazard (GWTPH) model was developed to untangle factors associated with the interval-censored crash duration. The results show that the GWTPH model outperforms the global model in goodness-of-fit. Many factors present a spatiotemporally heterogeneous effect. For example, the global model merely revealed that deploying dynamic message signs (DMS) shortened the crash time to normal. Notably, the GWTPH model highlights an average reduction of 32.8% with a standard deviation of 31% in time to normal. The study's findings and application of new spatiotemporal techniques are valuable for practitioners to localize strategies for incident management. For instance, deploying DMS can be very helpful in corridors when incidents happen, especially during peak hours., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

Several dimensional frameworks for characterizing heterogeneity in alcohol use disorder (AUD) have been proposed, including the Addictions Neuroclinical Assessment (ANA). The ANA is a framework for assessing individual variability within AUD across three domains corresponding to the proposed stages of the addiction cycle: reward (binge-intoxication stage), negative emotionality (withdrawal-negative affect stage), and cognitive control (preoccupation-anticipation stage). Recent work has evaluated the ANA's three-factor structure and construct validity, primarily in treatment-seekers with AUD. We extended this research by examining the factor structure, bias across alcohol use severity, longitudinal invariance, and concurrent and predictive validity of a novel assessment of the ANA domains in adults with past 12-month regular (10 + alcohol units/week) alcohol use. Participants recruited from Prolific ( N = 732), a crowdsourced data collection platform, completed various self-report measures. A test-retest subsample ( n = 234) completed these measures 30 days later. Split-half exploratory factor analysis and confirmatory factor analysis supported the three-factor structure of the ANA. The overall factor structure was invariant across 30 days. Concurrently and prospectively, ANA domains demonstrated convergent validity concerning theoretically aligned alcohol-related, psychological, and personality measures. However, there was evidence of poor discriminant validity, and several cognitive control and reward items demonstrated bias across alcohol use severity. Future research is needed to improve the measurement of ANA domains using multimodal indicators, examine longitudinal changes in domains and their relationship with alcohol use severity, characterize phenotypic subgroups based on relative levels of domains, and compare the utility of the ANA with other proposed frameworks for measuring AUD heterogeneity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Background: Emerging literature suggests that LGBTQ+ cancer survivors are more likely to experience financial burden than non-LGBTQ+ survivors. However, LGBTQ+ cancer survivors experience with cost-coping behaviors such as crowdfunding is understudied., Methods: We aimed to assess LGBTQ+ inequity in cancer crowdfunding by combining community-engaged and technology-based methods. Crowdfunding campaigns were web-scraped from GoFundMe and classified as cancer-related and LGBTQ+ or non-LGBTQ+ using term dictionaries. Bivariate analyses and generalized linear models were used to assess differential effects in total goal amount raised by LGBTQ+ status. Stratified models were run by online reach and LGBTQ+ inclusivity of state policy., Results: A total of N = 188,342 active cancer-related crowdfunding campaigns were web-scraped from GoFundMe in November 2022, of which N = 535 were LGBTQ+ and ranged from 2014 to 2022. In multivariable models of recent campaigns (2019-2022), LGBTQ+ campaigns raised $1608 (95% CI: -2139, -1077) less than non-LGBTQ+ campaigns. LGBTQ+ campaigns with low (26-45 donors), moderate (46-87 donors), and high (88-240 donors) online reach raised on average $1152 (95% CI: -$1589, -$716), $1050 (95% CI: -$1737, -$364), and $2655 (95% CI: -$4312, -$998) less than non-LGBTQ+ campaigns respectively. When stratified by LGBTQ+ inclusivity of state level policy states with anti-LGBTQ+ policy/lacking equitable policy raised on average $1910 (95% CI: -2640, -1182) less than non-LGBTQ+ campaigns from the same states., Conclusions and Relevance: Our findings revealed LGBTQ+ inequity in cancer-related crowdfunding, suggesting that LGBTQ+ cancer survivors may be less able to address financial burden via crowdfunding in comparison to non-LGBTQ+ cancer survivors-potentially widening existing economic inequities., (© 2024 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Background: Patient education materials are commonly reported to be difficult to understand., Objectives: We aimed to use crowdsourcing to improve patient education materials at our institution., Methods: This was a department-wide quality improvement project to increase organizational health literacy. There are 6 phases of this pilot study: (1) evaluating preexisting patient education materials, (2) evaluating online patient education materials at the society (the American Society of Plastic Surgeon) and government level (Medline Plus), (3) redesigning our patient education material and reevaluating the education material, (4) crowdsourcing to evaluate understandability of the new patient education material, (5) data analysis, and (6) incorporating crowdsourcing suggestions to the patient education material., Results: Breast-related patient education materials are not easy to read at the institution level, the society level, and the government level. Our new implant-based breast reconstruction patient education material is easy to read as demonstrated by the crowdsourcing evaluation. More than 90% of the participants reported our material is "very easy to understand" or "easy to understand." The crowdsourcing process took 1.5 days, with 700 workers responding to the survey. The total cost was $9. After incorporating participants' feedback into the finalized material, the readability of the material is at the recommended reading level. The material also had the recommended length (between 400 and 800 words)., Discussion: Our study demonstrated a pathway for clinicians to efficiently obtain a large amount of feedback to improve patient education materials. Crowdsourcing is an effective tool to improve organizational health literacy., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Crowdsourcing has been used in computational pathology to generate cell and cell nuclei annotations for machine learning. Herein, we broaden its scope to the previously unsolved challenging task of glioma cell detection. This requires multiplexed immunofluorescence microscopy due to diffuse invasiveness and exceptional similarity between glioma cells and reactive astrocytes. In four pilot experiments, we iteratively developed a task design enabling high-quality annotations by crowdworkers on Amazon Mechanical Turk. We applied majority or weighted vote and validated them against ground truth in the final setting. On the base of a YOLO convolutional neural network architecture, we used these consensus labels for training with different image representations regarding colors, intensities, and immmunohistochemical marker combinations. A crowd of 712 workers defined aggregated point annotations in 235 images with an average [Formula: see text] score of 0.627 for majority vote. The networks resulted in acceptable [Formula: see text] scores up to 0.69 for YOLOv8 on average and indicated first evidence for transferability to images lacking tumor markers, especially in IDH-wildtype glioblastoma. Our work confirms feasibility of crowdsourcing to generate labels suitable for training of machine learning tools in the challenging and clinically relevant use case of glioma microenvironment., (© 2024. The Author(s).)

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures., (© 2024. The Author(s).)

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth., Competing Interests: Declaration of interests S.V.L. is a board member at, holds stock in, and consults for Siolta Therapeutics. She also consults for the Atria Academy of Science and Medicine and for Sanofi. J.C.C. is co-founder of PrecisionProfile and OncoRx Insights. N.Aghaeepour. is a member of the scientific advisory boards of January AI, Parallel Bio, Celine Therapeutics, and WellSim Biomedical Technologies and is a paid consultant for Mara BioSystems. J.G. and M.S. have filed a patent related to the phylotype generation process., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

In this paper, we present a human-based computation approach for the analysis of peripheral blood smear (PBS) images images in patients with Sickle Cell Disease (SCD). We used the Mechanical Turk microtask market to crowdsource the labeling of PBS images. We then use the expert-tagged erythrocytesIDB dataset to assess the accuracy and reliability of our proposal. Our results showed that when a robust consensus is achieved among the Mechanical Turk workers, probability of error is very low, based on comparison with expert analysis. This suggests that our proposed approach can be used to annotate datasets of PBS images, which can then be used to train automated methods for the diagnosis of SCD. In future work, we plan to explore the potential integration of our findings with outcomes obtained through automated methodologies. This could lead to the development of more accurate and reliable methods for the diagnosis of SCD., (© 2024. The Author(s).)

Background: Approximately 30% of patients experience substantial improvement in depression after 2 months without treatment, and 45% with antidepressants. The smallest worthwhile difference (SWD) refers to an intervention's smallest beneficial effect over a comparison patients deem worthwhile given treatment burdens (harms, expenses and inconveniences), but is undetermined for antidepressants., Objective: Estimating the SWD of commonly prescribed antidepressants for depression compared to no treatment., Methods: The SWD was estimated as a patient-required difference in response rates between antidepressants and no treatment after 2 months. An online cross-sectional survey using Prolific, MQ Mental Health and Amazon Mechanical Turk crowdsourcing services in the UK and USA between October 2022 and January 2023 garnered participants (N=935) that were a mean age of 44.1 (SD=13.9) and 66% women (n=617)., Findings: Of 935 participants, 124 reported moderate-to-severe depressive symptoms but were not in treatment, 390 were in treatment and 495 reported absent-to-mild symptoms with or without treatment experiences. The median SWD was a 20% (IQR=10-30%) difference in response rates for people with moderate-to-severe depressive symptoms, not in treatment, and willing to consider antidepressants, and 25% (IQR=10-35%) for the full sample., Conclusions: Our observed SWDs mean that the current 15% antidepressant benefit over no treatment was sufficient for one in three people to accept antidepressants given the burdens, but two in three expected greater treatment benefits., Implications: While a minority may be satisfied with the best currently available antidepressants, more effective and/or less burdensome medications are needed, with more attention given to patient perspectives., Competing Interests: Competing interests: TAF reports personal fees from Boehringer-Ingelheim, DT Axis, Kyoto University Original, Shionogi, SONY and UpToDate, and a grant from Shionogi, outside the submitted work; In addition, TAF has patents 2020-548587 and 2022-082495 pending, and intellectual properties for Kokoro-app licensed to Mitsubishi-Tanabe. ACh has received research, educational and consultancy fees from INCiPiT (Italian Network for Paediatric Trials), CARIPLO Foundation, Lundbeck and Angelini Pharma. He is the CI/PI of two trials about seltorexant in depression, sponsored by Janssen. In the last 3 years SL has received honoraria as a consultant and/or advisor and/or for lectures and/or for educational material from Alkermes, Angelini, Eisai, Gedeon Richter, Janssen, Lundbeck, Medichem, Medscape, Merck Sharpp and Dome, Mitsubishi, Neurotorium, NovoNordisk, Otsuka, Recordati, Roche, Rovi, Sanofi Aventis, TEVA. All the other authors declared no conflict of interest., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. Published by BMJ.)

Introduction: Research mentorship is critical for advancing science, but there are few practical strategies for cultivating mentorship in health research resource-limited settings. WHO/TDR Global commissioned a group to develop a practical guide on research mentorship. This global qualitative evidence synthesis included data from a crowdsourcing open call and scoping review to identify and propose strategies to enhance research mentorship in low/middle-income country (LMIC) institutions., Methods: The crowdsourcing open call used methods recommended by WHO/TDR and solicited descriptions of strategies to enhance research mentorship in LMICs. The scoping review used the Cochrane Handbook and predefined the approach in a protocol. We extracted studies focused on enhancing health research mentorship in LMICs. Textual data describing research mentorship strategies from the open call and studies from the scoping review were coded into themes. The quality of evidence supporting themes was assessed using the Confidence in the Evidence from Reviews of Qualitative research approach., Results: The open call solicited 46 practical strategies and the scoping review identified 77 studies. We identified the following strategies to enhance research mentorship: recognising mentorship as an institutional responsibility that should be provided and expected from all team members (8 strategies, 15 studies; moderate confidence); leveraging existing research and training resources to enhance research mentorship (15 strategies, 49 studies; moderate confidence); digital tools to match mentors and mentees and sustain mentorship relations over time (14 strategies, 11 studies; low confidence); nurturing a culture of generosity so that people who receive mentorship then become mentors to others (7 strategies, 7 studies; low confidence); peer mentorship defined as informal and formal support from one researcher to another who is at a similar career stage (16 strategies, 12 studies; low confidence)., Interpretation: Research mentorship is a collective institutional responsibility, and it can be strengthened in resource-limited institutions by leveraging already existing resources. The evidence from the crowdsourcing open call and scoping review informed a WHO/TDR practical guide. There is a need for more formal research mentorship programmes in LMIC institutions., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Patients consider many factors when selecting a plastic surgeon. Previous studies have demonstrated the importance of board certification and reputation in this decision. Despite this, there is a paucity of knowledge on the role that cost of procedure, social media, and surgeon training have on the decision-making process., Methods: The authors' study used a population-based survey administered by Amazon Mechanical Turk. Adults 18 years and older and residing in the United States were asked to rank the importance of 36 factors from 0 (least important) to 10 (most important) when selecting a plastic surgeon., Results: A total of 369 responses were analyzed. The mean age of respondents was 36.9 years (SD, 10.9 years), and 174 participants (47.2%) were female. Of those surveyed, 216 (55.0%) had previously undergone plastic surgery, and all respondents were considering plastic surgery either at the time of survey or in the future. The most common first step for respondents in identifying a plastic surgeon was a web-based search (32.2%). The top three most important factors in selecting a plastic surgeon were surgeon's experience with the desired procedure (7.48), surgeon's board certification (7.38), and surgeon's years in practice (7.36). The three least important factors were the surgeon's race (5.43), number of social media posts (5.62), and television appearances (5.64)., Conclusions: The authors' survey provides insight into the role that different elements play in the decision of selecting a plastic surgeon in the United States. Understanding how patients select a plastic surgeon can help surgeons optimize these elements in their practices., (Copyright © 2023 by the American Society of Plastic Surgeons.)

Background: Seasonal respiratory viruses had lower incidence during their 2019-2020 and 2020-2021 seasons, which overlapped with the COVID-19 pandemic. The widespread implementation of precautionary measures to prevent transmission of SARS-CoV-2 has been seen to also mitigate transmission of seasonal influenza. The COVID-19 pandemic also led to changes in care seeking and access. Participatory surveillance systems have historically captured mild illnesses that are often missed by surveillance systems that rely on encounters with a health care provider for detection., Objective: This study aimed to assess if a crowdsourced syndromic surveillance system capable of detecting mild influenza-like illness (ILI) also captured the globally observed decrease in ILI in the 2019-2020 and 2020-2021 influenza seasons, concurrent with the COVID-19 pandemic., Methods: Flu Near You (FNY) is a web-based participatory syndromic surveillance system that allows participants in the United States to report their health information using a brief weekly survey. Reminder emails are sent to registered FNY participants to report on their symptoms and the symptoms of household members. Guest participants may also report. ILI was defined as fever and sore throat or fever and cough. ILI rates were determined as the number of ILI reports over the total number of reports and assessed for the 2016-2017, 2017-2018, 2018-2019, 2019-2020, and 2020-2021 influenza seasons. Baseline season (2016-2017, 2017-2018, and 2018-2019) rates were compared to the 2019-2020 and 2020-2021 influenza seasons. Self-reported influenza diagnosis and vaccination status were captured and assessed as the total number of reported events over the total number of reports submitted. CIs for all proportions were calculated via a 1-sample test of proportions., Results: ILI was detected in 3.8% (32,239/848,878) of participants in the baseline seasons (2016-2019), 2.58% (7418/287,909) in the 2019-2020 season, and 0.27% (546/201,079) in the 2020-2021 season. Both influenza seasons that overlapped with the COVID-19 pandemic had lower ILI rates than the baseline seasons. ILI decline was observed during the months with widespread implementation of COVID-19 precautions, starting in February 2020. Self-reported influenza diagnoses decreased from early 2020 through the influenza season. Self-reported influenza positivity among ILI cases varied over the observed time period. Self-reported influenza vaccination rates in FNY were high across all observed seasons., Conclusions: A decrease in ILI was detected in the crowdsourced FNY surveillance system during the 2019-2020 and 2020-2021 influenza seasons, mirroring trends observed in other influenza surveillance systems. Specifically, the months within seasons that overlapped with widespread pandemic precautions showed decreases in ILI and confirmed influenza. Concerns persist regarding respiratory pathogens re-emerging with changes to COVID-19 guidelines. Traditional surveillance is subject to changes in health care behaviors. Systems like FNY are uniquely situated to detect disease across disease severity and care seeking, providing key insights during public health emergencies., (©Autumn Gertz, Benjamin Rader, Kara Sewalk, Tanner J Varrelman, Mark Smolinski, John S Brownstein. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 28.12.2023.)

Objectives: Segmentation is crucial in medical imaging. Deep learning based on convolutional neural networks showed promising results. However, the absence of large-scale datasets and a high degree of inter- and intra-observer variations pose a bottleneck. Crowdsourcing might be an alternative, as many non-experts provide references. We aim to compare different types of crowdsourcing for medical image segmentation., Methods: We develop a crowdsourcing platform that integrates citizen science (incentive: participating in the research), paid microtask (incentive: financial reward), and gamification (incentive: entertainment). For evaluation, we choose the use case of sclera segmentation in fundus images as a proof-of-concept and analyze the accuracy of crowdsourced masks and the generalization of learning models trained with crowdsourced masks., Results: The developed platform is suited for the different types of crowdsourcing and offers an easy and intuitive way to implement crowdsourcing studies. Regarding the proof-of-concept study, citizen science, paid microtask, and gamification yield a median F-score of 82.2, 69.4, and 69.3 % compared to expert-labeled ground truth, respectively. Generating consensus masks improves the gamification masks (78.3 %). Despite the small training data (50 images), deep learning reaches median F-scores of 80.0, 73.5, and 76.5 % for citizen science, paid microtask, and gamification, respectively, indicating sufficient generalizability., Conclusions: As the platform has proven useful, we aim to make it available as open-source software for other researchers., (© 2023 the author(s), published by De Gruyter, Berlin/Boston.)

Objective: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question., Materials and Methods: Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system., Results: The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort., Discussion: Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data., Conclusion: This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Biodiversity is crucial for human health, but previous methods of measuring biodiversity require intensive resources and have other limitations. Crowdsourced datasets from citizen scientists offer a cost-effective solution for characterizing biodiversity on a large spatial scale. This study has two aims: 1) to generate fine-resolution plant species diversity maps in California urban areas using crowdsourced data and extrapolation methods; and 2) to examine their associations with sociodemographic factors and identify subpopulations with low biodiversity exposure. We used iNaturalist observations from 2019 to 2022 to calculate species diversity metrics by exploring the sampling completeness in a 5 × 5-km 2 grid and then computing species diversity metrics for grid cells with at least 80 % sample completeness (841 out of 4755 grid cells). A generalized additive model with ordinary kriging (GAM OK) provided moderately reliable estimates, with correlations of 0.64-0.66 between observed and extrapolated metrics, relative mean absolute errors of 21 %-23 %, and relative root mean squared errors of 27 %-30 % for grid cells with ≥80 % sample completeness from 10-fold cross-validation. GAM OK was further applied to extrapolate species diversity metrics from saturated grid cells (N = 841) to the remaining grid cells with <80 % sample completeness (N = 3914) and generate diversity maps that cover the grid. Further, generalized linear mixed models were used to examine the associations between species diversity and sociodemographic indicators at census tract level. The wild vascular plant species diversity metrics were inversely associated with neighborhood socioeconomic status (i.e., unemployment, linguistic isolation, educational attainment, and poverty rate). Minority populations (i.e., African American, Asian American, and Hispanic) and children had significantly lower diversity exposure in their neighborhoods. Crowdsourcing data offers a cost-effective solution for characterizing large-scale biodiversity in urban areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)