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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: The consultation process, where a clinician seeks an opinion from another clinician, is foundational in medicine. However, the effectiveness of group diagnosis has not been studied., Objective: To compare individual diagnosis to group diagnosis on two dimensions: group size (n=3 or 6) and group process (interactive or artificial groups)., Methodology: Thirty-six internal or emergency medicine residents participated in the study. Initially, each resident worked through four written cases on their own, providing a primary diagnosis and a differential diagnosis. Next, participants formed into groups of three. Using a videoconferencing platform, they worked through four additional cases, collectively providing a single primary diagnosis and differential diagnosis. The process was repeated using a group of six with four new cases. Cases were all counterbalanced. Retrospectively, nominal (ie, artificial) groups were formed by aggregating individual participant data into subgroups of three and six and analytically computing scores. Presence of the correct diagnosis as primary diagnosis or included in the differential diagnosis, as well as the number of diagnoses mentioned, was calculated for all conditions. Means were compared using analysis of variance., Results: For both authentic and nominal groups, the diagnostic accuracy of group diagnosis was superior to individual for both the primary diagnosis and differential diagnosis. However, there was no improvement in diagnostic accuracy when comparing a group of three to a group of six. Interactive and nominal groups were equivalent; however, this may be an artefact of the method used to combine data., Conclusions: Group diagnosis improves diagnostic accuracy. However, a larger group is not necessarily superior to a smaller group. In this study, interactive group discussion does not result in improved diagnostic accuracy., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

HIV incidence among young people (Black and Latinx women and men who have sex with men ages 16-24 years), in the United States is high. Traditional top-down approaches for pre-exposure prophylaxis (PrEP) social marketing are not effectively reaching this population. Crowdsourcing is a promising approach to engaging young people in the development of innovative solutions to raise awareness and use of PrEP among those at highest risk of HIV. This study engaged young people in the design and evaluation of an online crowdsourcing contest to promote PrEP among Washington, DC youth. The contest used standard methods recommended by the World Health Organization and feedback from our community partners. Online recruitment using social media elicited online votes and survey responses. We analyzed cross-sectional surveys using descriptive statistics, and semi-structured interviews with contest participants using thematic coding to explore barriers and facilitators to contest engagement. Approximately 82% of entries were from young people in DC. A convenience sample of 181 people voted on their favorite crowdsourced PrEP messages and shared their awareness and attitudes about PrEP. The contest website received 2,500 unique visitors and 4,600 page views. Themes from semi-structured interviews (n = 16) included the need for more community engagement in developing PrEP messaging and positive attitudes towards crowdsourcing. Survey data (n = 887) showed that the crowdsourced messages were well-liked and resonated with the community. Most preferred to see PrEP messages in social media (23%), email (17%) and videos (14%). Approximately 70% of survey participants reported that after viewing the crowdsourced message they would talk to their sexual partner or medical provider (63%) about PrEP, use PrEP (58%), and learn more about PrEP (56%). Crowdsourced messages solicit substantial online viewership. More implementation research is needed to understand the public health impact of integrating social media, crowdsourcing, and community engagement to develop PrEP promotional messages., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Taggart 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.)

Background: As crowdfunding sites proliferate, visual content often serves as the initial bridge connecting a project to its potential backers, underscoring the importance of image selection in effectively engaging an audience., Objective: This paper aims to explore the relationship between images and crowdfunding success in cancer-related crowdfunding projects., Methods: We used the Alibaba Cloud platform to detect individual features in images. In addition, we used the Recognize Anything Model to label images and obtain content tags. Furthermore, the discourse atomic topic model was used to generate image topics. After obtaining the image features and image content topics, we built regression models to investigate the factors that influence the results of crowdfunding success., Results: Images with a higher proportion of young people (β=0.0753; P<.001), a larger number of people (β=0.00822; P<.001), and a larger proportion of smiling faces (β=0.0446; P<.001) had a higher success rate. Image content related to good things and patient health also contributed to crowdfunding success (β=0.082, P<.001; and β=0.036, P<.001, respectively). In addition, the interaction between image topics and image characteristics had a significant effect on the final fundraising outcome. For example, when smiling faces are considered in conjunction with the image topics, using more smiling faces in the rest and play theme increased the amount of money raised (β=0.0152; P<.001). We also examined causality through a counterfactual analysis, which confirmed the influence of the variables on crowdfunding success, consistent with the results of our regression models., Conclusions: In the realm of web-based medical crowdfunding, the importance of uploaded images cannot be overstated. Image characteristics, including the number of people depicted and the presence of youth, significantly improve fundraising results. In addition, the thematic choice of images in cancer crowdfunding efforts has a profound impact. Images that evoke beauty and resonate with health issues are more likely to result in increased donations. However, it is critical to recognize that reinforcing character traits in images of different themes has different effects on the success of crowdfunding campaigns., (©Renwu Wang, Huimin Xu, Xupin Zhang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 15.11.2024.)

Crowdsourcing strategies are useful in the development of public health interventions. Crowdsourcing engages end users in a co-creation process through challenge contests, designathons or online collaborations. Drawing on our experience of crowdsourcing in four African countries, we provide guidance on designing crowdsourcing strategies across seven steps: deciding on the type of crowdsourcing strategy, convening a steering committee, developing the content of the call for ideas, promotion, evaluation, recognizing finalists and sharing back ideas or implementing the solutions., (© The Author(s) 2024. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine.)

Human forecasting accuracy improves through the "wisdom of the crowd" effect, in which aggregated predictions tend to outperform individual ones. Past research suggests that individual large language models (LLMs) tend to underperform compared to human crowd aggregates. We simulate a wisdom of the crowd effect with LLMs. Specifically, we use an ensemble of 12 LLMs to make probabilistic predictions about 31 binary questions, comparing them with those made by 925 human forecasters in a 3-month tournament. We show that the LLM crowd outperforms a no-information benchmark and is statistically indistinguishable from the human crowd. We also observe human-like biases, such as the acquiescence bias. In another study, we find that LLM predictions (of GPT-4 and Claude 2) improve when exposed to the median human prediction, increasing accuracy by 17 to 28%. However, simply averaging human and machine forecasts yields more accurate results. Our findings suggest that LLM predictions can rival the human crowd's forecasting accuracy through simple aggregation.

Importance: Health datasets from clinical sources do not reflect the breadth and diversity of disease, impacting research, medical education, and artificial intelligence tool development. Assessments of novel crowdsourcing methods to create health datasets are needed., Objective: To evaluate if web search advertisements (ads) are effective at creating a diverse and representative dermatology image dataset., Design, Setting, and Participants: This prospective observational survey study, conducted from March to November 2023, used Google Search ads to invite internet users in the US to contribute images of dermatology conditions with demographic and symptom information to the Skin Condition Image Network (SCIN) open access dataset. Ads were displayed against dermatology-related search queries on mobile devices, inviting contributions from adults after a digital informed consent process. Contributions were filtered for image safety and measures were taken to protect privacy. Data analysis occurred January to February 2024., Exposure: Dermatologist condition labels as well as estimated Fitzpatrick Skin Type (eFST) and estimated Monk Skin Tone (eMST) labels., Main Outcomes and Measures: The primary metrics of interest were the number, quality, demographic diversity, and distribution of clinical conditions in the crowdsourced contributions. Spearman rank order correlation was used for all correlation analyses, and the χ2 test was used to analyze differences between SCIN contributor demographics and the US census., Results: In total, 5749 submissions were received, with a median of 22 (14-30) per day. Of these, 5631 (97.9%) were genuine images of dermatological conditions. Among contributors with self-reported demographic information, female contributors (1732 of 2596 contributors [66.7%]) and younger contributors (1329 of 2556 contributors [52.0%] aged <40 years) had a higher representation in the dataset compared with the US population. Of 2614 contributors who reported race and ethnicity, 852 (32.6%) reported a racial or ethnic identity other than White. Dermatologist confidence in assigning a differential diagnosis increased with the number of self-reported demographic and skin-condition-related variables (Spearman R = 0.1537; P < .001). Of 4019 contributions reporting duration since onset, 2170 (54.0%) reported onset within less than 7 days of submission. Of the 2835 contributions that could be assigned a dermatological differential diagnosis, 2523 (89.0%) were allergic, infectious, or inflammatory conditions. eFST and eMST distributions reflected the geographical origin of the dataset., Conclusions and Relevance: The findings of this survey study suggest that search ads are effective at crowdsourcing dermatology images and could therefore be a useful method to create health datasets. The SCIN dataset bridges important gaps in the availability of images of common, short-duration skin conditions.

This paper focuses on the online anti-epidemic resource allocation between demanders and suppliers considering the epidemic spatial spread. The spatial crowdsourcing with sharing platform is an effective way for anti-epidemic resource allocation, and a reasonable online matching strategy can improve the efficiency of resource utilization. The paper proposes online matching heuristic strategy (HSTRF-LRLUF strategy) and designs an online batch bilateral matching algorithm for anti-epidemic resources, which considers the impact of grid spatial aggregation and diffusion risk of emerging infectious diseases. The population distribution within grids and the commuting patterns between grids can provide decision support for selecting online matching strategies of anti-epidemic resources. A larger matching time window focusing on the spatial transmission risk (TR) of the epidemic can obtain better matching results. However, with a smaller matching time window, the decision makers can focus on spatial agglomeration risk (OR) or spatial diffusion risk (IR). The paper effectively combines the spatial crowdsourcing model with the anti-epidemic resource allocation to achieve the allocation of emergency resources to individuals. A combined anti-epidemic resource online matching heuristic strategies is designed from the spatial agglomeration risk and the spatial diffusion risk. Decision makers can dynamically adjust the online matching strategies of anti-epidemic resources by evaluating the spatial agglomeration risk, the spatial diffusion risk, and the overall spatial transmission risk based on the real-time spread of the epidemic and the supply of anti-epidemic resources., (© 2024. The Author(s).)

This study aims to explore adoption barriers from three aspects critical to facilitating robotic-assisted-surgery (RAS) adoption: the human-robot-interaction, built-environment, and training. Guidelines for research and design are identified from these perspectives. This study consisted of three phases: 1) surveys to RAS stakeholders and a crowd-sourcing survey; 2) stakeholder focus groups; and 3) a workshop with subject-matter experts to prioritize future research for RAS. Barriers from the human-robot-interaction perspective include hesitancy among clinicians to trust automation and physical discomfort during prolonged RAS. Barriers impeding communication and workflow disruptions were identified considering the built-environment. Training barriers were exemplified by varying curriculums from formal (courses) to informal (peer training) for surgeons. Crowd-sourced survey results suggest socio-economic status plays a role in RAS perception. Design guidelines include: 1) improved ergonomics; 2) deliberate introduction of automation; 3) sufficient in-room storage for prospective operating rooms; and 4) the development of compact RAS devices for older/smaller rooms., 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.)

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).)

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.

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.)

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.)

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.)

Financial considerations continue to impact access to heart transplantation. Transplant recipients face various costs, including, but not limited to, the index hospitalization, immunosuppressive medications, and lodging and travel to appointments. In this study, we sought to describe the state of crowdfunding for individuals being evaluated for heart transplantation. Using the search term heart transplant, 1000 GoFundMe campaigns were reviewed. After exclusions, 634 (63.4%) campaigns were included. Most campaigns were in support of white individuals (57.8%), males (63.1%) and adults (76.7%). Approximately 15% of campaigns had not raised any funds. The remaining campaigns fundraised a median of $53.24 dollars per day. Of the patients, 44% were admitted at the time of the fundraising. Within the campaigns in the United States, the greatest proportions were in the Southeast United States in non-Medicaid expansion states. These findings highlight the significant financial toxicities associated with heart transplantation and the need for advocacy at the governmental and payer levels to improve equitable access and coverage for all., Competing Interests: Disclosures EMD and JL are on the Board of Directors for The Equity in Heart Transplant Project. NI is the founder and executive director of The Equity in Heart Transplant Project. All remaining authors have no conflicts of interest to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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.)

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.)

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).

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.)

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).)

The application of the General Data Protection Regulation (GDPR) 2016/679/EC, the Regulation for the protection of personal data, is a challenge and must be seen as an opportunity for the redesign of the systems that are being used for the processing of personal data. An unexplored area where systems are being used to collect and process personal data are the e-Participation environment. The latest generations of such environments refer to sociotechnical systems based on the exploitation of the increasing use of Social Media, by using them as valuable tools, able to provide answers and decision support in public policy formulation. This work explores the privacy requirements that GDPR imposes in such environments, contributing to the identification of challenges that e-Participation approaches have to deal with, with regard to privacy protection.

Maintaining data quality on Amazon Mechanical Turk (MTurk) has always been a concern for researchers. These concerns have grown recently due to the bot crisis of 2018 and observations that past safeguards of data quality (e.g., approval ratings of 95%) no longer work. To address data quality concerns, CloudResearch, a third-party website that interfaces with MTurk, has assessed ~165,000 MTurkers and categorized them into those that provide high- (~100,000, Approved) and low- (~65,000, Blocked) quality data. Here, we examined the predictive validity of CloudResearch's vetting. In a pre-registered study, participants (N = 900) from the Approved and Blocked groups, along with a Standard MTurk sample (95% HIT acceptance ratio, 100+ completed HITs), completed an array of data-quality measures. Across several indices, Approved participants (i) identified the content of images more accurately, (ii) answered more reading comprehension questions correctly, (iii) responded to reversed coded items more consistently, (iv) passed a greater number of attention checks, (v) self-reported less cheating and actually left the survey window less often on easily Googleable questions, (vi) replicated classic psychology experimental effects more reliably, and (vii) answered AI-stumping questions more accurately than Blocked participants, who performed at chance on multiple outcomes. Data quality of the Standard sample was generally in between the Approved and Blocked groups. We discuss how MTurk's Approval Rating system is no longer an effective data-quality control, and we discuss the advantages afforded by using the Approved group for scientific studies on MTurk., (© 2022. The Author(s).)

Background: Two crowdsourcing open calls were created to enhance community engagement in dengue control in Sri Lanka. We analyzed the process and outcomes of these digital crowdsourcing open calls., Methods: We used standard World Health Organization methods to organize the open calls, which used exclusively digital methods because of coronavirus disease 2019 (COVID-19). We collected and analyzed sociodemographic information and digital engagement metrics from each submission. Submissions in the form of textual data describing community-led strategies for mosquito release were coded using grounded theory., Results: The open calls received 73 submissions. Most people who submitted ideas spoke English, lived in Sri Lanka, and were 18 to 34 years old. The total Facebook reach was initially limited (16 161 impressions), prompting expansion to a global campaign, which reached 346 810 impressions over 14 days. Diverse strategies for the distribution of Wolbachia-infected mosquito boxes were identified, including leveraging traditional festivals, schools, and community networks. Fifteen submissions (21%) suggested the use of digital tools for monitoring and evaluation, sharing instructions, or creating networks. Thirteen submissions (18%) focused on social and economic incentives to prompt community engagement and catalyze community-led distribution., Conclusions: Our project demonstrates that digital crowdsourcing open calls are an effective way to solicit creative and innovative ideas in a resource-limited setting., Competing Interests: Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Introduction: Amazon's Mechanical Turk (MTurk) appears to be a reliable resource for studying clinical populations and accessing hard-to-reach populations. Recent research suggests that MTurk may also be a viable option for military recruitment., Objective: The goal of the current study was to examine the utility of collecting clinical data on military samples recruited via MTurk., Method: Participants were 535 military veterans ( M age = 37.45; 71.8% men; 69.5% White) who completed measures assessing trauma and mental health., Results: Findings indicate that rates of military traumas and mental health diagnoses were higher than published comparisons; posttraumatic stress disorder (PTSD) and depression symptoms were found to be higher than values found in a nationally representative sample, lower than a treatment-seeking sample, and comparable to a MTurk-recruited military sample. Alcohol misuse was found to be higher than both nationally representative and treatment-seeking samples. Psychometric analyses indicated support for convergent validity of measures, and confirmatory factor analysis results demonstrated that empirically supported factor models of PTSD were replicated in the current sample; the hybrid model demonstrated the best fit., Conclusions: Our findings support the utility of MTurk for collecting clinical data on military samples. Increasing access to and recruitment of military samples is important for advancing the field of military psychology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Background: Despite great efforts in HIV prevention worldwide, HIV testing uptake among men who have sex with men (MSM) remains suboptimal. The effectiveness of digital, crowdsourced, multilevel interventions in improving HIV testing is still unclear., Objective: The aim of this study was to evaluate the effect of a digital, crowdsourced, multilevel intervention in improving HIV testing uptake among MSM in China., Methods: We conducted a 2-arm cluster randomized controlled trial among MSM in 11 cities in Shandong province, China, from August 2019 to April 2020. Participants were men who were HIV seronegative or had unknown serum status, had anal sex with a man in the past 12 months, and had not been tested for HIV in the past 3 months. Participants were recruited through a gay dating app and community-based organizations from preselected cities; these cities were matched into 5 blocks (2 clusters per block) and further randomly assigned (1:1) to receive a digital, crowdsourced, multilevel intervention (intervention arm) or routine intervention (control arm). The digital multilevel intervention was developed through crowdsourced open calls tailored for MSM, consisting of digital intervention images and videos, the strategy of providing HIV self-testing services through digital tools, and peer-moderated discussion within WeChat groups. The primary outcome was self-reported HIV testing uptake in the previous 3 months. An intention-to-treat approach was used to examine the cluster-level effect of the intervention in the 12-month study period using generalized linear mixed models and the individual-level effect using linear mixed models., Results: A total of 935 MSM were enrolled (404 intervention participants and 531 controls); 751 participants (80.3%) completed at least one follow-up survey. Most participants were younger than 30 years (n=601, 64.3%), single (n=681, 72.8%), had a college degree or higher (n=629, 67.3%), and had an HIV testing history (n=785, 84%). Overall, the proportion of testing for HIV in the past 3 months at the 3-, 6-, 9-, and 12-month follow-ups was higher in the intervention arm (139/279, 49.8%; 148/266, 55.6%; 189/263, 71.9%; and 171/266, 64.3%, respectively) than the control arm (183/418, 43.8%; 178/408, 43.6%; 206/403, 51.1%; and 182/397, 48.4%, respectively), with statistically significant differences at the 6-, 9-, and 12-month follow-ups. At the cluster level, the proportion of participants who had tested for HIV increased 11.62% (95% CI 0.74%-22.5%; P=.04) with the intervention. At the individual level, participants in the intervention arm had 69% higher odds for testing for HIV in the past 3 months compared with control participants, but the result was not statistically significant (risk ratio 1.69, 95% CI 0.87-3.27; P=.11)., Conclusions: The intervention effectively improved HIV testing uptake among Chinese MSM. Our findings highlight that digital, crowdsourced, multilevel interventions should be made more widely available for HIV prevention and other public health issues., Trial Registration: Chinese Clinical Trial Registry ChiCTR1900024350; http://www.chictr.org.cn/showproj.aspx?proj=36718., International Registered Report Identifier (irrid): RR2-10.1186/s13063-020-04860-8., (©Yuxi Lin, Ci Ren, Meizhen Liao, Dianmin Kang, Chuanxi Li, Kedi Jiao, Lin Wang, Yu Yan, Yijun Li, Taoyu Wu, Chunxiao Cheng, Zhe Zhao, Zece Xu, Weiming Tang, Joseph D Tucker, Wei Ma. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.10.2023.)

Introduction: Crowdsourcing is an emerging technique to engage or access a wider set of experts and multiple stakeholders through online platforms, which might effectively be employed in waste management. Therefore, we assessed the feasibility of the crowdsourcing method to provide an alternative approach that can improve household waste segregation using an "online-slogan-contest"., Methods: The contest was promoted via targeted emails to various governmental and non-governmental organizations and through social media platforms for around 4 weeks (25 days). The entries were received through a Google form. The slogans were assessed by the experts and analyzed using content analysis methods., Results: Total 969 entries were received from different geographic regions in India. Of that, 456 were in English and 513 in Hindi. Five themes of waste segregation emerged from the received slogans: (1) Community awareness, responsibility, and support, (2) Significance of household waste segregation, (3) Use of separate dustbins, (4) Health and well-being, and (5) Environment and sustainability., Discussion: Crowdsourcing approaches can be used by local authorities for improving waste management approaches and are recommended as these involve a wider audience within a short time frame. Moreover, this approach is flexible and integrating crowdsourcing approaches strengthens our understanding of existing waste management activities., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Krishnan, Sahoo, Kalyanasundaram, Singh, Srinivas, Pathak, Stålsby Lundborg, Atkins, Rousta and Diwan.)

Crowdsourced online food images, when combined with food image recognition technologies, have the potential to offer a cost-effective and scalable solution for the assessment of the restaurant nutrition environment. While previous research has explored this approach and validated the accuracy of food image recognition technologies, much remains unknown about the validity of crowdsourced food images as the primary data source for large-scale assessments. In this paper, we collect data from multiple sources and comprehensively examine the validity of using crowdsourced food images for assessing the restaurant nutrition environment in the Greater Hartford region. Our results indicate that while crowdsourced food images are useful in terms of the initial assessment of restaurant nutrition quality and the identification of popular food items, they are subject to selection bias on multiple levels and do not fully represent the restaurant nutrition quality or customers' dietary behaviors. If employed, the food image data must be supplemented with alternative data sources, such as field surveys, store audits, and commercial data, to offer a more representative assessment of the restaurant nutrition environment.

As a response to the shortcomings of the U.S. healthcare system, Americans are increasingly turning to crowdfunding platforms to bankroll their health-related costs. However, although medical crowdfunding has rapidly become institutionalized as part of the U.S. healthcare financing landscape, empirical evidence on how Americans perceive its role in healthcare and the impact it might have on public attitudes is scarce. To shed more light on the above, we analyze data from one correlational and one experimental study conducted over September-November 2021. Our correlational study reveals that political orientation is associated with Americans' views on medical crowdfunding. Specifically, we find that those who self-identified as conservative perceived medical crowdfunding as a valid part of the system, and more positively than a universal healthcare system. In contrast, medical crowdfunding is perceived less positively, as hindering a system of universal and affordable healthcare by those more liberally-oriented. In our experimental study, we explore how medical crowdfunding narratives can induce social attitudes conducive to change. Specifically, we test the effect of politicized narratives (vs. control) on group efficacy and subsequently on collective action intentions for healthcare reform, as a function of political orientation. Our results show that politicized narratives might induce collective action intentions through higher group efficacy, but only among those who self-identified as conservative. Liberally-oriented individuals held high collective action intentions for healthcare reform and were not affected by the manipulation. Our work is the first to establish empirically that medical crowdfunding, when employing politicized narratives, can induce collective action intentions, but this effect is moderated by political ideology., Competing Interests: Declarations of 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 Ltd.. All rights reserved.)

Academics are increasingly turning to crowdsourcing platforms to recruit research participants. Their endeavors have benefited from a proliferation of studies attesting to the quality of crowdsourced data or offering guidance on managing specific challenges associated with doing crowdsourced research. Thus far, however, relatively little is known about what it is like to be a participant in crowdsourced research. Our analysis of almost 1400 free-text responses provides insight into the frustrations encountered by workers on one widely used crowdsourcing site: Amazon's MTurk. Some of these frustrations stem from inherent limitations of the MTurk platform and cannot easily be addressed by researchers. Many others, however, concern factors that are directly controllable by researchers and that may also be relevant for researchers using other crowdsourcing platforms such as Prolific or CrowdFlower. Based on participants' accounts of their experiences as crowdsource workers, we offer recommendations researchers might consider as they seek to design online studies that demonstrate consideration for respondents and respect for their time, effort, and dignity., (© 2022. The Author(s).)

Background: Engaging patients in health behaviors is critical for better outcomes, yet many patient partnership behaviors are not widely adopted. Behavioral economics-based interventions offer potential solutions, but it is challenging to assess the time and cost needed for different options. Crowdsourcing platforms can efficiently and rapidly assess the efficacy of such interventions, but it is unclear if web-based participants respond to simulated incentives in the same way as they would to actual incentives., Objective: The goals of this study were (1) to assess the feasibility of using crowdsourced surveys to evaluate behavioral economics interventions for patient partnerships by examining whether web-based participants responded to simulated incentives in the same way they would have responded to actual incentives, and (2) to assess the impact of 2 behavioral economics-based intervention designs, psychological rewards and loss of framing, on simulated medication reconciliation behaviors in a simulated primary care setting., Methods: We conducted a randomized controlled trial using a between-subject design on a crowdsourcing platform (Amazon Mechanical Turk) to evaluate the effectiveness of behavioral interventions designed to improve medication adherence in primary care visits. The study included a control group that represented the participants' baseline behavior and 3 simulated interventions, namely monetary compensation, a status effect as a psychological reward, and a loss frame as a modification of the status effect. Participants' willingness to bring medicines to a primary care visit was measured on a 5-point Likert scale. A reverse-coding question was included to ensure response intentionality., Results: A total of 569 study participants were recruited. There were 132 in the baseline group, 187 in the monetary compensation group, 149 in the psychological reward group, and 101 in the loss frame group. All 3 nudge interventions increased participants' willingness to bring medicines significantly when compared to the baseline scenario. The monetary compensation intervention caused an increase of 17.51% (P<.001), psychological rewards on status increased willingness by 11.85% (P<.001), and a loss frame on psychological rewards increased willingness by 24.35% (P<.001). Responses to the reverse-coding question were consistent with the willingness questions., Conclusions: In primary care, bringing medications to office visits is a frequently advocated patient partnership behavior that is nonetheless not widely adopted. Crowdsourcing platforms such as Amazon Mechanical Turk support efforts to efficiently and rapidly reach large groups of individuals to assess the efficacy of behavioral interventions. We found that crowdsourced survey-based experiments with simulated incentives can produce valid simulated behavioral responses. The use of psychological status design, particularly with a loss framing approach, can effectively enhance patient engagement in primary care. These results support the use of crowdsourcing platforms to augment and complement traditional approaches to learning about behavioral economics for patient engagement., (©Kay-Yut Chen, Yan Lang, Yuan Zhou, Ludmila Kosmari, Kathryn Daniel, Ayse Gurses, Yan Xiao. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.07.2023.)

Crowdsourcing is a term that describes a method of distributed effort by which a goal is accomplished by a large group of people, typically initiated by a public call-to-action and is a portmanteau of the words "crowd" and "outsourcing" coined in 2005. Crowdfunding is the practice of funding a project or venture via crowdsourced financial contributions. Crowdsourcing and crowdfunding have been used to advance scientific progress for hundreds of years, with the frequency and scale of their use rapidly increasing during the twenty-first century, thanks to the advent of the internet and the mainstream adoption of related connective technologies. The field of aging research has uniquely benefited from this trend, with the emergence of multiple communities and platforms designed to coordinate gerontology funding and research collaboration. Furthermore, the recent widespread adoption of blockchain technology is also creating powerful new opportunities to crowdfund and crowdsource aging research specifically., (Copyright © 2023 Cold Spring Harbor Laboratory Press; all rights reserved.)

Symptom measurement in psychiatric research increasingly uses digitized self-report inventories and is turning to crowdsourcing platforms for recruitment, e.g., Amazon Mechanical Turk (mTurk). The impact of digitizing pencil-and-paper inventories on the psychometric properties is underexplored in mental health research. Against this background, numerous studies report high prevalence estimates of psychiatric symptoms in mTurk samples. Here we develop a framework to evaluate the online implementation of psychiatric symptom inventories relative to two domains, that is, the adherence to (i) validated scoring and (ii) standardized administration. We apply this new framework to the online use of the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Alcohol Use Disorder Identification Test (AUDIT). Our systematic review of the literature identified 36 implementations of these three inventories on mTurk across 27 publications. We also evaluated methodological approaches to enhance data quality, e.g., the use of bot detection and attention check items. Of the 36 implementations, 23 reported the applied diagnostic scoring criteria and only 18 reported the specified symptom timeframe. None of the 36 implementations reported adaptations made in their digitization of the inventories. While recent reports attribute higher rates of mood, anxiety, and alcohol use disorders on mTurk to data quality, our findings indicate that this inflation may also relate to the assessment methods. We provide recommendations to enhance both data quality and fidelity to validated administration and scoring methods., Competing Interests: Declaration of competing interest None, (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In behavioral economics, income shock manipulations (hypothetical narratives where money is gained or lost) can provide meaningful insight into decision-making related to primary and secondary commodities. To date, few studies have examined the impact of income shock on delay discounting in healthy controls and individuals using substances, and no study to date has examined the impact of income shock on alcohol demand. This study examined the effects of income shock on behavioral economic assessments (i.e., delay discounting and alcohol demand) among a sample of adult participants recruited via Amazon Mechanical Turk ( N = 140). The sample was predominantly middle-aged, male, and White (mean age = 38.81; 37.1% female; 77.1% White). Participants were randomly assigned to one of three income shock narratives (negative, neutral, positive). They completed two versions of the 5-choice delay discounting task for $100 and $1,000 and an alcohol purchase task (APT) before and after the income shock narrative. Following negative income shock, preferences for smaller immediate rewards increased in the $100 version of the discounting task and decreased the price at which alcohol consumption was suppressed to zero (i.e., break point). Sensitivity to price changes in the APT decreased following neutral and positive income shock but not the negative condition. Results replicate and extend prior findings examining the impact of income shock on delay discounting. Furthermore, this is the first study to study income shock effects on alcohol demand. Findings suggest that primary commodity manipulation (i.e., money) can potentially impact decision-making regarding secondary commodities (i.e., alcohol). (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Background: In this study, we investigate the characterization of medical crowdsourcing on GoFundMe for plastic surgery procedures, with overall funds raised being the primary end point., Hypothesis: Certain demographic factors such as sex and race mentioned in campaign narratives are associated with the effectiveness of medical crowdfunding campaigns., Methods: Search terms were used to aggregate fundraising campaigns for plastic surgery medical procedures on GoFundMe. These studies were then stratified by demographics based on campaign text or author consensus, and were further subdivided into categories based on procedure type., Results: Men were found to have higher median shares than women-raising an average of $609 more than female counterparts ( P < 0.05). Fundraising for themes such as lack of insurance, travel costs, lifesaving treatment, and end-of-life expenses were more successful than the theme of psychosocial effects of disease or social impairment. In addition, those that included a smiling picture of the recipient and those created by a friend/relative raised more funds. Although no significant difference was found in fundraising between demographics based on race, a majority (72.8%) of campaigners were White. Across ~2000 plastic surgery campaigns, a total of $10,186,687 were raised from these data., Conclusions: We identified both modifiable and nonmodifiable factors that influence success. These successful campaigns can serve as a learning opportunity for many who have been marginalized by the medical and pharmaceutical industry, and they demonstrate a promising area for demographic studies., Competing Interests: Conflicts of interest and sources of funding: There are no conflicts of interest, affiliations, or relationships to disclose. No competing financial interests exist, and no funding was received for this article., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Scientific research is typically performed by expert individuals or groups who investigate potential solutions in a sequential manner. Given the current worldwide exponential increase in technical innovations, potential solutions for any new problem might already exist, even though they were developed to solve a different problem. Therefore, in crowdsourcing ideation, a research question is explained to a much larger group of individuals beyond the specialist community to obtain a multitude of diverse, outside-the-box solutions. These are then assessed in parallel by a group of experts for their capacity to solve the new problem. The 2 key problems in brain tumor surgery are the difficulty of discerning the exact border between a tumor and the surrounding brain, and the difficulty of identifying the function of a specific area of the brain. Both problems could be solved by a method that visualizes the highly organized fiber tracts within the brain; the absence of fibers would reveal the tumor, whereas the spatial orientation of the tracts would reveal the area's function. To raise awareness about our challenge of developing a means of intraoperative, real-time, noninvasive identification of fiber tracts and tumor borders to improve neurosurgical oncology, we turned to the crowd with a crowdsourcing ideation challenge., Objective: Our objective was to evaluate the feasibility of a crowdsourcing ideation campaign for finding novel solutions to challenges in neuroscience. The purpose of this paper is to introduce our chosen crowdsourcing method and discuss it in the context of the current literature., Methods: We ran a prize-based crowdsourcing ideation competition called HORAO on the commercial platform HeroX. Prize money previously collected through a crowdfunding campaign was offered as an incentive. Using a multistage approach, an expert jury first selected promising technical solutions based on broad, predefined criteria, coached the respective solvers in the second stage, and finally selected the winners in a conference setting. We performed a postchallenge web-based survey among the solvers crowd to find out about their backgrounds and demographics., Results: Our web-based campaign reached more than 20,000 people (views). We received 45 proposals from 32 individuals and 7 teams, working in 26 countries on 4 continents. The postchallenge survey revealed that most of the submissions came from single solvers or teams working in engineering or the natural sciences, with additional submissions from other nonmedical fields. We engaged in further exchanges with 3 out of the 5 finalists and finally initiated a successful scientific collaboration with the winner of the challenge., Conclusions: This open innovation competition is the first of its kind in medical technology research. A prize-based crowdsourcing ideation campaign is a promising strategy for raising awareness about a specific problem, finding innovative solutions, and establishing new scientific collaborations beyond strictly disciplinary domains., (©Philippe Schucht, Andrea Maria Mathis, Michael Murek, Irena Zubak, Johannes Goldberg, Stephanie Falk, Andreas Raabe. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.04.2023.)

Background: As trachoma is eliminated, skilled field graders become less adept at correctly identifying active disease (trachomatous inflammation-follicular [TF]). Deciding if trachoma has been eliminated from a district or if treatment strategies need to be continued or reinstated is of critical public health importance. Telemedicine solutions require both connectivity, which can be poor in the resource-limited regions of the world in which trachoma occurs, and accurate grading of the images., Objective: Our purpose was to develop and validate a cloud-based "virtual reading center" (VRC) model using crowdsourcing for image interpretation., Methods: The Amazon Mechanical Turk (AMT) platform was used to recruit lay graders to interpret 2299 gradable images from a prior field trial of a smartphone-based camera system. Each image received 7 grades for US $0.05 per grade in this VRC. The resultant data set was divided into training and test sets to internally validate the VRC. In the training set, crowdsourcing scores were summed, and the optimal raw score cutoff was chosen to optimize kappa agreement and the resulting prevalence of TF. The best method was then applied to the test set, and the sensitivity, specificity, kappa, and TF prevalence were calculated., Results: In this trial, over 16,000 grades were rendered in just over 60 minutes for US $1098 including AMT fees. After choosing an AMT raw score cut point to optimize kappa near the World Health Organization (WHO)-endorsed level of 0.7 (with a simulated 40% prevalence TF), crowdsourcing was 95% sensitive and 87% specific for TF in the training set with a kappa of 0.797. All 196 crowdsourced-positive images received a skilled overread to mimic a tiered reading center and specificity improved to 99%, while sensitivity remained above 78%. Kappa for the entire sample improved from 0.162 to 0.685 with overreads, and the skilled grader burden was reduced by over 80%. This tiered VRC model was then applied to the test set and produced a sensitivity of 99% and a specificity of 76% with a kappa of 0.775 in the entire set. The prevalence estimated by the VRC was 2.70% (95% CI 1.84%-3.80%) compared to the ground truth prevalence of 2.87% (95% CI 1.98%-4.01%)., Conclusions: A VRC model using crowdsourcing as a first pass with skilled grading of positive images was able to identify TF rapidly and accurately in a low prevalence setting. The findings from this study support further validation of a VRC and crowdsourcing for image grading and estimation of trachoma prevalence from field-acquired images, although further prospective field testing is required to determine if diagnostic characteristics are acceptable in real-world surveys with a low prevalence of the disease., (©Christopher J Brady, R Chase Cockrell, Lindsay R Aldrich, Meraf A Wolle, Sheila K West. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 06.04.2023.)

The study of data quality in crowdsourcing campaigns is currently a prominent research topic, given the diverse range of participants involved. A potential solution to enhancing data quality processes in crowdsourcing is cognitive personalization, which involves appropriately adapting or assigning tasks based on a crowd worker's cognitive profile. There are two common methods for assessing a crowd worker's cognitive profile: administering online cognitive tests, and inferring behavior from task fingerprinting based on user interaction log events. This article presents the findings of a study that investigated the complementarity of both approaches in a microtask scenario, focusing on personalizing task design. The study involved 134 unique crowd workers recruited from a crowdsourcing marketplace. The main objective was to examine how the administration of cognitive ability tests can be used to allocate crowd workers to microtasks with varying levels of difficulty, including the development of a deep learning model. Another goal was to investigate if task fingerprinting can be used to allocate crowd workers to different microtasks in a personalized manner. The results indicated that both objectives were accomplished, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model with 95% accuracy in predicting the accuracy of the microtasks. While we achieved an accuracy of 95%, it is important to note that the small dataset size may have limited the model's performance.

Background: Recent studies have analyzed the factors that contribute to variations in the success of crowdfunding campaigns for a specific cancer type; however, little is known about the influential factors among crowdfunding campaigns for multiple cancers., Objective: The purpose of this study was to examine the relationship between project features and the success of cancer crowdfunding campaigns and to determine whether text features affect campaign success for various cancers., Methods: Using cancer-related crowdfunding projects on the GoFundMe website, we transformed textual descriptions from the campaigns into structured data using natural language processing techniques. Next, we used penalized logistic regression and correlation analyses to examine the influence of project and text features on fundraising project outcomes. Finally, we examined the influence of campaign description sentiment on crowdfunding success using Linguistic Inquiry and Word Count software., Results: Campaigns were significantly more likely to be successful if they featured a lower target amount (Goal amount, β=-1.949, z score=-82.767, P<.001) for fundraising, a higher number of previous donations, agency (vs individual) organizers, project pages containing updates, and project pages containing comments from readers. The results revealed an inverted U-shaped relationship between the length of the text and the amount of funds raised. In addition, more spelling mistakes negatively affected the funds raised (Number of spelling errors, β=-1.068, z score=-38.79, P<.001)., Conclusions: Difficult-to-treat cancers and high-mortality cancers tend to trigger empathy from potential donors, which increases the funds raised. Gender differences were observed in the effects of emotional words in the text on the amount of funds raised. For cancers that typically occur in women, links between emotional words used and the amount of funds raised were weaker than for cancers typically occurring among men., (©Xupin Zhang, Xinqi Tao, Bingxiang Ji, Renwu Wang, Silvia Sörensen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.03.2023.)

During the early months of COVID-19, many people in the US turned to charitable crowdfunding to seek and provide assistance. Little is known about the needs, hopes or experiences that motivated US pandemic crowdfunding and how these were correlated with campaign success. This study uses a mixed-methods data analysis of a randomised cluster sample of 919 US GoFundMe campaigns during the first 7 months of the pandemic. Overall, most campaigns performed poorly, and 38% got no donations at all. The largest proportion of campaigns aimed to address individual, acute financial struggles, often arising from considerable challenges accessing or qualifying for government assistance. These campaigns, as well as those involving campaigners and beneficiaries of colour, tended to be least successful. Qualitative thematic analysis revealed three key crowdfunding motivations that reflect individualistic, agentive responses to the pandemic: struggling, helping and adapting. These motivations reveal a shift away from social suffering and collective mobilisation and towards largely individualised efforts of survival as digital crowdfunding becomes a key domain of crisis response. Crowdfunding platforms are playing an increasingly important role in mediating and influencing individual and collective responses to crisis, which has important political ramifications for how societies perceive and address health emergencies., (© 2022 Foundation for the Sociology of Health & Illness.)

The purpose of this report is to: (1) highlight challenges of transitioning the delivery of simulation from centralized, in-person laboratory to decentralized, home-based, online format; (2) suggest a solution that involves the use of crowdsourcing community-based 3-dimensional printers to produce affordable simulators; and (3) present exploratory research and a test case aiming to identify crowdsourcing frameworks to accomplish this. We present a test case that shows the potential of the proposed solution to scale up the decentralized simulation practices during and beyond the COVID-19 pandemic. As a largely uncharted territory, the test case highlighted successes and areas for improvement that need to be addressed through both theoretical and empirical research and testing before full implementation and scale-up., Competing Interests: The authors have declared they have no conflicts of interest., (Copyright © 2023 Society for Simulation in Healthcare.)

Amidst the Coronavirus crisis, many fundraising projects have emerged to relieve financial burdens resulting from social distancing policies. Crowdfunding is a way to raise money to fund a business, project or charity, through either social media or other online platforms to reach hundreds of potential sponsors. We developed guidelines for effective donation-based crowdfunding through online platforms. Using Futures Research (FR) technique, we conducted our analyses in 3 phases. In Phase 1, we reviewed relevant literature and conducted in-depth interviews of related parties. In Phase 2, we interviewed experts using Ethnographic Futures Research (EFR) technique. In Phase 3, we visualized the future using the principles of Futures Wheel, Cross-impact Matrix and Scenarios. Based on our findings, effective donation-based crowdfunding platforms should adopt Blockchain technology for transparency and accountability, and incentivize donations to keep backers loyal. Founders should be required to obtain fundraising licenses from relevant regulators. Finally, laws and regulations that protect platform users should be standardized internationally. Our proposed guidelines hope to improve the quality and transparency of future fundraising activities., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Sirisawat 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.)