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Contributory science—including citizen and community science—allows scientists to leverage participant-generated data while providing an opportunity for engaging with local community members. Data yielded by participant-generated biodiversity platforms allow professional scientists to answer ecological and evolutionary questions across both geographic and temporal scales, which is incredibly valuable for conservation efforts. The data reported to contributory biodiversity platforms, such as eBird and iNaturalist, can be driven by social and ecological variables, leading to biased data. Though empirical work has highlighted the biases in contributory data, little work has articulated how biases arise in contributory data and the societal consequences of these biases. We present a conceptual framework illustrating how social and ecological variables create bias in contributory science data. In this framework, we present four filters—participation, detectability, sampling and preference—that ultimately shape the type and location of contributory biodiversity data. We leverage this framework to examine data from the largest contributory science platforms—eBird and iNaturalist—in St. Louis, Missouri, the United States, and discuss the potential consequences of biased data. Lastly, we conclude by providing several recommendations for researchers and institutions to move towards a more inclusive field. With these recommendations, we provide opportunities to ameliorate biases in contributory data and an opportunity to practice equitable biodiversity conservation. Read the free Plain Language Summary for this article on the Journal blog.

The healthcare industry is undergoing a profound transformation with the integration of artificial intelligence (AI) into various healthcare settings. However, the successful implementation of AI in healthcare is challenged by several factors, particularly the lack of transparency and explainability in current AI systems. These limitations hamper their effectiveness and reduce their adoption in the field. The key issues that require attention encompass accountability, biases, erroneous results, lack of justification of medical decisions, and the erosion of trust. This paper addresses the challenges raised by non-transparent and non-explainable AI and analyses whether these features jeopardise the responsible integration of AI into healthcare.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets. Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied. The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations. This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

Serial dependence has shown seemingly contradictory effects on visual perception and working memory. While serial dependence promotes perpetual and mnemonic stability, it biases behavioral reports toward prior information. The neural mechanisms that drive both biasing and adaptive stabilizing effects are not well understood. We proposed and tested a reactivation and integration mechanism that can account for these contradictory effects. We used multivariate pattern analyses of EEG data (26 human participants, 17 females, 9 males) to examine the reactivation of prior reported orientation during the delay period of a visual working memory task. The reactivation strength of prior reports, but not prior sensory items, was predictive of the magnitude of serial dependency biases. These reactivated representations integrated with the representation of the current memory item and improved the ability to decode the current contents of memory. Overall, our data provide convergent evidence suggesting that prior reports in a visual working memory task are reactivated on the subsequent trial and become integrated with current memory representations. This similarity-dependent reactivation mechanism drives both report biasing and stabilization effects attributed to serial dependence in working memory. [ABSTRACT FROM AUTHOR]

Ecologists often leverage contributory science, also referred to as citizen science, to answer large-scale spatial and temporal biodiversity questions. Contributory science platforms, such as eBird and iNaturalist, provide researchers with incredibly fine-scale data to track biodiversity. However, data generated by these platforms are spatially biased. Research has shown that factors like income, race, and historical redlining can influence spatial patterns of reported eBird and iNaturalist data. However, the role of contemporary residential segregation remains unclear. Additionally, we do not understand how these variables potentially relate to certain Census tracts having more or less biodiversity data than you would expect based on size or population density. To further understand the social factors that may contribute to spatial biases in eBird and iNaturalist data, we focused on three cities within the USA (Oakland, California; St. Louis, Missouri; and Baltimore, Maryland). We specifically investigated how income, race, segregation, and redlining via Home Owners' Loan Corporation grades (grades A = best, B, C, and D = hazardous and "redlined") are associated with the difference between reported and expected observations based on area and human population density. We find that census tracts with higher income and more White people generally have more observations than expected. We only find segregation to influence differences in reported and expected observations in Baltimore, with more segregated Census tracts having more observations than expected. Lastly, we find that grades C and D consistently have fewer data than expected compared with grades A and B for both platforms in each city. Our results show that although each city has distinct societal and ecological features, societal inequity permeates each city to shape the uptake of data for two of the largest sources of biodiversity data. [ABSTRACT FROM AUTHOR]

Facial recognition is an artificial intelligence-based technology that, like many other forms of artificial intelligence, suffers from an accuracy deficit. This paper focuses on one particular use of facial recognition, namely identification, both as authentication and as recognition. Despite technological advances, facial recognition technology can still produce erroneous identifications. This paper addresses algorithmic identification failures from an upstream perspective by identifying the main causes of misidentifications (in particular, the probabilistic character of this technology, its 'black box' nature and its algorithmic bias) and from a downstream perspective, highlighting the possible legal consequences of such failures in various scenarios (namely liability lawsuits). In addition to presenting the causes and effects of such errors, the paper also presents measures that can be deployed to reduce errors and avoid liabilities. [ABSTRACT FROM AUTHOR]

Eco-acoustic indices allow us to rapidly evaluate habitats and ecosystems and derive information about anthropophonic impacts. However, it is proven that indices' values and trends are not comparable between studies. These incongruences may be caused by the availability on the market of recorders with different characteristics and costs. Thus, there is a need to reduce these biases and incongruences to ensure an accurate analysis and comparison between soundscape ecology studies and habitat assessments. In this study, we propose and validate an audio recording equalization protocol to reduce eco-acoustic indices' biases, by testing three soundscape recorder models: Song Meter Micro, Soundscape Explorer Terrestrial and Audiomoth. The equalization process aligns the signal amplitude and frequency response of the soundscape recorders to those of a type 1 level meter. The adjustment was made in MATLAB R2023a using a filter curve generated comparing a reference signal (white noise); the measurements were performed in an anechoic chamber using 11 audio sensors and a type 1 sound level meter (able to produce a.WAV file). The statistical validation of the procedure was performed on recordings obtained in an urban and Regional Park (Italy) assessing a significant reduction in indices' biases on the Song Meter Micro and Audiomoth. [ABSTRACT FROM AUTHOR]

This paper focuses on the ethics of AI and the principles behind it. Prior to the rules governing AI, it is necessary to understand the ethical issues related to the development, implementation and responsible use of AI systems. These ethical foundations are necessary to protect the rights and dignity of individuals, ensure fairness in data access and processing, minimise biases and risks associated with algorithms, promote transferability in automated decisions, and promote trustworthiness in technology, human benefit and social welfare. It is also proposed to study the ethics of AI from an all-encompassing point of view that integrates both applied ethics and metaethics of AI. [ABSTRACT FROM AUTHOR]

Observational studies on COVID-19 vaccine effectiveness (VE) have provided critical real-world data, informing public health policy globally. These studies, primarily using pre-existing data sources, have been indispensable in assessing VE across diverse populations and developing sustainable vaccination strategies. Cohort design is frequently employed in VE research. The rapid implementation of vaccination campaigns during the COVID-19 pandemic introduced differential vaccination influenced by sociodemographic disparities, public policies, perceived risks, health-promoting behaviors, and health status, potentially resulting in biases such as healthy user bias, healthy vaccinee effect, frailty bias, differential depletion of susceptibility bias, and confounding by indication. The overwhelming burden on healthcare systems has escalated the risk of data inaccuracies, leading to outcome misclassifications. Additionally, the extensive array of diagnostic tests used during the pandemic has also contributed to misclassification biases. The urgency to publish quickly may have further influenced these biases or led to their oversight, affecting the validity of the findings. These biases in studies vary considerably depending on the setting, data sources, and analytical methods and are likely more pronounced in low- and middle-income country (LMIC) settings due to inadequate data infrastructure. Addressing and mitigating these biases is essential for accurate VE estimates, guiding public health strategies, and sustaining public trust in vaccination programs. Transparent communication about these biases and rigorous improvement in the design of future observational studies are essential.

IntroductionArtificial Intelligence (AI) is increasingly being integrated into anesthesiology to enhance patient safety, improve efficiency, and streamline various aspects of practice.ObjectiveThis study aims to evaluate whether AI-generated images accurately depict the demographic racial and ethnic diversity observed in the Anesthesia workforce and to identify inherent social biases in these images.MethodsThis cross-sectional analysis was conducted from January to February 2024. Demographic data were collected from the American Society of Anesthesiologists (ASA) and the European Society of Anesthesiology and Intensive Care (ESAIC). Two AI text-to-image models, ChatGPT DALL-E 2 and Midjourney, generated images of anesthesiologists across various subspecialties. Three independent reviewers assessed and categorized each image based on sex, race/ethnicity, age, and emotional traits.ResultsA total of 1,200 images were analyzed. We found significant discrepancies between AI-generated images and actual demographic data. The models predominantly portrayed anesthesiologists as White, with ChatGPT DALL-E2 at 64.2% and Midjourney at 83.0%. Moreover, male gender was highly associated with White ethnicity by ChatGPT DALL-E2 (79.1%) and with non-White ethnicity by Midjourney (87%). Age distribution also varied significantly, with younger anesthesiologists underrepresented. The analysis also revealed predominant traits such as “masculine, ““attractive, “and “trustworthy” across various subspecialties.ConclusionAI models exhibited notable biases in gender, race/ethnicity, and age representation, failing to reflect the actual diversity within the anesthesiologist workforce. These biases highlight the need for more diverse training datasets and strategies to mitigate bias in AI-generated images to ensure accurate and inclusive representations in the medical field.

Abstract Contributory science—including citizen and community science—allows scientists to leverage participant‐generated data while providing an opportunity for engaging with local community members. Data yielded by participant‐generated biodiversity platforms allow professional scientists to answer ecological and evolutionary questions across both geographic and temporal scales, which is incredibly valuable for conservation efforts. The data reported to contributory biodiversity platforms, such as eBird and iNaturalist, can be driven by social and ecological variables, leading to biased data. Though empirical work has highlighted the biases in contributory data, little work has articulated how biases arise in contributory data and the societal consequences of these biases. We present a conceptual framework illustrating how social and ecological variables create bias in contributory science data. In this framework, we present four filters—participation, detectability, sampling and preference—that ultimately shape the type and location of contributory biodiversity data. We leverage this framework to examine data from the largest contributory science platforms—eBird and iNaturalist—in St. Louis, Missouri, the United States, and discuss the potential consequences of biased data. Lastly, we conclude by providing several recommendations for researchers and institutions to move towards a more inclusive field. With these recommendations, we provide opportunities to ameliorate biases in contributory data and an opportunity to practice equitable biodiversity conservation. Read the free Plain Language Summary for this article on the Journal blog.

Shravani Durbhakula,1 Tony Y Wang,2 Kara G Segna,2 Gerard R Limerick,2,3 Mustafa Y Broachwala,3 Michael E Schatman,4 Munfarid A Zaidi,5 Ingharan James Siddarthan,6 Serkan Toy7 1Department of Anesthesiology, Division of Pain Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; 2Department of Anesthesiology & Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA; 3Department of Physical Medicine & Rehabilitation, Johns Hopkins School of Medicine, Baltimore, MD, USA; 4Department of Anesthesiology, Perioperative Care, and Pain Medicine, New York University Grossman School of Medicine, New York, NY, USA; 5Department of Anesthesiology, Baylor College of Medicine, Houston, TX, USA; 6Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA; 7Departments of Basic Science Education and Health Systems & Implementation Science, Virginia Tech Carilion School of Medicine, Roanoke, VA, USACorrespondence: Shravani Durbhakula, Department of Anesthesiology, Division of Pain Medicine, Vanderbilt University Medical Center, Nashville, TN, USA, Email s.durbhakula@vumc.orgObjective: To examine the immediate effects of a comprehensive pain course on medical students’ pre-existing perceptions and attitudes toward pain patients and opioid management.Methods: First-year medical students at a major academic medical center enrolled in a required pre-clerkship pain course in June 2020 and completed pre- and post-course online surveys with Likert-scale questions about their attitudes toward pain management and opioid-related issues. Additionally, the surveys included a free-text question where the students listed the first five words that came to mind when hearing the word “opioids”. These words were categorized as “professional” or “lay” words and further as having “positive”, “negative”, or “neutral” connotations. Data analyses included descriptive statistics, as well as non-parametric and parametric tests.Results: Fifty-four of the 119 students responded to pretest and posttest surveys and were included in paired analyses. There was a significant difference between the number of professional words used before (M=1.21, SD=0.97) and after the course (M=2.40 SD=1.33); t(52)=− 6.39, P< 0.001. Students also used more lay-positive words after the course (M=0.81, SD=0.63) than they used pre-course (M=0.23, SD=0.43); t(51)=− 5.98, P< 0.001. Students’ post-course responses to several key Likert-scale questions showed significant shifts toward more positive attitudes about caring for patients with pain. For example, students acknowledged greater comfort in providing opioids for chronic pain (P< 0.001) where appropriate, and enhanced interest in handling complex pain cases (P< 0.001).Conclusion: Results showed that a comprehensive, multi-disciplinary pain course could greatly enhance first-year medical students’ attitudes toward pain management, chronic pain patients, and the complex issues surrounding opioids.Keywords: medical education, opioids, stigma, epidemic, biases, pain

Contributory science—including citizen and community science—allows scientists to leverage participant‐generated data while providing an opportunity for engaging with local community members. Data yielded by participant‐generated biodiversity platforms allow professional scientists to answer ecological and evolutionary questions across both geographic and temporal scales, which is incredibly valuable for conservation efforts.The data reported to contributory biodiversity platforms, such as eBird and iNaturalist, can be driven by social and ecological variables, leading to biased data. Though empirical work has highlighted the biases in contributory data, little work has articulated how biases arise in contributory data and the societal consequences of these biases.We present a conceptual framework illustrating how social and ecological variables create bias in contributory science data. In this framework, we present four filters—participation, detectability, sampling and preference—that ultimately shape the type and location of contributory biodiversity data. We leverage this framework to examine data from the largest contributory science platforms—eBird and iNaturalist—in St. Louis, Missouri, the United States, and discuss the potential consequences of biased data.Lastly, we conclude by providing several recommendations for researchers and institutions to move towards a more inclusive field. With these recommendations, we provide opportunities to ameliorate biases in contributory data and an opportunity to practice equitable biodiversity conservation. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Th is study offers empirical insights into investor behavior and its correlation with various behavioral biases in the context of investment decisions in the Pakistan Stock Exchange (PSX). Data was collected through a structured questionnaire from 261 individual investors in Pakistan. The study employs hierarchical regression analysis to test the hypothesis. It considers several behavioral biases, and statistically, anchoring and adjustment, overconfidence, and herding show a significant impact. The study uses financial literacy to examine its moderating effects on these biases, and the result suggests that it significantly influences behavioral biases related to investment decisions. The results underscore the unique investment behaviors in emerging markets, contrasting with established norms in well-developed financial markets. These findings can inform policymakers and stock market authorities about investor decision-making in emerging economies.

This research paper conducts a Critical Discourse Analysis of the Instagram campaign run by WeAreHERe, a peer-to-peer mentorship program designed for women high school students and first-year university students at Politecnico di Torino. The study scrutinizes 92 posts shared on the WeAreHERe Instagram page during the academic year 2021-2022. The primary goal of the research is, first, to bridge an existing gap in the literature by connecting studies on stereotypes, biases, and mentorship as a constructive means to promote women’s role models with research on gender-based structural oppression. Secondly, this study aims to enhance the WeAreHERe campaign through a comprehensive analysis, ultimately providing insights for shaping future policies about mentorship programs for female students in STEM fields. The analysis showed that most of the posts focus on the individual level rather than on structural oppression and the role models they depict are presented as relatable rather than unique humans. Even when the motivational posts acknowledge the existence of a gender-oppressive structure they seldom mention it clearly, failing to uncover the generative link between this structure of oppression and gender biases and stereotypes. The results show that the literature on stereotypes and biases could be enriched by an approach focused on structural oppression. From these results we developed some guidelines that could help the WeAreHERe campaign – and other similar campaigns that aims at fighting stereotypes about women in the STEM field – to tackle issues of systematic oppression. The use of role models is more effective when it highlights systematic oppression and favours the creation of communities that would help bringing systematic change.

In Ukraine, important processes are underway to identify those areas of psychotherapy that deserve official recognition by law. The official recognition of psychotherapeutic methods and techniques is associated with the fact that they are recognized as clinically and statistically effective. Such psychotherapy is reimbursed by insurance and is actively promoted in training courses, often "at the expense of other methods of psychotherapy." The psychotherapeutic procedure itself is becoming more and more standardized and impersonal, conforming to a protocol. The fate of client-centered and experiential psychotherapy (PCE-therapy) is not so easy in the world: evidence of its effectiveness is not reflected in the NICE (National Center for Collaboration in Mental Health) guidelines for depression and schizophrenia, which to some extent determine health policy. Therefore, there are widespread prejudices about the ineffectiveness of this type of therapy: it is not effective for severe mental illness, crisis states, specific phobias and traumas, behavioral problems, for those clients who need a directive approach, for assessing and diagnosing clients, etc. The article presents specific studies of the effectiveness of client-centered and experiential psychotherapy for specific symptoms (anxiety, depression, schizophrenia, borderline personality disorders, and some somatic disorders), as well as data from a meta-analysis of the effectiveness of client-centered and experiential therapy. PCE-therapy proved to be highly effective according to the criteria: "before-after" treatment; 2) "treatment-no treatment"; 3) "comparison with other types of psychotherapy" - the data obtained are generally statistically and clinically equivalent in terms of effectiveness to other methods of psychotherapy. PCE therapy proved to be the most effective in the following cases: interpersonal problems, self-harming behavior, coping with chronic somatic diseases, psychosis. It turned out that cognitive behavioral therapy (CBT) has mixed benefits compared to PCE therapy. However, Elliott et al. note that, firstly, these "studies were mostly performed by CBT therapists" and, secondly, "low-quality versions of PCE therapy were used as comparison conditions". The most effective methods of PCE therapy were EFT and PCT. A list of approaches included in PCA-therapy is given, and their general characteristics are given.

Artificial Intelligence (AI) has revolutionized the fashion industry, impacting various stages of the fashion lifecycle and offering promises of enhanced operational efficiency, personalized consumer experiences, and contributions to sustainability and innovation. This paper outlines the main trends, implications, constraints, and potential risks associated with AI in fashion design and production. In particular, the paper delves into biases in AI-powered systems, emphasizing data, algorithmic, and transparency gaps that can perpetuate discrimination. Bringing ethics and pluralism into AI-powered design, the paper introduces intersectional feminism and feminist design principles, which can help envisioning a more inclusive, equitable, and unbiased fashion discourse. In conclusion, the paper contends that feminist design principles can inspire the development of more inclusive and ethical AI-powered fashion. By challenging norms, promoting diverse representations, and incorporating user perspectives, feminist design can contribute to a socially responsible and equitable future for fashion technology.

Ecologists often leverage contributory science, also referred to as citizen science, to answer large-scale spatial and temporal biodiversity questions. Contributory science platforms, such as eBird and iNaturalist, provide researchers with incredibly fine-scale data to track biodiversity. However, data generated by these platforms are spatially biased. Research has shown that factors like income, race, and historical redlining can influence spatial patterns of reported eBird and iNaturalist data. However, the role of contemporary residential segregation remains unclear. Additionally, we do not understand how these variables potentially relate to certain Census tracts having more or less biodiversity data than you would expect based on size or population density. To further understand the social factors that may contribute to spatial biases in eBird and iNaturalist data, we focused on three cities within the USA (Oakland, California; St. Louis, Missouri; and Baltimore, Maryland). We specifically investigated how income, race, segregation, and redlining via Home Owners’ Loan Corporation grades (grades A = best, B, C, and D = hazardous and “redlined”) are associated with the difference between reported and expected observations based on area and human population density. We find that census tracts with higher income and more White people generally have more observations than expected. We only find segregation to influence differences in reported and expected observations in Baltimore, with more segregated Census tracts having more observations than expected. Lastly, we find that grades C and D consistently have fewer data than expected compared with grades A and B for both platforms in each city. Our results show that although each city has distinct societal and ecological features, societal inequity permeates each city to shape the uptake of data for two of the largest sources of biodiversity data.

This paper examines a sample of 20 sustainability-oriented real-world experiment reports frorn 2006 to 2020, with the aim of uncovering the logic and methods used to demonstrate their success, that means, to show that they achieved their stated objectives. Following a distinction often made in the social sciences, I look for features of either the variance orthe process approach to causal inference. I find that reports of transition experiments, socio-technical experiments, and community-based interventions display characteristic features of the process approach. Reports of trials, pilots, and field experiments, on the other hand, mostly use the variance approach to demonstrate success. An important observation is the limited recognition of possible biases related to the methods used orthe data. 1 describe a number of possible biases that may be of importance in the context of sustainability-oriented real-world experiments. Irnportant examples include measurement errors and biases in participant selection. Recognising the biases and correcting them where necessary can strengthen the validity of the findings obtained and help other researchers in designingtheir experiments. [ABSTRACT FROM AUTHOR]

The last decade has seen a rise in big data approaches, including in the humanities, whereby large quantities of data are collected and analysed. In this paper, we discuss long-form audio recordings that result from individuals wearing a recording device for many hours. Linguists, psychologists and anthropologists can use them, for example, to study infants' or adults' linguistic behaviour. In the past, recorded individuals and communities have resided in high-income countries (HICs) almost exclusively. Recognising the need for better representation of all cultures and linguistic experiences, researchers have more recently started to collect long-form audio recordings in low- and middle-income countries (LMICs). We aim to help researchers to collect, analyse and use these recordings ethically. To do so, we identify four main ethical challenges linked to research that relies on long-form recordings in LMICs. We provide recommendations to overcome these challenges. These considerations should be useful to researchers employing other big data techniques collected via wearables. [ABSTRACT FROM AUTHOR]

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Purpose: Despite the rising prevalence of developmental disabilities (DD) in the US, there remains insufficient training for healthcare professionals to care for this medically underserved population - particularly adults. The National Inclusive Curriculum for Health Education (NICHE) aims to improve attitudes and knowledge towards people with intellectual and developmental disabilities (PWIDD); herein we describe one such intervention. Method: The intervention integrated didactic, panel presentation and clinical skills components into a 2nd year medical school curriculum. The didactic session, covering health and assessment of PWIDDs, history of IDD, stigma, etc., was co-taught by a developmental pediatrician, family medicine physician and social worker. A panel of 3 adult self-advocates (SAs) with DD and a parent of a child with DD spoke about their lived experiences. One week later, students practiced taking clinical histories of SAs within small group settings with adult PWIDDs, facilitated by medical school faculty. Students completed the NICHE Knowledge(49 items) and Attitudes (60 items) surveys. The evaluation analyzed pre/post intervention differences in a) knowledge and attitude scores overall and b) by student age, gender, intended medical specialty, and prior experiences with PWIDDs. Open-ended comments were analyzed with content analysis. Results: Overall Knowledge scores increased from pre-to posttest (n = 85; 65[19] vs. 73[17], p = 0.00), while Attitudes score improved (i.e., decreased) (n = 88; 0.55 [.06] vs. 0.53 [0.06]); p = 0.00). Higher pretest knowledge was found among female identified students (vs. others; p = 0.01) and those knowing > = 5 PWIDD (vs < 5; p = 0.02). Students characterize their IDD training and experience prior to intervention as 'lacking' and described the sessions as effective. Conclusions: A brief (4 hours total) intervention was associated with modest but significant improved knowledge and attitudes towards PWIDDs. Replication and sustainability of this and other NICHE interventions are needed to fill gaps in PWIDDs' health care. [ABSTRACT FROM AUTHOR]

Prevailing social, economic and political ideas and paradigms constitute the lens through which scientists observe, assess, and understand the world. This affects how we interpret and understand the mechanisms governing the interaction between organisms and has led, in most cases, to dominant explanations and paradigms that are difficult to overthrow. This is the case of ecological theory, whose perspectives have followed the rationale of societal changes. From the industrial revolution to very recently, species competition for resources was regarded as one of the main drivers of species interactions. Nowadays, a new and rapidly growing way of thinking emerged, fueled by the high sequencing capacities, ultra-resolution microscopy and the slowly growing number of different social and gender perspectives participating in ecological studies: that living beings are not just single organisms interacting with other single organisms, but complex communities of macro- and microorganisms living and evolving together. The information emerging from this field is bringing new light to previously disregarded aspects of the ecological interactions that, in our opinion, will change the main paradigms in ecology. As members of a South American scientific network of Aquatic Microbial Ecology (MicroSudAqua), here we propose to explore alternative explanations for ecological observations, searching for new traits accounting for cooperation between microorganisms as a fundamental evolutionary and ecological strategy. [ABSTRACT FROM AUTHOR]

Missing data can introduce biases and affect the generalizability of research findings, undermining the scientific rigor of studies and impeding the development of evidence-based practices. To overcome these challenges, health-care providers and researchers must adopt robust strategies to identify, prevent, and handle missing data effectively. This includes implementing standardized data collection procedures, utilizing advanced statistical techniques to account for missingness, and embracing emerging technologies that facilitate comprehensive and timely data capture. By prioritizing the management of missing data, health-care professionals and researchers can enhance patient care, improve research quality, and ultimately contribute to better health outcomes for individuals and populations.

Abstract A better understanding of factors that can affect preferences and choices may contribute to more accurate decision-making. Several studies have investigated the effects of cognitive biases on decision-making and their relationship with cognitive abilities and thinking dispositions. While studies on behaviour, attitude, personality, and health worries have examined their relationship with human values, research on cognitive bias has not investigated its relationship to individual differences in human values. The purpose of this study was to explore individual differences in biased choices, examining the relationships of the human values self-direction, conformity, power, and universalism with the anchoring effect, the framing effect, the certainty effect, and the outcome bias, as well as the mediation of need for cognition and the moderation of numeracy in these relationships. We measured individual differences and within-participant effects with an online questionnaire completed by 409 Brazilian participants, with an age range from 18 to 80 years, 56.7% female, and 43.3% male. The cognitive biases studied consistently influenced choices and preferences. However, the biases showed distinct relationships with the individual differences investigated, indicating the involvement of diverse psychological mechanisms. For example, people who value more self-direction were less affected only by anchoring. Hence, people more susceptible to one bias were not similarly susceptible to another. This can help in research on how to weaken or strengthen cognitive biases and heuristics.

We present a data set on marine mites (family Halacaridae) in European waters. The data set gathers all the published records of marine mites from the North European Seas, Lusitania, Mediterranean Sea and Black Sea marine provinces, all belonging to the temperate North Atlantic geographical realm. The database includes 3006 records collected from 260 original publications. For each record, the dataset provides complementary taxonomic, geographical, and ecological information, as well as remarks regarding the sampling methods used in each study. We use this dataset to briefly discuss potential knowledge gaps and biases across marine regions and habitats. We hope that these data will provide a baseline for further studies in biogeography and ecology.

Eco-acoustic indices allow us to rapidly evaluate habitats and ecosystems and derive information about anthropophonic impacts. However, it is proven that indices’ values and trends are not comparable between studies. These incongruences may be caused by the availability on the market of recorders with different characteristics and costs. Thus, there is a need to reduce these biases and incongruences to ensure an accurate analysis and comparison between soundscape ecology studies and habitat assessments. In this study, we propose and validate an audio recording equalization protocol to reduce eco-acoustic indices’ biases, by testing three soundscape recorder models: Song Meter Micro, Soundscape Explorer Terrestrial and Audiomoth. The equalization process aligns the signal amplitude and frequency response of the soundscape recorders to those of a type 1 level meter. The adjustment was made in MATLAB R2023a using a filter curve generated comparing a reference signal (white noise); the measurements were performed in an anechoic chamber using 11 audio sensors and a type 1 sound level meter (able to produce a .WAV file). The statistical validation of the procedure was performed on recordings obtained in an urban and Regional Park (Italy) assessing a significant reduction in indices’ biases on the Song Meter Micro and Audiomoth.

Education for human rights is achieved both through formal educational approaches and non-formal educational activities. Project-based learning is a valuable exercise in applying knowledge, skills, and attitudes acquired throughout years of schooling in different life contexts. The skills of participatory citizenship, specific to a democratic society, can be formed and developed in an integrated manner through educational projects.

Recommendations for economic policies can be based on different theoretical perspectives and may present hidden biases. Identifying these biases is challenging when they are embedded in recommendations from sources with high technological and social disruptive potential, where a good level of impartiality is expected, such as contemporary large language models. Thus, a questionnaire was administered to economists affiliated with the Brazilian academic community to assess their perception of orthodox/heterodox biases in economic policy recommendations derived from interactions with ChatGPT. The results showed that: i) there is still no consensus on the concepts of orthodoxy and heterodoxy in Brazil; ii) there are indications of a positive relationship between how self-proclaimed heterodox (orthodox) an economist is and how heterodox (orthodox) the perceived bias in an economic policy is; iii) it was not possible to identify a consistently orthodox or heterodox bias in ChatGPT's recommendations, which exhibited a good degree of impartiality.

This study uses data from the KSE-100 stock index from 2009 to 2021 to investigate the influence of technological advancements, investors' sentiments, and behavioral factors on stock market inefficiency. The study employs a threshold regression model to distinguish between different thresholds of these factors and a Wald test is used to determine the significance of the variances among thresholds. The results indicate that technological advancements, investors' sentiments, and behavioral factors significantly affect the predictability of returns in the KSE-100 index. This study is unique in the context of Pakistan and provides a comprehensive framework for understanding stock market operations. The findings have practical applications for individuals, researchers, managers, institutions, and policymakers seeking to better understand the impact of technology, society, and behavioral biases on stock markets. Furthermore, this study provides unique insights into addressing the issue of market inefficiency by identifying the factors responsible for such behavior.

ABSTRACTPurpose Despite the rising prevalence of developmental disabilities (DD) in the US, there remains insufficient training for healthcare professionals to care for this medically underserved population – particularly adults. The National Inclusive Curriculum for Health Education (NICHE) aims to improve attitudes and knowledge towards people with intellectual and developmental disabilities (PWIDD); herein we describe one such intervention.Method The intervention integrated didactic, panel presentation and clinical skills components into a 2nd year medical school curriculum. The didactic session, covering health and assessment of PWIDDs, history of IDD, stigma, etc., was co-taught by a developmental pediatrician, family medicine physician and social worker. A panel of 3 adult self-advocates (SAs) with DD and a parent of a child with DD spoke about their lived experiences. One week later, students practiced taking clinical histories of SAs within small group settings with adult PWIDDs, facilitated by medical school faculty. Students completed the NICHE Knowledge(49 items) and Attitudes (60 items) surveys. The evaluation analyzed pre/post intervention differences in a) knowledge and attitude scores overall and b) by student age, gender, intended medical specialty, and prior experiences with PWIDDs. Open-ended comments were analyzed with content analysis.Results Overall Knowledge scores increased from pre-to posttest (n = 85; 65[19] vs. 73[17], p = 0.00), while Attitudes score improved (i.e., decreased) (n = 88; 0.55 [.06] vs. 0.53 [0.06]); p = 0.00). Higher pretest knowledge was found among female identified students (vs. others; p = 0.01) and those knowing > = 5 PWIDD (vs

Copyright of IDP: Revista de Internet, Derecho y Politica is the property of Universitat Oberta de Catalunya and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many systems engineering projects begin with the involvement of stakeholders to aid in decision-making processes. As an application of systems engineering, systems architecture involves the documentation of stakeholder needs gathered via elicitation and the transformation of these needs into requirements for a system. Within human–machine teaming, systems architecture allows for the creation of a system with desired characteristics elicited from stakeholders involved with the project or system. Though stakeholders can be excellent sources for expert opinion, vested interests in a project may potentially bias stakeholders and impact decision-making processes. These biases may influence the design of the system architecture, potentially resulting in a system that is developed with unbalanced and misrepresented stakeholder preferences. This paper presents an activity analysis of the Stakeholder Needs and Requirements Process as described in the Systems Engineering Body of Knowledge (SEBoK) to identify potential biases associated with this elicitation process. As part of the research presented in this paper, a workshop was conducted where currently practicing systems architects provided feedback regarding perceptions of biases encountered during the elicitation process. The findings of this research will aid systems architects, developers, and users in understanding how biases may impact stakeholder elicitation within the architecting process. [ABSTRACT FROM AUTHOR]

Copyright of IDP: Revista de Internet, Derecho y Politica is the property of Universitat Oberta de Catalunya and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper investigates the effect of heuristics and common biases in entrepreneurs' marketing performance of their biotechnological ventures. There is limited research on the impact of heuristics and biases in the field of entrepreneurship in the biotechnology industry. Therefore, the study addressed this research gap and adopted a quantitative methodology to highlight the effect of heuristics and biases: herding, overconfidence, anchoring, and loss aversion on marketing performance. Data was collected from nascent bio-entrepreneurs who were active in the U.S. biotech industry. A total sample of 243 respondents was analyzed through different statistical techniques. Confirmatory factor analysis was conducted, followed by structural equation modeling. Our study's findings supported the proposed hypotheses, and it was concluded that common biases and heuristics significantly impact the marketing performance of biotech ventures. The study has significantly contributed to the existing literature on common heuristics and biases and provides suggestions for future research. [ABSTRACT FROM AUTHOR]

A better understanding of factors that can affect preferences and choices may contribute to more accurate decision-making. Several studies have investigated the effects of cognitive biases on decision-making and their relationship with cognitive abilities and thinking dispositions. While studies on behaviour, attitude, personality, and health worries have examined their relationship with human values, research on cognitive bias has not investigated its relationship to individual differences in human values. The purpose of this study was to explore individual differences in biased choices, examining the relationships of the human values self-direction, conformity, power, and universalism with the anchoring effect, the framing effect, the certainty effect, and the outcome bias, as well as the mediation of need for cognition and the moderation of numeracy in these relationships. We measured individual differences and within-participant effects with an online questionnaire completed by 409 Brazilian participants, with an age range from 18 to 80 years, 56.7% female, and 43.3% male. The cognitive biases studied consistently influenced choices and preferences. However, the biases showed distinct relationships with the individual differences investigated, indicating the involvement of diverse psychological mechanisms. For example, people who value more self-direction were less affected only by anchoring. Hence, people more susceptible to one bias were not similarly susceptible to another. This can help in research on how to weaken or strengthen cognitive biases and heuristics. [ABSTRACT FROM AUTHOR]

Copyright of Revista Evidenciação Contábil & Finanças is the property of Revista Evidenciacao Contabil & Financas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Wildlife‐documentary production has expanded over recent decades, while studies report reduced direct contact with nature. The role of documentaries and other electronic content in educating people about biodiversity is therefore likely to be growing increasingly important. This study investigated whether the content of wildlife documentaries is an accurate reflection of the natural world and whether conservation messaging in documentaries has changed over time. We sampled an online film database (n = 105) to quantify the representation of taxa and habitats over time, and compared this with actual taxonomic diversity in the natural world. We assessed whether the precision with which an organism could be identified from the way it was mentioned varied between taxa or across time, and whether mentions of conservation and anthropogenic impacts on the natural world changed over time. Mentions of organisms (n = 374) were very biased towards vertebrates (81.1% of mentions) relative to invertebrates (17.9% of mentions), despite vertebrates representing only 3.4% of described species, compared to 74.9% for invertebrates. Mentions were highly variable across groups and between time periods, particularly for insects, fish and reptiles. Plants had a consistently low representation across time periods. A range of habitats was represented, the most common being tropical forest and the least common being deep ocean, but there was no change over time. Mentions identifiable to species were significantly different between taxa, with 41.8% of mentions of vertebrates identifiable to species compared with just 7.5% of invertebrate mentions and 10% of plant mentions. This did not change over time. Conservation was mentioned in 16.2% of documentaries overall, but in almost 50% of documentaries in the current decade. Anthropogenic impacts were mentioned in 22.1% of documentaries and never before the 1970s. Our results show that documentaries provide a diverse picture of nature with an increasing focus on conservation, with likely benefits for public awareness. However, they overrepresent vertebrate species, potentially directing public attention towards these taxa. We suggest widening the range of taxa featured to redress this and call for a greater focus on threats to biodiversity to improve public awareness. Read the free Plain Language Summary for this article on the Journal blog.

Prevailing social, economic and political ideas and paradigms constitute the lens through which scientists observe, assess, and understand the world. This affects how we interpret and understand the mechanisms governing the interaction between organisms and has led, in most cases, to dominant explanations and paradigms that are difficult to overthrow. This is the case of ecological theory, whose perspectives have followed the rationale of societal changes. From the industrial revolution to very recently, species competition for resources was regarded as one of the main drivers of species interactions. Nowadays, a new and rapidly growing way of thinking emerged, fueled by the high sequencing capacities, ultra-resolution microscopy and the slowly growing number of different social and gender perspectives participating in ecological studies: that living beings are not just single organisms interacting with other single organisms, but complex communities of macro- and microorganisms living and evolving together. The information emerging from this field is bringing new light to previously disregarded aspects of the ecological interactions that, in our opinion, will change the main paradigms in ecology. As members of a South American scientific network of Aquatic Microbial Ecology (MicroSudAqua), here we propose to explore alternative explanations for ecological observations, searching for new traits accounting for cooperation between microorganisms as a fundamental evolutionary and ecological strategy. * Photo: Hyalosphenia papilio (Amoebozoa; Arcellinida), an amoeba species that hosts endosymbiotic algae related to the genus Chlorella. These organisms live in boreal peat bogs, an oligotrophic environment where they obtain much needed nutrients through their photosynthesizing partners. The amoeba requires the alga to close its life cycle, and has never been observed without it. Image: Prof. Edward A. D. Mitchell

It is generally agreed that one origin of machine bias is resulting from characteristics within the dataset on which the algorithms are trained, i.e., the data does not warrant a generalized inference. We, however, hypothesize that a different 'mechanism' may also be responsible for machine bias, namely that biases may originate from (i) the programmers' cultural background, including education or line of work, or (ii) the contextual programming environment, including software requirements or developer tools. Combining an experimental and comparative design, we study the effects of cultural and contextual metaphors, and test whether each of these are 'transferred' from the programmer to the program, thus constituting a machine bias. Our results show that (i) cultural metaphors influence the programmer's choices and (ii) contextual metaphors induced through priming can be used to moderate or exacerbate the effects of the cultural metaphors. Our studies are purposely performed with users of varying educational backgrounds and programming skills stretching from novice to proficient. [ABSTRACT FROM AUTHOR]

Copyright of IUSTA is the property of Universidad Santo Tomas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)