A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health.

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

Crowdsourcing citizens for statewide mapping of per- and polyfluoroalkyl substances (PFAS) in Florida drinking water.

Per- and polyfluoroalkyl substances (PFAS) are a class of persistent chemicals that have been associated with a diverse array of adverse environmental and human health related effects. In addition to a growing list of health concerns, PFAS are also ubiquitously used and pervasive in our natural and built environments, and they have an innate ability to be highly mobile once released into the environment with an unmatched ability to resist degradation. As such, PFAS have been detected in a wide variety of environmental matrices, including soil, water, and biota; however, the matrix that largely dictates human exposure to PFAS is drinking water, in large part due to their abundance in water sources and our reliance on drinking water. As Florida is heavily reliant upon water and its varying sources, the primary objective of this study was to survey the presence of PFAS in drinking water collected from taps from the state of Florida (United States). In this study, 448 drinking water samples were collected by networking with trained citizen scientists, with at least one sample collected from each of the 67 counties in Florida. Well water, tap water, and bottled water, all sourced from Florida, were extracted and analyzed (31 PFAS) using isotope dilution and ultra-high-performance liquid chromatography - tandem mass spectrometry (UHPLC-MS/MS). Overall, when examining ∑PFAS: the minimum, maximum, median, and mean were ND, 219, 2.90, and 14.06 ng/L, respectively. The data herein allowed for a comparison of PFAS in drinking water geographically within the state of Florida, providing vital baseline concentrations for prospective monitoring and highlighting hotspots that require additional testing and mitigation. By incorporating citizen scientists into the study, we aimed to educate impacted communities regarding water quality issues and solutions.

Developing Crowdsourced Clinical Registry Studies.

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.

Development of a crowdsourcing- and gamification-based mobile application to collect epidemiological information and promote healthy lifestyles in Mexico.

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

Development and validation of VaxConcerns: A taxonomy of vaccine concerns and misinformation with Crowdsource-Viability.

We present VaxConcerns, a taxonomy for vaccine concerns and misinformation. VaxConcerns is an easy-to-teach taxonomy of concerns and misinformation commonly found among online anti-vaccination media and is evaluated to produce high-quality data annotations among crowdsource workers, opening the potential adoption of the framework far beyond just academic or medical communities. The taxonomy shows high agreement among experts and outperforms existing taxonomies for vaccine concerns and misinformation when presented to non-expert users. Our proof-of-concept study on the changes in anti-vaccination content during the COVID-19 pandemic indicate impactful future use cases, such as longitudinal studies of the shift in vaccine concerns over time.

The HI V O pen Call on I nformed C onsent and E thics in Research (VOICE) for Adolescents and Young Adults: A Digital Crowdsourcing Open Call in Low- and Middle-Income Countries.

BACKGROUND: Many adolescents and young adults (AYAs; 10-24 years old) are excluded from HIV research because of social, ethical, and legal challenges with informed consent, resulting in limited AYA-focused data. We use a participatory approach to identify strategies for improving AYA consent processes in HIV research in low- and middle-income countries (LMICs). METHODS: We conducted a digital crowdsourcing open call for ideas to improve AYA consent to HIV research in LMICs. Crowdsourcing involves engaging a group of people in problem-solving, then sharing emergent solutions. Submissions were evaluated by 3 independent judges using predefined criteria, with exceptional strategies receiving prizes. Demographic data were collected, and textual data were qualitatively analyzed for emergent themes in barriers and facilitators for improving AYA consent in HIV research, guided by a socioecological model. RESULTS: We received 110 strategies total; 65 were eligible for evaluation, 25 of which were identified as finalists. Fifty-eight participants from 10 LMICs submitted the 65 eligible submissions, of which 30 (52%) were 18 to 24 years old. Thematic analysis identified 10 barriers to AYA consent, including HIV stigma, limited education, and legal/regulatory barriers. Strategies for improving AYA consent processes revealed 7 potential facilitators: enhancing AYA engagement in research, involving parents/guardians, improving education/awareness, improving institutional practices/policy, making research participation more AYA-friendly, enhancing engagement of other key communities of interest, and empowering AYA. CONCLUSIONS: Diverse communities of interest in LMICs developed compelling strategies to enhance informed consent that may improve AYA inclusion in HIV research. These data will be used to develop practical guidance on improving AYA consent processes.

A critical analysis of trans-visibility through online medical crowdfunding.

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

A smartphone-based crowd-sourced real-time surveillance platform (apple snail inspector) for the invasive snails: a design and development study.

BACKGROUND: The large amphibious freshwater apple snail is an important invasive species in China, but there is currently no method available for their surveillance. The development and popularization of smartphones provide a new platform for research on surveillance technologies for the early detection and effective control of invasive species. METHODS: The ASI surveillance system was developed based on the infrastructure of the WeChat platform and Amap. The user can directly enter the game interface through the WeChat port on their mobile phone, and the system automatically obtains their location. The user can then report the location of apple snails. The administrator can audit the reported information, and all information can be exported to Microsoft Excel version 2016 for analysis. The map was generated by ArcGIS 10.2 and was used to characterize the spatial and temporal distribution of apple snails in Jiangsu Province. RESULTS: The architecture of ASI consists of three parts: a mobile terminal, a server terminal and a desktop terminal. We published more than 10 tweets on the official WeChat account of the system to announce it to the public, and a total of 207 users in 2020 and 2021 correctly reported sightings of apple snails. We identified 550 apple snails breeding sites in 2020 and 2021, featuring ponds (81%), parks (17%) and farmland (2%). In addition, most of the locations contained snail eggs, and the reporting times mainly occurred between May and September. CONCLUSIONS: The ASI is an effective surveillance system that can be used to identify the breeding locations of apple snails and provides the basis of prevention and control for its dispersal. Its successful development and operation provide new potential avenues for surveillance of other public health issues.

Defining Preferred Esthetics of the Ideal Phallus via Crowdsource Survey.

OBJECTIVE: To describe phalloplasty subunits and determine the preferred crowdsourced esthetics. Esthetic ideals are often used to guide reconstruction, and there has been an increase in the number of gender-affirming surgeries and reconstructive phalloplasties performed. However, there is a paucity of literature describing ideal phalloplasty esthetics. METHODS: Phallus esthetic subunits were defined, and a split testing-based survey was used. Subjects were solicited via Craigslist, Amazon Mechanical Turk, and Reddit and distributed among health care co-workers. Computer-generated images with variable ratios of glans, corona, and shaft were provided and respondents were asked to select the most esthetically pleasing photo. Demographic information was gathered. Univariate and multivariate regression were performed. RESULTS: A total of 1029 people responded to the survey request and 909 people (88.3%) completed the entire survey. There were 440 respondents who self-identified as male, 334 female, 92 transgender male, and 25 transgender female. The health care field was the profession for 55.4%. Health care providers had 65.3% higher odds of preferring the longer shaft length-to-width ratio, 30.3% less odds of preferring a bilateral taper of the glans, and 48.4% less odds of preferring an angulated shaft compared to non-health care providers (P = .006, P = .021, P <.001, respectively). When compared to males, transgender females were more than 13 times likely to prefer an angulated glans corona junction (P = .008). CONCLUSION: The ideal phallic esthetic varies by individual, and there were statistically significant preferences across age, education, health care status, gender, and sexual orientation. This study can serve as a guide on phalloplasties for patients and gender-affirming surgeons.

Task design for crowdsourced glioma cell annotation in microscopy images.

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

Estimating the smallest worthwhile difference of antidepressants: a cross-sectional survey.

BACKGROUND: Approximately 30% of patients experience substantial improvement in depression after 2 months without treatment, and 45% with antidepressants. The smallest worthwhile difference (SWD) refers to an intervention's smallest beneficial effect over a comparison patients deem worthwhile given treatment burdens (harms, expenses and inconveniences), but is undetermined for antidepressants. OBJECTIVE: Estimating the SWD of commonly prescribed antidepressants for depression compared to no treatment. METHODS: The SWD was estimated as a patient-required difference in response rates between antidepressants and no treatment after 2 months. An online cross-sectional survey using Prolific, MQ Mental Health and Amazon Mechanical Turk crowdsourcing services in the UK and USA between October 2022 and January 2023 garnered participants (N=935) that were a mean age of 44.1 (SD=13.9) and 66% women (n=617). FINDINGS: Of 935 participants, 124 reported moderate-to-severe depressive symptoms but were not in treatment, 390 were in treatment and 495 reported absent-to-mild symptoms with or without treatment experiences. The median SWD was a 20% (IQR=10-30%) difference in response rates for people with moderate-to-severe depressive symptoms, not in treatment, and willing to consider antidepressants, and 25% (IQR=10-35%) for the full sample. CONCLUSIONS: Our observed SWDs mean that the current 15% antidepressant benefit over no treatment was sufficient for one in three people to accept antidepressants given the burdens, but two in three expected greater treatment benefits. IMPLICATIONS: While a minority may be satisfied with the best currently available antidepressants, more effective and/or less burdensome medications are needed, with more attention given to patient perspectives.

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.

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

Data Liberation and Crowdsourcing in Medical Research: The Intersection of Collective and Artificial Intelligence.

In spite of an exponential increase in the volume of medical data produced globally, much of these data are inaccessible to those who might best use them to develop improved health care solutions through the application of advanced analytics such as artificial intelligence. Data liberation and crowdsourcing represent two distinct but interrelated approaches to bridging existing data silos and accelerating the pace of innovation internationally. In this article, we examine these concepts in the context of medical artificial intelligence research, summarizing their potential benefits, identifying potential pitfalls, and ultimately making a case for their expanded use going forward. A practical example of a crowdsourced competition using an international medical imaging dataset is provided. Keywords: Artificial Intelligence, Data Liberation, Crowdsourcing © RSNA, 2023.

Health research mentorship in low-income and middle-income countries: a global qualitative evidence synthesis of data from a crowdsourcing open call and scoping review.

INTRODUCTION: Research mentorship is critical for advancing science, but there are few practical strategies for cultivating mentorship in health research resource-limited settings. WHO/TDR Global commissioned a group to develop a practical guide on research mentorship. This global qualitative evidence synthesis included data from a crowdsourcing open call and scoping review to identify and propose strategies to enhance research mentorship in low/middle-income country (LMIC) institutions. METHODS: The crowdsourcing open call used methods recommended by WHO/TDR and solicited descriptions of strategies to enhance research mentorship in LMICs. The scoping review used the Cochrane Handbook and predefined the approach in a protocol. We extracted studies focused on enhancing health research mentorship in LMICs. Textual data describing research mentorship strategies from the open call and studies from the scoping review were coded into themes. The quality of evidence supporting themes was assessed using the Confidence in the Evidence from Reviews of Qualitative research approach. RESULTS: The open call solicited 46 practical strategies and the scoping review identified 77 studies. We identified the following strategies to enhance research mentorship: recognising mentorship as an institutional responsibility that should be provided and expected from all team members (8 strategies, 15 studies; moderate confidence); leveraging existing research and training resources to enhance research mentorship (15 strategies, 49 studies; moderate confidence); digital tools to match mentors and mentees and sustain mentorship relations over time (14 strategies, 11 studies; low confidence); nurturing a culture of generosity so that people who receive mentorship then become mentors to others (7 strategies, 7 studies; low confidence); peer mentorship defined as informal and formal support from one researcher to another who is at a similar career stage (16 strategies, 12 studies; low confidence). INTERPRETATION: Research mentorship is a collective institutional responsibility, and it can be strengthened in resource-limited institutions by leveraging already existing resources. The evidence from the crowdsourcing open call and scoping review informed a WHO/TDR practical guide. There is a need for more formal research mentorship programmes in LMIC institutions.

LGBTQ+ inequity in crowdfunding cancer costs: The influence of online reach and LGBTQ+ state policy.

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.

Addressing Current Deficits in Patient Education Materials Through Crowdsourcing.

BACKGROUND: Patient education materials are commonly reported to be difficult to understand. OBJECTIVES: We aimed to use crowdsourcing to improve patient education materials at our institution. METHODS: This was a department-wide quality improvement project to increase organizational health literacy. There are 6 phases of this pilot study: (1) evaluating preexisting patient education materials, (2) evaluating online patient education materials at the society (the American Society of Plastic Surgeon) and government level (Medline Plus), (3) redesigning our patient education material and reevaluating the education material, (4) crowdsourcing to evaluate understandability of the new patient education material, (5) data analysis, and (6) incorporating crowdsourcing suggestions to the patient education material. RESULTS: Breast-related patient education materials are not easy to read at the institution level, the society level, and the government level. Our new implant-based breast reconstruction patient education material is easy to read as demonstrated by the crowdsourcing evaluation. More than 90% of the participants reported our material is "very easy to understand" or "easy to understand." The crowdsourcing process took 1.5 days, with 700 workers responding to the survey. The total cost was $9. After incorporating participants' feedback into the finalized material, the readability of the material is at the recommended reading level. The material also had the recommended length (between 400 and 800 words). DISCUSSION: Our study demonstrated a pathway for clinicians to efficiently obtain a large amount of feedback to improve patient education materials. Crowdsourcing is an effective tool to improve organizational health literacy.

Crowdsourced human-based computational approach for tagging peripheral blood smear sample images from Sickle Cell Disease patients using non-expert users.

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.

In the Age of Social Media, How Does the Public Choose a Plastic Surgeon? A Crowdsourcing Analysis of Major Deciding Factors.

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

Measurement invariance of a Posttraumatic Stress Disorder symptoms measure (PCL-5) in college student and Amazon's mechanical TURK samples.

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

Examining the validity of the addictions neuroclinical assessment domains in a crowdsourced sample of adults with current alcohol use.

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