Protocol for the crowdsourced image-based morbidity hotspot surveillance for neglected tropical diseases (CIMS-NTDs).

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.

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.

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.

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.

Using a Delphi Method Approach to Select Theoretical Underpinnings of Crowdsourcing and Rank Their Application to a Crowdsourcing App.

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.

Interactive crowdsourcing to fact-check politicians.

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).

Enhancing Community Participation in Dengue Control Through Digital Crowdsourcing: An Analysis of a World Mosquito Program Digital Open Call in Sri Lanka.

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.

Feasibility assessment of crowdsourcing slogans for promoting household waste segregation in India: a cross-sectional study.

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.

Digital, Crowdsourced, Multilevel Intervention to Promote HIV Testing Among Men Who Have Sex With Men: Cluster Randomized Controlled Trial.

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.

Using Crowdsourced Food Image Data for Assessing Restaurant Nutrition Environment: A Validation Study.

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.

Inducing collective action intentions for healthcare reform through medical crowdfunding framing.

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.

Assessing Interventions on Crowdsourcing Platforms to Nudge Patients for Engagement Behaviors in Primary Care Settings: Randomized Controlled Trial.

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.

Measuring psychiatric symptoms online: A systematic review of the use of inventories on Amazon Mechanical Turk (mTurk).

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.

A Model for Cognitive Personalization of Microtask Design.

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.

Exploring Novel Innovation Strategies to Close a Technology Gap in Neurosurgery: HORAO Crowdsourcing Campaign.

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.

Thematic and Demographic Analysis of Crowdsourcing Practices for Plastic Surgery.

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.

A Virtual Reading Center Model Using Crowdsourcing to Grade Photographs for Trachoma: Validation Study.

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.

The Success of Cancer Crowdfunding Campaigns: Project and Text Analysis.

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, ß=-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, ß=-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.

Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing.

Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing.

Applying and Testing a Crowdsourcing Platform to Support Home-Based Simulation.

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.