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.

Patients' Experiences With Therapeutic Approaches for Post-COVID Syndrome: Results of a Crowdsourced Research Survey.

Some patients develop multiple protracted sequelae after infection with SARS-CoV-2, collectively known as post-COVID syndrome or long COVID. To date, there is no evidence showing benefit of specific therapies for this condition, and patients likely resort to self-initiated therapies. We aimed to obtain information about therapies used by and needs of this population via inductive crowdsourcing research. Patients completed an online questionnaire about their symptoms and experiences with therapeutic approaches. Responses of 499 participants suggested few approaches (eg, mind-body medicine, respiratory therapy) had positive effects and showed a great need for patient-centered communication (eg, more recognition of this syndrome). Our findings can help design clinical studies and underscore the importance of the holistic approach to care provided by family medicine.

Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening.

Mass community testing is a critical means for monitoring the spread of the COVID-19 pandemic. Polymerase chain reaction (PCR) is the gold standard for detecting the causative coronavirus 2 (SARS-CoV-2) but the test is invasive, test centers may not be readily available, and the wait for laboratory results can take several days. Various machine learning based alternatives to PCR screening for SARS-CoV-2 have been proposed, including cough sound analysis. Cough classification models appear to be a robust means to predict infective status, but collecting reliable PCR confirmed data for their development is challenging and recent work using unverified crowdsourced data is seen as a viable alternative. In this study, we report experiments that assess cough classification models trained (i) using data from PCR-confirmed COVID subjects and (ii) using data of individuals self-reporting their infective status. We compare performance using PCR-confirmed data. Models trained on PCR-confirmed data perform better than those trained on patient-reported data. Models using PCR-confirmed data also exploit more stable predictive features and converge faster. Crowd-sourced cough data is less reliable than PCR-confirmed data for developing predictive models for COVID-19, and raises concerns about the utility of patient reported outcome data in developing other clinical predictive models when better gold-standard data are available.

The future of digital donation crowdfunding.

Amidst the Coronavirus crisis, many fundraising projects have emerged to relieve financial burdens resulting from social distancing policies. Crowdfunding is a way to raise money to fund a business, project or charity, through either social media or other online platforms to reach hundreds of potential sponsors. We developed guidelines for effective donation-based crowdfunding through online platforms. Using Futures Research (FR) technique, we conducted our analyses in 3 phases. In Phase 1, we reviewed relevant literature and conducted in-depth interviews of related parties. In Phase 2, we interviewed experts using Ethnographic Futures Research (EFR) technique. In Phase 3, we visualized the future using the principles of Futures Wheel, Cross-impact Matrix and Scenarios. Based on our findings, effective donation-based crowdfunding platforms should adopt Blockchain technology for transparency and accountability, and incentivize donations to keep backers loyal. Founders should be required to obtain fundraising licenses from relevant regulators. Finally, laws and regulations that protect platform users should be standardized internationally. Our proposed guidelines hope to improve the quality and transparency of future fundraising activities.

Crowdsourcing homemade facemasks: 772 U.S. health facilities' responses to personal protective equipment shortages in the first half of 2020.

PURPOSE: We examined 772 U.S. health facilities' responses to Personal Protective Equipment (PPE) shortages in the first half of 2020, as they crowdsourced face coverings from volunteer makers to be used as respiratory protection during crisis surge capacity. The purpose was to examine facemask specification requests from health facilities and develop a framework for crowdsourcing last resort PPE. DESIGN/METHODOLOGY/APPROACH: Homemade facemask donation requests from health facilities in 47 states systematically recorded in a public database maintained by public health graduate students at a major U.S. university were analysed. Open coding was used to content analyse facemask types and specifications, intended uses, delivery logistics and donation management strategies. FINDINGS: Our analysis revealed information gaps: Science-based information was scarce in 2020, leading to improvised specifications for facemask materials and designs. It also revealed the emergence of a crowdsourcing structure: Task specifications for volunteer facemasks makers, delivery logistics, and practical management of donations within the pandemic context. In anticipation of future pandemics and localised PPE shortages, we build on this empirical evidence to propose a framework for crowdsourcing science-informed facemasks from volunteers. Categorised within (a) logistics and workflow management, (b) task specifications and management, and (c) practical management of contributions functional areas, the framework outlines the required tasks and specifications for crowdsourcing. ORIGINALITY: A novel empirically derived framework for crowdsourcing homemade facemasks is proposed, based on empirical analysis and crowdsourcing system design strategies. Our findings and the framework may be used for refining crisis capacity guidelines, as part of strategic planning and preparation for future pandemics that disrupt supply chains and cause shortages in protective equipment.

COVID-19 cluster size and transmission rates in schools from crowdsourced case reports.

The role of schools in the spread of SARS-CoV-2 is controversial, with some claiming they are an important driver of the pandemic and others arguing that transmission in schools is negligible. School cluster reports that have been collected in various jurisdictions are a source of data about transmission in schools. These reports consist of the name of a school, a date, and the number of students known to be infected. We provide a simple model for the frequency and size of clusters in this data, based on random arrivals of index cases at schools who then infect their classmates with a highly variable rate, fitting the overdispersion evident in the data. We fit our model to reports from four Canadian provinces, providing estimates of mean and dispersion for cluster size, as well as the distribution of the instantaneous transmission parameter ß, whilst factoring in imperfect ascertainment. According to our model with parameters estimated from the data, in all four provinces (i) more than 65% of non-index cases occur in the 20% largest clusters, and (ii) reducing instantaneous transmission rate and the number of contacts a student has at any given time are effective in reducing the total number of cases, whereas strict bubbling (keeping contacts consistent over time) does not contribute much to reduce cluster sizes. We predict strict bubbling to be more valuable in scenarios with substantially higher transmission rates.

During the COVID-19 pandemic, public health officials promoted social distancing as a way to reduce SARS-CoV-2 transmission. The goal of social distancing is to reduce the number, proximity, and duration of face-to-face interactions between people. To achieve this, people shifted many activities online or canceled events outright. In education, some schools closed and shifted to online learning, while others continued classes in person with safety precautions. Better information about SARS-CoV-2 transmission in schools could help public health officials to make decisions of what activities to keep in person and when to suspend classes. If safety measures lower transmission in schools considerably, then closing schools may not be worth online education's social, educational, and economic costs. However, if transmission of SARS-CoV-2 in schools remains high despite measures, closing schools may be essential, despite the costs. Tupper et al. used data about COVID-19 cases in children attending in-person school in four Canadian provinces between 2020 and 2021 to fit a computer model of school transmission. On average, their analysis shows that one infected person in a school leads to between two and three further cases. Most of the time, no more students are infected, indicating that normally infection clusters are small; and only rarely does one infected person set off a large outbreak. The model also showed that measures to reduce transmission, like masking or small class sizes, were more effective than interventions such as keeping students with the same cohort all day (bubbling). Tupper et al. caution that their findings apply to the variants of SARS-CoV-2 circulating in Canada during the 2020-2021 school year, and may not apply to newer, highly transmissible strains like Omicron. However, the model could always be adapted to assess school or workplace transmission of more recent strains of SARS-CoV-2, and more generally of other diseases. Thus, Tupper et al. provide a new approach to estimating the rate of disease transmission and comparing the impact of different prevention strategies.

Evaluating and volunteering for crowdsourced interventions: Cross-sectional data on COVID-19 safety from a University Survey.

Despite many innovative ideas generated in response to COVID-19, few studies have examined community preferences for these ideas. Our study aimed to determine university community members' preferences for three novel ideas identified through a crowdsourcing open call at the University of North Carolina (UNC) for making campus safer in the pandemic, as compared to existing (i.e. pre-COVID-19) resources. An online survey was conducted from March 30, 2021 -May 6, 2021. Survey participants included UNC students, staff, faculty, and others. The online survey was distributed using UNC's mass email listserv and research directory, departmental listservs, and student text groups. Collected data included participant demographics, COVID-19 prevention behaviors, preferences for finalist ideas vs. existing resources in three domains (graduate student supports, campus tours, and online learning), and interest in volunteering with finalist teams. In total 437 survey responses were received from 228 (52%) staff, 119 (27%) students, 78 (18%) faculty, and 12 (3%) others. Most participants were older than age 30 years (309; 71%), women (332, 78%), and white (363, 83.1%). Five participants (1%) were gender minorities, 66 (15%) identified as racial/ethnic minorities, and 46 (10%) had a disability. Most participants preferred the finalist idea for a virtual campus tour of UNC's lesser-known history compared to the existing campus tour (52.2% vs. 16.0%). For graduate student supports, 41.4% of participants indicated no preference between the finalist idea and existing supports; for online learning resources, the existing resource was preferred compared to the finalist idea (41.6% vs. 30.4%). Most participants agreed that finalists' ideas would have a positive impact on campus safety during COVID-19 (81.2%, 79.6%, and 79.2% for finalist ideas 1, 2 and 3 respectively). 61 (14.1%) participants indicated interest in volunteering with finalist teams. Together these findings contribute to the development and implementation of community-engaged crowdsourced campus safety interventions during COVID-19.

UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio.

We suggested a unified system with core components of data augmentation, ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and uncertainty estimation to quickly and consistently detect COVID-19 using acoustic evidence. To increase the model's capacity to identify a minority class, data augmentation and cost-sensitive loss are incorporated (infected samples). In the COVID-19 detection challenge, ImageNet-pretrained ResNet-50 has been found to be effective. The unified framework also integrates deep ensemble learning and uncertainty estimation to integrate predictions from various base classifiers for generalisation and reliability. We ran a series of tests using the DiCOVA2021 challenge dataset to assess the efficacy of our proposed method, and the results show that our method has an AUC-ROC of 85.43 percent, making it a promising method for COVID-19 detection. The unified framework also demonstrates that audio may be used to quickly diagnose different respiratory disorders.

COVID-19 Misinformation and Social Network Crowdfunding: Cross-sectional Study of Alternative Treatments and Antivaccine Mandates.

BACKGROUND: Crowdfunding is increasingly used to offset the financial burdens of illness and health care. In the era of the COVID-19 pandemic and associated infodemic, the role of crowdfunding to support controversial COVID-19 stances is unknown. OBJECTIVE: We sought to examine COVID-19-related crowdfunding focusing on the funding of alternative treatments not endorsed by major medical entities, including campaigns with an explicit antivaccine, antimask, or antihealth care stances. METHODS: We performed a cross-sectional analysis of GoFundMe campaigns for individuals requesting donations for COVID-19 relief. Campaigns were identified by key word and manual review to categorize campaigns into "Traditional treatments," "Alternative treatments," "Business-related," "Mandate," "First Response," and "General." For each campaign, we extracted basic narrative, engagement, and financial variables. Among those that were manually reviewed, the additional variables of "mandate type," "mandate stance," and presence of COVID-19 misinformation within the campaign narrative were also included. COVID-19 misinformation was defined as "false or misleading statements," where cited evidence could be provided to refute the claim. Descriptive statistics were used to characterize the study cohort. RESULTS: A total of 30,368 campaigns met the criteria for final analysis. After manual review, we identified 53 campaigns (0.17%) seeking funding for alternative medical treatment for COVID-19, including popularized treatments such as ivermectin (n=14, 26%), hydroxychloroquine (n=6, 11%), and vitamin D (n=4, 7.5%). Moreover, 23 (43%) of the 53 campaigns seeking support for alternative treatments contained COVID-19 misinformation. There were 80 campaigns that opposed mandating masks or vaccination, 48 (60%) of which contained COVID-19 misinformation. Alternative treatment campaigns had a lower median amount raised (US $1135) compared to traditional (US $2828) treatments (P<.001) and a lower median percentile of target achieved (11.9% vs 31.1%; P=.003). Campaigns for alternative treatments raised substantially lower amounts (US $115,000 vs US $52,715,000, respectively) and lower proportions of fundraising goals (2.1% vs 12.5%) for alternative versus conventional campaigns. The median goal for campaigns was significantly higher (US $25,000 vs US $10,000) for campaigns opposing mask or vaccine mandates relative to those in support of upholding mandates (P=.04). Campaigns seeking funding to lift mandates on health care workers reached US $622 (0.15%) out of a US $410,000 goal. CONCLUSIONS: A small minority of web-based crowdfunding campaigns for COVID-19 were directed at unproven COVID-19 treatments and support for campaigns aimed against masking or vaccine mandates. Approximately half (71/133, 53%) of these campaigns contained verifiably false or misleading information and had limited fundraising success. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1001/jamainternmed.2019.3330.

Doing 'our bit': Solidarity, inequality, and COVID-19 crowdfunding for the UK National Health Service.

The expanding phenomenon of crowdfunding for healthcare creates novel potential roles for members of the public as fundraisers and donors of particular forms of provision. While sometimes interpreted as an empowering phenomenon (Gonzales et al., 2018), or a potentially useful communication of unmet needs (Saleh et al., 2021), scholars have predominantly been critical of the way in which crowdfunding for healthcare normalises unmet needs and exacerbates entrenched inequalities (Berliner and Kenworthy, 2017; Igra et al., 2021; Paulus and Roberts, 2018). We report a thematic analysis of the text of 945 fundraising appeals created on JustGiving and GoFundMe in the first months of the COVID-19 pandemic, where the recipient was NHS Charities Together's dramatically successful COVID-19 Urgent Appeal. Unlike in existing accounts of individual healthcare crowdfunding, we identify the relative absence of both coherent problem definition and of a fundable solution within the pages. Instead, appeals are dominated by themes of solidarity and duty during the UK's 'hard' lockdown of 2020. A national appeal reduces the risks of crowdfunding exacerbating existing health inequalities, but we argue that two kinds of non-financial consequences of collective crowdfunding require further exploration. Specifically, we need to better understand how expanded practices of fundraising co-exist with commitment to dutiful, means-based funding of healthcare via taxation. We must also attend to how celebration of the NHS as a national achievement, might squeeze spaces for critique and challenge. Analyses of crowdfunding need to explore both financial and non-financial aspects of practices within different health system and historical contexts.

Crowdfunding narratives and the valuation of vaccines for COVID-19.

Social media spreads information about vaccines and can be used to better understand public attitudes about them. Using American crowdfunding campaigns that mentioned COVID-19 vaccines from January 2020 to March 2021, this paper investigates public attitudes towards vaccines, specifically the perceived role vaccines could (or couldn't) play in ending the pandemic. We identified 776 crowdfunding campaigns and coded each for their aims and whether they valued vaccines as returning their community to a pre-pandemic state (utopian), helping some but not all people (cautious), and doubtful about the likely positive impacts of vaccines (skeptical). Cautious and skeptical valuations increased over time whereas utopian views declined. This paper uniquely situates attitudes toward COVID-19 vaccines in the context of financial need (as characterized by the campaigners). It offers insight into the "vaccine class gap" in America and demonstrates the usefulness of crowdfunding campaigns for assessing public views on vaccines.

Engaging Remote Aboriginal Communities in COVID-19 Public Health Messaging via Crowdsourcing.

Health comunication is a critical component of pandemic mitigation, but mainstream prevention messaging often lacks social, cultural and linguistic relevance to vulnerable populations. This community case study presents a novel, highly participatory pandemic prevention communication campaign that engaged individuals in remote Aboriginal communities of the Northern Territory of Australia directly in prevention messaging via crowdsourcing, and distributed videos to remote area post-codes via targeted Facebook advertising. Facebook metrics, administrative campaign data and national statistics are used to assess campaign reach and engagement. The case study discusses lessons learned from the campaign, including how seeking unscripted COVID-19 prevention video messaging can support community ownership of pandemic messaging, rapid content generation, and a high level of Facebook user engagement. It also discusses the effectiveness of targeting remote area post-codes via Facebook advertising both to reach the target audience, and to support quality improvement assessments to inform health communication decision-making in a low resource setting.

Crowdsourcing methods in addiction science: Emerging research and best practices.

Crowdsourcing platforms such as Amazon Mechanical Turk, Prolific, and Qualtrics Panels have become a dominant form of sampling in recent years. Crowdsourcing enables researchers to effectively and efficiently sample research participants with greater geographic variability, access to hard-to-reach populations, and reduced costs. These methods have been increasingly used across varied areas of psychological science and essential for research during the COVID-19 pandemic due to their facilitation of remote research. Recent work documents methods for improving data quality, emerging crowdsourcing platforms, and how crowdsourcing data fit within broader research programs. Addiction scientists will benefit from the adoption of best practice guidelines in crowdsourcing as well as developing novel approaches, venues, and applications to advance the field. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, China.

The lack of physical activity has become a rigorous challenge for many countries, and the relationship between physical activity and the built environment has become a hot research topic in recent decades. This study uses the Strava Heatmap (novel crowdsourced data) to extract the distribution of cycling and running tracks in central Chengdu in December 2021 (during the COVID-19 pandemic) and develops spatial regression models for numerous 500 × 500 m grids (N = 2,788) to assess the impacts of the built environment on the cycling and running intensity indices. The findings are summarized as follows. First, land-use mix has insignificant effects on the physical activity of residents, which largely contrasts with the evidence gathered from previous studies. Second, road density, water area, green space area, number of stadiums, and number of enterprises significantly facilitate cycling and running. Third, river line length and the light index have positive associations with running but not with cycling. Fourth, housing price is positively correlated with cycling and running. Fifth, schools seem to discourage these two types of physical activities during the COVID-19 pandemic. This study provides practical implications (e.g., green space planning and public space management) for urban planners, practitioners, and policymakers.

Crowdsourcing the identification of studies for COVID-19-related Cochrane Rapid Reviews.

BACKGROUND: Utilisation of crowdsourcing within evidence synthesis has increased over the last decade. Crowdsourcing platform Cochrane Crowd has engaged a global community of 22,000 people from 170 countries. The COVID-19 pandemic presented an opportunity to engage the community and keep up with the exponential output of COVID-19 research. AIMS: To test whether a crowd could accurately assess study eligibility for reviews under time constraints. OUTCOME MEASURES: time taken to complete each task, time to produce required training modules, crowd sensitivity, specificity and crowd consensus. METHODS: We created four crowd tasks, corresponding to four Cochrane COVID-19 Rapid Reviews. The search results of each were uploaded and an interactive training module was developed for each task. Contributors who had participated in another COVID-19 task were invited to participate. Each task was live for 48-h. The final inclusion and exclusion decisions made by the core author team were used as the reference standard. RESULTS: Across all four reviews 14,299 records were screened by 101 crowd contributors. The crowd completed each screening task within 48-h for three reviews and in 52 h for one. Sensitivity ranged from 94% to 100%. Four studies, out of a total of 109, were incorrectly rejected by the crowd. However, their absence ultimately would not have altered the conclusions of the reviews. Crowd consensus ranged from 71% to 92% across the four reviews. CONCLUSION: Crowdsourcing can play a valuable role in study identification and offers willing contributors the opportunity to help identify COVID-19 research for rapid evidence syntheses.

Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review.

Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.

Priorities for Safer In-Person School for Children With Medical Complexity During COVID-19.

OBJECTIVES: To establish statewide consensus priorities for safer in-person school for children with medical complexity (CMC) during the coronavirus disease 2019 (COVID-19) pandemic using a rapid, replicable, and transparent priority-setting method. METHODS: We adapted the Child Health and Nutrition Research Initiative Method, which allows for crowdsourcing ideas from diverse stakeholders and engages technical experts in prioritizing these ideas using predefined scoring criteria. Crowdsourcing surveys solicited ideas from CMC families, school staff, clinicians and administrators through statewide distribution groups/listservs using the prompt: "It is safe for children with complex health issues and those around them (families, teachers, classmates, etc.) to go to school in-person during the COVID-19 pandemic if/when…" Ideas were aggregated and synthesized into a unique list of candidate priorities. Thirty-four experts then scored each candidate priority against 5 criteria (equity, impact on COVID-19, practicality, sustainability, and cost) using a 5-point Likert scale. Scores were weighted and predefined thresholds applied to identify consensus priorities. RESULTS: From May to June 2021, 460 stakeholders contributed 1166 ideas resulting in 87 candidate priorities. After applying weighted expert scores, 10 consensus CMC-specific priorities exceeded predetermined thresholds. These priorities centered on integrating COVID-19 safety and respiratory action planning into individualized education plans, educating school communities about CMC's unique COVID-19 risks, using medical equipment safely, maintaining curricular flexibility, ensuring masking and vaccination, assigning seats during transportation, and availability of testing and medical staff at school. CONCLUSIONS: Priorities for CMC, identified by statewide stakeholders, complement and extend existing recommendations. These priorities can guide implementation efforts to support safer in-person education for CMC.

Using social media to crowdsource collection of urine samples during a national pandemic.

The COVID-19 pandemic and subsequent lockdown had a substantial impact on normal research operations. Researchers needed to adapt their methods to engage at-home participants. One method is crowdsourcing, in which researchers use social media to recruit participants, gather data, and collect samples. We utilized this method to develop a diagnostic test for Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS). Participants were recruited via posts on popular social-media platforms, and enrolled via a website. Participants received and returned a mail kit containing bladder symptom surveys and a urine sample cup containing room-temperature preservative. Using this method, we collected 1254 IC/BPS and control samples in 3 months from all 50 United States. Our data demonstrate that crowdsourcing is a viable alternative to traditional research, with the ability to reach a broad patient population rapidly. Crowdsourcing is a powerful tool for at-home participation in research, particularly during the lockdown caused by the COVID-19 pandemic.

Early-stage spatial disease surveillance of novel SARS-CoV-2 variants of concern in Germany with crowdsourced data.

The emergence and rapid spread of novel variants of concern (VOC) of the coronavirus 2 constitute a major challenge for spatial disease surveillance. We explore the possibility to use close to real-time crowdsourced data on reported VOC cases (mainly the Alpha variant) at the local area level in Germany. The aim is to use these data for early-stage estimates of the statistical association between VOC reporting and the overall COVID-19 epidemiological development. For the first weeks in 2021 after international importation of VOC to Germany, our findings point to significant increases of up to 35-40% in the 7-day incidence rate and the hospitalization rate in regions with confirmed VOC cases compared to those without such cases. This is in line with simultaneously produced international evidence. We evaluate the sensitivity of our estimates to sampling errors associated with the collection of crowdsourced data. Overall, we find no statistical evidence for an over- or underestimation of effects once we account for differences in data representativeness at the regional level. This points to the potential use of crowdsourced data for spatial disease surveillance, local outbreak monitoring and public health decisions if no other data on new virus developments are available.