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.
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.
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).
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.

To evaluate an approach using automation and crowdsourcing to identify and classify randomized controlled trials (RCTs) for rheumatoid arthritis (RA) in a living systematic review (LSR).
Records from a database search for RCTs in RA were screened first by machine learning and Cochrane Crowd to exclude non-RCTs, then by trainee reviewers using a Population, Intervention, Comparison, and Outcome (PICO) annotator platform to assess eligibility and classify the trial to the appropriate review. Disagreements were resolved by experts using a custom online tool. We evaluated the efficiency gains, sensitivity, accuracy, and interrater agreement (kappa scores) between reviewers.
From 42,452 records, machine learning and Cochrane Crowd excluded 28,777 (68%), trainee reviewers excluded 4,529 (11%), and experts excluded 7,200 (17%). The 1,946 records eligible for our LSR represented 220 RCTs and included 148/149 (99.3%) of known eligible trials from prior reviews. Although excluded from our LSRs, 6,420 records were classified as other RCTs in RA to inform future reviews. False negative rates among trainees were highest for the RCT domain (12%), although only 1.1% of these were for the primary record. Kappa scores for two reviewers ranged from moderate to substantial agreement (0.40-0.69).
A screening approach combining machine learning, crowdsourcing, and trainee participation substantially reduced the screening burden for expert reviewers and was highly sensitive.

Globally, the adoption and implementation of policies to improve the healthiness of food environments and prevent population weight gain have been inadequate. This is partly because of the complexity associated with monitoring dynamic food environments. Crowdsourcing is a citizen science approach that can increase the extent and nature of food environment data collection by engaging citizens as sensors or volunteered computing experts. There has been no literature synthesis to guide the application of crowdsourcing to food environment monitoring. We systematically conducted a scoping review to address this gap. Forty-two articles met our eligibility criteria. Photovoice techniques were the most employed methodological approaches (n = 25 studies), commonly used to understand overall access to healthy food. A small number of studies made purpose-built apps to collect price or nutritional composition data and were scaled to receive large amounts of data points. Twenty-nine studies crowdsourced food environment data by engaging priority populations (e.g., households receiving low incomes). There is growing potential to develop scalable crowdsourcing platforms to understand food environments through the eyes of everyday people. Such crowdsourced data may improve public and policy engagement with equitable food policy actions.

BACKGROUND: It has been hypothesized that low access to healthy and nutritious food increases health disparities. Low-accessibility areas, called food deserts, are particularly commonplace in lower-income neighborhoods. The metrics for measuring the food environment\'s health, called food desert indices, are primarily based on decadal census data, limiting their frequency and geographical resolution to that of the census. We aimed to create a food desert index with finer geographic resolution than census data and better responsiveness to environmental changes.
METHODS: We augmented decadal census data with real-time data from platforms such as Yelp and Google Maps and crowd-sourced answers to questionnaires by the Amazon Mechanical Turks to create a real-time, context-aware, and geographically refined food desert index. Finally, we used this refined index in a concept application that suggests alternative routes with similar ETAs between a source and destination in the Atlanta metropolitan area as an intervention to expose a traveler to better food environments.
RESULTS: We made 139,000 pull requests to Yelp, analyzing 15,000 unique food retailers in the metro Atlanta area. In addition, we performed 248,000 walking and driving route analyses on these retailers using Google Maps\' API. As a result, we discovered that the metro Atlanta food environment creates a strong bias towards eating out rather than preparing a meal at home when access to vehicles is limited. Contrary to the food desert index that we started with, which changed values only at neighborhood boundaries, the food desert index that we built on top of it captured the changing exposure of a subject as they walked or drove through the city. This model was also sensitive to the changes in the environment that occurred after the census data was collected.
CONCLUSIONS: Research on the environmental components of health disparities is flourishing. New machine learning models have the potential to augment various information sources and create fine-tuned models of the environment. This opens the way to better understanding the environment and its effects on health and suggesting better interventions.

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.

Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly.

Crowdsourcing uses online platforms to collect large data from laypersons and has been increasingly used over the past 5 years to answer questions about aesthetic and functional outcomes following plastic and reconstructive surgery. This systematic review evaluates crowdsourcing articles in plastic and reconstructive surgery based on study topic, participants, and effect size in the hopes of describing best practices.
A systematic search strategy was developed with a licensed librarian and attending plastic surgeon to query all articles using crowdsourcing in plastic and reconstructive surgery. Covidence systematic review manager was used by two independent reviewers to import articles, screen abstracts, evaluate full texts, and extract data.
A search run on October 8, 2021, yielded 168 studies, of which 45 were ultimately included. Craniofacial surgery and aesthetic surgery collectively constituted over half of studies. Participants in plastic surgery crowdsourcing studies are more commonly from the United States, female, straight, 25 to 35 years old; have completed college; and earn $20,000 to $50,000 per year. Studies typically assessed aesthetic perceptions, cost approximately $350, ran a median of 9 days, included approximately 60 unique survey items, and included approximately 40 unique human images.
Crowdsourcing is a relatively new, low-cost method of garnering high-volume data from laypersons that may further our understanding of public perception in plastic and reconstructive surgery. As with other nascent fields, there is significant variability in number of subjects used, subject compensation, and methodology, indicating an opportunity for quality improvement.

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.

To understand trainee experiences of participating in a living systematic review (LSR) for rheumatoid arthritis and the potential benefits in terms of experiential evidence-based medicine (EBM) education.
We conducted a mixed-methods study with trainees who participated in the LSR and who were recruited broadly from training programs in two countries. Trainees received task-specific training and completed one or more tasks in the review: assessing article eligibility, data extraction, and quality assessment. Trainees completed a survey followed by a one-on-one interview. Data were triangulated to produce broad themes.
Twenty one trainees, most of whom had a little prior experience with systematic reviews, reported a positive overall experience. Key benefits included learning opportunities, task segmentation (ability to focus on a single task, as opposed to an entire review), working in a supportive environment, international collaboration, and incentives such as authorship or acknowledgment. Trainees reported improvement in their competency as a Scholar, Collaborator, Leader, and Medical Expert. Challenges included communication and technical difficulties and appropriate matching of tasks to trainee skillsets.
Participating in an LSR provided benefits to a wide range of trainees and may provide an opportunity for experiential EBM training, while helping LSR sustainability.

Medical crowdfunding provides opportunities for individuals who lack financial resources to access the health services that they need. Despite the popularity of medical crowdfunding, the current understanding of the success of medical crowdfunding campaigns is fragmented and inadequate.
We aimed to comprehensively investigate which factors lead to the success of medical crowdfunding campaigns.
A search was conducted in PubMed, PsycINFO, Web of Science, ACM Digital Library, and ScienceDirect from 2010 to June 2020. Papers directly and indirectly related to the success of medical crowdfunding campaigns were included. Two reviewers independently extracted information on the success of medical crowdfunding campaigns.
Our search yielded 441 articles, of which 13 met the inclusion criteria. Medical crowdfunding is increasingly attracting academic attention, and most studies leverage text analysis as their research methods; however, there is a lack of consensus on the definition of medical crowdfunding among researchers. Four categories of factors that affect the success of medical crowdfunding were identified: platforms, raisers, donors, and campaigns.
Although some limitations exist in our systematic review, our study captured and mapped literatures of the success of medical crowdfunding campaigns systematically, which can be used as the basis for future research on this topic.