A Men Who Have Sex With Men–Friendly Doctor Finder Hackathon in Guangzhou, China: Development of a Mobile Health Intervention to Enhance Health Care Utilization
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHeath)-based HIV and sexual health promotion among men who have sex with men (MSM) is feasible in low- and middle-income settings. However, many currently available mHealth tools on the market were developed by the private sector for profit and have limited input from MSM communities. OBJECTIVE: A health hackathon is an intensive contest that brings together participants from multidisciplinary backgrounds to develop a proposed solution for a specific health issue within a short period. The purpose of this paper was to describe a hackathon event that aimed to develop an mHealth tool to enhance health care (specifically HIV prevention) utilization among Chinese MSM, summarize characteristics of the final prototypes, and discuss implications for future mHealth intervention development. METHODS: The hackathon took place in Guangzhou, China. An open call for hackathon participants was advertised on 3 Chinese social media platforms, including Blued, a popular social networking app among MSM. All applicants completed a Web-based survey and were then scored. The top scoring applicants were grouped into teams based on their skills and content area expertise. Each team was allowed 1 month to prepare for the hackathon. The teams then came together in person with on-site expert mentorship for a 72-hour hackathon contest to develop and present mHealth prototype solutions. The judging panel included experts in psychology, public health, computer science, social media, clinical medicine, and MSM advocacy. The final prototypes were evaluated based on innovation, usability, and feasibility. RESULTS: We received 92 applicants, and 38 of them were selected to attend the April 2019 hackathon. A total of 8 teams were formed, including expertise in computer science, user interface design, business or marketing, clinical medicine, and public health. Moreover, 24 participants self-identified as gay, and 3 participants self-identified as bisexual. All teams successfully developed a prototype tool. A total of 4 prototypes were designed as a mini program that could be embedded within a popular Chinese social networking app, and 3 prototypes were designed as stand-alone apps. Common prototype functions included Web-based physician searching based on one's location (8 prototypes), health education (4 prototypes), Web-based health counseling with providers or lay health volunteers (6 prototypes), appointment scheduling (8 prototypes), and between-user communication (2 prototypes). All prototypes included strategies to ensure privacy protection for MSM users, and some prototypes offered strategies to ensure privacy of physicians. The selected prototypes are undergoing pilot testing. CONCLUSIONS: This study demonstrated the feasibility and acceptability of using a hackathon to create mHealth intervention tools. This suggests a different pathway to developing mHealth interventions and could be relevant in other settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".