A Crowdsourcing Open Contest to Design Pre-Exposure Prophylaxis Promotion Messages: Protocol for an Exploratory Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: In the United States, black men who have sex with men (BMSM) are disproportionately affected by HIV. Pre-exposure prophylaxis (PrEP) can reduce HIV incidence. However, real-world implementation of PrEP outside of clinical trials has identified racial disparities in PrEP awareness, uptake, and adherence. In the context of a long history of medical mistrust and power imbalances between scientists and community members, strategies to increase uptake of PrEP among BMSM should consider ways to ensure messages address the needs and priorities of the community. Crowdsourcing contests shift traditional individual tasks to a large group and may enhance community engagement. OBJECTIVE: This paper describes the research protocol of a contest approach to soliciting PrEP promotion messages among BMSM in Baltimore. METHODS: Open-contest implementation and evaluation will proceed as follows: (1) organize a community steering group; (2) develop platforms to solicit crowd input; (3) engage the community to contribute ideas through a Web-based forum and in-person events; (4) evaluate contest entries using both community panel judge assessment and crowd voting; (5) utilize mixed methods to evaluate feasibility, acceptability, and community engagement; and (6) disseminate contest results. RESULTS: This study was funded by the National Institutes of Health (National Institute of Mental Health: R34MH116725) in May 2018 and was approved by the institutional review board in April 2018. The open contest started in February 2019, and data analyses for the mixed method evaluation are expected to complete in December 2019. CONCLUSIONS: The contest will potentially bring new ideas in developing more impactful and locally defined PrEP promotion campaigns. We will determine whether an open-contest approach is acceptable among BMSM in Baltimore. If successful, this study can inform future projects using a similar approach on how to identify and implement programs and policies that are more responsive to community needs and that build up community assets. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/15590.
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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 | 0.066 | 0.080 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.080 | 0.017 |
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".