Using Crowdsourcing to Develop a Peer-Led Intervention for Safer Dating App Use: Pilot Study
Bibliographic record
Abstract
BACKGROUND: Smartphone-based dating apps are rapidly transforming how people seek potential sexual and romantic partners. However, they can also increase the risk of unsafe sexual behaviors, harassment, and infringement of personal privacy. Current research on interventions for safer dating app use remains insufficient. OBJECTIVE: The goal of this study was to describe the development of an intervention for safer dating app usage using crowdsourcing and peer-led approaches. METHODS: This paper describes the development of an intervention program designed to promote safer dating app use among college students. Crowdsourcing and peer-led approaches were adopted during key stages of the development process. Focus group discussions were held to assess the experience and needs of dating app users. A crowdsourcing contest then solicited ideas for performance objectives for the intervention. These objectives were grouped to further identify practical strategies. A one-day intensive workshop was subsequently held with peer mentors to brainstorm ideas for the production of creative interventional materials. The intervention programs were produced and tested in a pilot study. The app's effectiveness will be evaluated in a cluster randomized controlled trial. RESULTS: The intervention program consists of a risk assessment tool, a first-person scenario game, and four short videos. The risk assessment tool, comprised of 14 questions, will give the participant a score to determine their level of risk of adverse events when using dating apps. The scenario game is a first-person simulation game where the players are presented with choices when faced with different scenarios. The short videos each last 2-4 minutes, with points of discussion aimed at addressing the risks of using dating apps. The programs were piloted and were found to be relatable and helpful when further modifications were made. CONCLUSIONS: Potential challenges identified during the development process included data management and analysis, sustaining peer mentors' interests and participation, and balancing between providing more information and perpetuating social stigma around dating app use. By integrating new approaches, such as crowdsourcing and the peer-led approach, in developing an intervention for safer dating app use, our development process provides a viable model for developing future interventions to address the risks associated with dating app use.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".