Assessment of Sexual Violence Risk Perception in Men Who Have Sex With Men: Proposal for the Development and Validation of “G-Date”
Bibliographic record
Abstract
BACKGROUND: Sexual violence (SV) is a significant problem for sexual minorities, including men who have sex with men (MSM). The limited research suggests SV is associated with a host of syndemic conditions. These factors tend to cluster and interact to worsen one another. Unfortunately, while much work has been conducted to examine these factors in heterosexual women, there is a lack of research examining MSM, especially their SV risk perception. Further, MSM are active users of dating and sexual networking (DSN) mobile apps, and this technology has demonstrated usefulness for creating safe spaces for MSM to meet and engage partners. However, mounting data demonstrate that DSN app use is associated with an increased risk for SV, especially given the higher likelihood of using alcohol and other drugs before sex. By contrast, some researchers have demonstrated that DSN technology can be harnessed as a prevention tool for HIV; unfortunately, no such work has progressed regarding SV. OBJECTIVE: This study aims to (1) use qualitative and quantitative methods to tailor an existing laboratory paradigm of SV risk perception in women for MSM using a DSN mobile app framework and (2) subject this novel paradigm to a rigorous validation study to confirm its usefulness in predicting SV, with the potential for use in future prevention endeavors. METHODS: To tailor the paradigm for MSM, a team of computer scientists created an initial DSN app (G-Date) and incorporated ongoing feedback about the usability, feasibility, and realism of this tool from a representative sample of MSM. We used focus groups and interviews to assist in the development of G-Date, including by identifying relevant stimuli, developing the cover story, and establishing the appropriate study language. To confirm the paradigm's usefulness, we are conducting an experimental study with web-based and face-to-face participants to determine the content, concurrent, and predictive validities of G-Date. We will evaluate whether certain correlates of SV informed by syndemics and minority stress theories (eg, history of SV and alcohol and drug use) affect the ability of MSM to detect SV risk within G-Date and how paradigm engagement influences behavior in actual DSN app use contexts. RESULTS: This study received funding from the National Institute on Alcohol Abuse and Alcoholism on September 10, 2020, and ethics approval on October 19, 2020, and we began app development for aim 1 immediately thereafter. We began data collection for the aim 2 validation study in December 2022. Initial results from the validation study are expected to be available after December 2025. CONCLUSIONS: We hope that G-Date will enhance our understanding of factors associated with SV risk and serve as a useful step in creating prevention programs for this susceptible population.
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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 | 0.172 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".