Assessment of Sexual Violence Risk Perception in Men Who Have Sex With Men: Proposal for the Development and Validation of “G-Date”
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,172 | 0,210 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,006 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,006 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,007 | 0,009 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».