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Record W4414791250 · doi:10.2196/81753

Low Risk Perception of Harm From Substance Use and Sexual Behaviors Among Online Help–Seeking Sexual and Gender Minoritized People in San Francisco, California: Cross-Sectional Survey

2025· article· en· W4414791250 on OpenAlexvenueno aff
Jarett Maycott, Sean Arayasirikul

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachHarmPerceptionSubstance usePublic healthRisk perceptionSexual behaviorPoison control

Abstract

fetched live from OpenAlex

BACKGROUND: Substance use and HIV epidemics have disproportionately affected sexual and gender minoritized (SGM) communities, with heightened risks among men who have sex with men (MSM) and transgender women of color due to intersecting challenges like poverty, mental health issues, and discrimination. Despite overall declines in substance use and sexual risk behaviors in the general population, these issues persist within SGM communities, exacerbated by stigma and systemic barriers to care. Digital health interventions have emerged as promising tools to address these disparities, offering accessible and stigma-reducing alternatives to traditional care, particularly effective among younger individuals and in underserved areas. OBJECTIVE: This study seeks to examine the social correlates of substance use and sexual risk perception among an online sample of help-seeking MSM and transgender women in San Francisco, California. METHODS: We recruited 409 help-seeking MSM and transgender women by using social media advertisements on Facebook, Instagram, and Grindr in 2022-2024. Participants provided informed consent and completed a baseline assessment. RESULTS: Utilization of testing resources for HIV and hepatitis was high among the participants (401/409, 98.04% and 360/409, 88.02%, respectively). Knowledge of HIV or other sexually transmitted infection health services was also high (379/409, 92.67%). Fewer participants (264/409, 64.55%) were knowledgeable about substance use-related services. Although many participants reported that using substances posed a high risk of harm, some perceived engaging in condomless sex, using prescription opioid drugs without a prescription, and using substances during sex as low risk (122/409, 29.83%, 41/409, 10.02%, and 60/409, 14.67%, respectively). Participants who reported experiencing unstable housing were more likely to report perceiving sharing needles (adjusted odds ratio [aOR] 7.20, 95% CI 1.99-27.80) and nonprescription opioid use (aOR 4.02, 95% CI 1.08-14.90) as low risk. Participants who reported an income below the federal poverty level were more likely to report perceiving sharing needles (aOR 6.35, 95% CI 1.84-23.40), prescription opioid use (aOR 2.89, 95% CI 1.32-6.18), and substance use during sex (aOR 2.29, 95% CI 1.14-4.48) as low risk. Participants who have not been tested for hepatitis in the past have 3.31 times the odds of perceiving prescription opioid use as low risk compared to counterparts who have been tested for hepatitis before (95% CI 1.36-7.68). CONCLUSIONS: This study underscores the importance of social determinants in shaping low risk perception of the harm associated with substance use behaviors among online help-seeking SGM people in San Francisco. These systemic inequities structure participants' perceptions, access, and utilization of preventive and public health services. Our findings identify critical opportunities for outreach and preventative efforts needed to serve vulnerable populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.120
GPT teacher head0.470
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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