Exploring the State of Sexual Health Literacy among Young Black Gay and Other Men who Have Sex with Other Men in Toronto, Ontario, Canada
Bibliographic record
Abstract
Despite advancements in diagnosis, treatment and prevention for HIV and other sexually transmitted infections (STIs), young Black gay and other men who have sex with men (YBGM) experience significantly higher HIV rates compared to other young gay and other men who have sex with men. While sexual health literature indicates various socio-structural factors (e.g., systemic racism, low income) contribute to these rates, research on YBGM’s sexual health literacy in Canada is sparse. I developed a qualitative study guided by intersectionality and the social-ecological model theoretical frameworks and methodologically informed by Charmaz’s constructivist grounded theory to begin addressing this scarcity. My primary research question was: what is the state of YBGM’s sexual health literacy? My sub-questions were: 1) what or who are YBGM’s sources of sexual health information?; 2) how do YBGM evaluate the information they gather from these sources? and 3) in what ways, if any, are YBGM applying this information in their everyday lives? Twenty-two YBGM aged 16 to 31 years underwent a socio-demographic survey and one-on-one semi-structured interviews. The first manuscript centered on YBGM’s pre-exposure prophylaxis (PrEP) knowledge. My findings revealed that YBGM’s limited PrEP knowledge was due to healthcare institutions’ inability to effectively disseminate PrEP information coupled with the impact of PrEP stigma and their social locations on this knowledge. Manuscript two explored YBGM’s approaches to evaluating the credibility of key sexual health information sources. My findings countered common narratives in the literature that identified sex-ed, same aged peers and family as key sources. Older Black peers were seen as a key source who used ‘testimonials’, a commonly used knowledge dissemination approach in the Black-African diaspora, to share their experiential sexual health knowledge with YBGM. My final manuscript explored the state of healthcare providers’ sexual health communication skills through the lens of YBGM. Participants’ accounts pointed to providers’ inadequate skills and to the healthcare system’s medicalization of YBGM whereby their race, sexual orientation and/or age automatically categorized them as needing HIV and other STI testing even when they sought non-sexual healthcare. We conclude with the study’s implications on sexual healthcare research, policy and practice.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".