Sexual health literacy among gay, bisexual and other men who have sex with men: a conceptual framework for future research
Bibliographic record
Abstract
Good sexual health requires navigating intimate relationships within diverse power dynamics and sexual cultures, coupled with the complexities of increasing biomedicalisation of sexual health. Understanding this is important for the implementation of biomedical HIV prevention. We propose a socially nuanced conceptual framework for sexual health literacy developed through a consensus building workshop with experts in the field. We use rigorous qualitative data analysis to illustrate the functionality of the framework by reference to two complementary studies. The first collected data from five focus groups (FGs) in 2012 (n = 22), with gay, bisexual and other men who have sex with men aged 18–75 years and 20 in-depth interviews in 2013 with men aged 19–60 years. The second included 12 FGs in 2014/15 with 55 patients/service providers involved in the use/implementation of HIV self-testing or HIV prevention/care. Sexual health literacy goes well beyond individual health literacy and is enabled through complex community practices and multi-sectoral services. It is affected by emerging (and older) technologies and demands tailored approaches for specific groups and needs. The framework serves as a starting point for how sexual health literacy should be understood in the evaluation of sustainable and equitable implementation of biomedical sexual healthcare and prevention internationally.
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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 | 0.055 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".