Sources of knowledge and truth related to anabolic/androgenic steroid use among two-spirit, gay, bisexual, queer, and other men who have sex with men
Bibliographic record
Abstract
OBJECTIVES: This study sought to explore how two-spirit, gay, bisexual, and queer cisgender and transgender (2SGBQ+) men engage with information related to non-prescribed anabolic/androgenic steroid (AAS) use, and how discourses of risk surrounding AASs influence their AAS use practices. Two objectives were achieved: (1) Sources of information that 2SGBQ+ men consulted when considering using AASs were identified and (2) the ways in which discourses of risk shaped 2SGBQ+ men's experiences of using AASs were revealed. METHODS: Participants were recruited for semi-structured interviews online and through word of mouth. A critical poststructural methodology and theories of risk discourse and biopolitics were used to identify themes and interpret data. RESULTS: Seventeen interviews were conducted with adult 2SGBQ+ cis and trans men. Three themes emerged: (1) Unauthoritative sources of knowledge and truth sought by current and prospective AAS users were inconsistent and difficult to evaluate; (2) Authoritative sources, including health care providers, reacted inconsistently; and (3) 2SGBQ+ men generated and shared lay knowledges as a form of community-led harm reduction. CONCLUSION: The complexities of seeking and evaluating information highlight the privileged nature of trustworthy, accurate information on the topic. Risk-as a discursive regime-places 2SGBQ+ male AAS users in the position to produce lay knowledge and cultivate their own "truths" on the topic, which can lead to preventable harm. Public health needs to address these biopolitical effects by considering these lay forms of knowledge as an untapped resource and design accessible and judgement-free AAS use harm reduction programs for 2SGBQ+ AAS users.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".