Masculinity, Muscularity, and HIV Sexual Risk Among Gay and Bisexual Men of Color
Bibliographic record
Abstract
Previous research has highlighted the association between HIV sexual risk behaviors, muscularity concerns, and masculinity among gay and bisexual men (GBM). A few studies that explored these issues, however, have used relatively small sample sizes and predominantly White GBM samples. In addition, little is known about whether a drive for muscularity and perceptions of masculinity are associated with HIV sexual risk behaviors among GBM of color. This community-based study examined the association between drive for muscularity, masculinity, and HIV sexual risk among a sample of 389 GBM of color in Toronto. In multivariable analyses, drive for muscularity and masculinity were significantly associated with HIV sexual risk, after controlling for sociodemographic variables and internalized homophobia. Findings suggest that a desire to be more muscular or a disappointment with one’s musculature, as well as an endorsement of body image and penis size as indicators of masculinity may play a role in HIV sexual risk behaviors. This study is among the first to examine the role of drive for muscularity and notions of masculinity in relation to HIV sexual risk exclusively among an ethnoracially diverse sample of GBM. Further research is needed to better understand the link between body image and masculinity to reduce HIV risk among GBM of color. (APA PsycInfo Database Record (c) 2018 APA, all rights reserved) Get Access
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".