“I Want to Feel Like a Full Man”: Conceptualizing Gay, Bisexual, and Heterosexual Men’s Sexual Difficulties
Bibliographic record
Abstract
Current understandings of sexual difficulties originate from a model that is based on the study of heterosexual men and women. Most research has focused on sexual difficulties experienced by heterosexual men incapable of engaging in vaginal penetration. To better understand men's perceptions and experiences of sexual difficulties, seven focus groups and 29 individual interviews were conducted with gay (n = 22), bisexual (n = 5), and heterosexual (n = 25) men. In addition, the extent to which difficulties reported by gay and bisexual men differ from heterosexual men was explored. Data were analyzed using thematic analysis applying an inductive approach. Two intercorrelated conceptualizations were identified: penis function (themes: medicalization, masculine identity, psychological consequences, coping mechanisms) and pain (themes: penile pain, pain during receptive anal sex). For the most part, gay, bisexual, and heterosexual men reported similar sexual difficulties; differences were evident regarding alternative masculinity, penis size competition, and pain during receptive anal sex. The results of this study demonstrate the complexity of men's sexual difficulties and the important role of sociocultural, interpersonal, and psychological factors. Limitations and suggested directions for future research are outlined.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".