‘Not the swab!’ Young men’s experiences with STI testing
Bibliographic record
Abstract
In Canada, STI rates are high and rising, especially amongst young men. Meanwhile, the needs of young men regarding STI testing services are poorly understood, as are the socio-cultural and structural factors that influence young men's sexual health-seeking behaviours. To better understand this phenomenon, we draw on interviews with 45 men (ages 15-25) from British Columbia, Canada. Our research reveals how structural forces (e.g. STI testing procedures) interact with socio-cultural factors (e.g. perceptions of masculinities and feminities) to shape young men's experiences with STI testing. STI testing was characterised as both a potentially sexualised experience (e.g. fears of getting an erection during genital examinations), and as a process where young men experience multiple vulnerabilities associated with exposing the male body in clinical service sites. In response, participants drew on dominant ideals of masculinity to reaffirm their predominately hetero-normative gender identities. Despite growing up in an era where sexual health promotion efforts have been undertaken, participants did not feel they had permission to engage in discussions with other men about sexual health issues. Attending to young men's perspectives on STI testing represents a starting point in reforming our approaches to addressing how socio-cultural and structural factors shape these experiences.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".