Recruiting and Engaging Heterosexual-Identified Men Who have Sex with Men: A Brief Report of Considerations for Sex Researchers
Bibliographic record
Abstract
Heterosexual-identified men who have sex with men (H-MSM) are a unique population difficult to identify and recruit for research and practice. Yet, engaging H-MSM remains a top research priority to learn more about this population's health needs. A scoping review was conducted to develop a stronger understanding of recruitment patterns involving H-MSM in research. The search and screening procedures yielded 160 total articles included in the present study. Most studies relied on venue-based and internet-based recruitment strategies. Thematic analysis was then used to identify three themes. Locations of H-MSM's sexual encounters related to where sex researchers may recruit participants; sociocultural backgrounds of H-MSM related to important characteristics researchers should acknowledge and consider when working with H-MSM; and engagement with health services related to how H-MSM interact with or avoid HIV/STI testing and treatment and other public health services. Findings suggest H-MSM have sex with other men in a variety of venues (e.g. bathhouses, saunas) but tend to avoid gay-centric venues. H-MSM also are diverse, and these unique identities should be accounted for when engaging them. Finally, H-MSM are less likely to access healthcare services than other MSM, highlighting the need for targeted advertisements and interventions specific for H-MSM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.072 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".