‘Why are PrEP gays always like this … ’: psychosocial influences on U.K.-based men who have sex with men’s perceptions and use of HIV pre-exposure prophylaxis
Bibliographic record
Abstract
Background Pre-Exposure Prophylaxis (PrEP) is a prescription-based drug used to prevent the spread of Human Immunodeficiency Virus (HIV). In the four nations of the United Kingdom, those with increased need for HIV prevention (e.g. some groups of men who have sex with men) are eligible for PrEP for free, provided by the National Health Service. However, the uptake of PrEP has faced several challenges and many still report barriers to accessing PrEP. This current study aimed to augment current understandings of key psychosocial factors that encourage and inhibit PrEP usage.Method Twenty-two individuals participated in a qualitative interview study and thematic analysis was used to analyse the data.Results The findings are presented under three themes: (1) Reckoning with the Legacies of HIV; (2) PrEP versus Condoms: tensions of sexual liberation!; and (3) The Transposition of PrEP Stigma.Conclusion This study highlights current psychosocial barriers to PrEP uptake and use, as well as the benefits (e.g. reduced HIV anxiety) that PrEP usage can elicit. Three superordinate themes describe how PrEP use is influenced by perceptions of HIV and individuals’ condom use preferences. These coalesce into an identity of a ‘PrEP User’, shaping how stigmas associated with PrEP are then both attributed and mitigated. These data hold merit for informing future PrEP uptake campaigns.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".