Gain-Framed Health Messaging, Medical Trust, and Pre-Exposure Prophylaxis (PrEP) Self-Efficacy: An Experimental Study
Bibliographic record
Abstract
Background: Despite the clinical efficacy of pre-exposure prophylaxis (PrEP) in preventing HIV, uptake remains suboptimal among men who have sex with men (MSM) in the United Kingdom (UK). Sustaining progress in the PrEP cascade requires more than biomedical availability; it demands effective, psychologically informed interventions that address persistent barriers. Psychological factors, such as medical mistrust, low PrEP self-efficacy, and identity-related processes, continue to undermine engagement. This study tested whether narrative persuasion and message framing could influence these barriers. Method: A sample of 253 MSM was recruited to participate in an online experiment and completed baseline measures of identity resilience before being randomly allocated to either the gain-framed (N = 122) or loss-framed (N = 124) narrative condition and then completing post-manipulation measures of medical mistrust and PrEP self-efficacy. After excluding 7 cases due to ineligibility, data from 246 participants were analysed using mediation analysis. Results: Participants in the gain-framed condition reported lower medical mistrust than those in the loss-framed condition. Medical mistrust was, in turn, associated with lower PrEP self-efficacy. Identity resilience was associated with lower medical mistrust and higher PrEP self-efficacy. Discussion: These findings provide novel causal evidence that gain-framed health narratives can reduce mistrust and indirectly enhance PrEP self-efficacy. Identity resilience also emerges as a key psychological factor influencing trust and behavioural confidence. Conclusions: Interventions to improve and sustain PrEP uptake should combine gain-framed, narrative-based messaging with strategies to bolster identity resilience. Such approaches may address psychosocial barriers more effectively and promote equitable PrEP uptake among MSM.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".