A behavioral economics approach to enhancing HIV preexposure and postexposure prophylaxis implementation
Bibliographic record
Abstract
PURPOSE OF REVIEW: The 'PrEP cliff' phenomenon poses a critical challenge in global HIV PrEP implementation, marked by significant dropouts across the entire PrEP care continuum. This article reviews new strategies to address 'PrEP cliff'. RECENT FINDINGS: Canadian clinicians have developed a service delivery model that offers presumptive PEP to patients in need and transits eligible PEP users to PrEP. Early findings are promising. This service model not only establishes a safety net for those who were not protected by PrEP, but it also leverages the immediate salience and perceived benefits of PEP as a natural nudge towards PrEP use. Aligning with Behavioral Economics, specifically the Salience Theory, this strategy holds potential in tackling PrEP implementation challenges. SUMMARY: A natural pathway between PEP and PrEP has been widely observed. The Canadian service model exemplifies an innovative strategy that leverages this organic pathway and enhances the utility of both PEP and PrEP services. We offer theoretical insights into the reasons behind these PEP-PrEP transitions and evolve the Canadian model into a cohesive framework for implementation.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".