#PrEP4Love: An Evaluation of a Sex-Positive HIV Prevention Campaign
Bibliographic record
Abstract
BACKGROUND: Pre-exposure prophylaxis (PrEP) is an effective but underutilized method for preventing HIV transmission in communities vulnerable to HIV. Public health campaigns aimed at increasing PrEP awareness and access have less evaluation data. OBJECTIVE: The aim of this study was to evaluate Chicago's PrEP campaign, PrEP4Love (P4L), a campaign that uses health equity and sex-positivity approaches for information dissemination. METHODS: P4L launched in February 2016 and remains an active campaign to date. The analysis period for this paper was from the launch date in February 2016 through May 15, 2016. Our analysis reviews the Web-based reach of the campaign through views on social media platforms (Facebook and Instagram), smart ads, or ads served to individuals across a variety of Web platforms based on their demographics and browsing history, and P4L website clicks. RESULTS: In total, 40,913,560 unique views were generated across various social media platforms. A total of 24,548 users clicked on P4L ads and 32,223,987 views were received from smart ads. The 3 most clicked on ads were STD Signs & Symptoms-More Information on STD Symptoms, HIV & AIDS Prevention, and HIV Prevention Medication. An additional 6,970,127 views were gained through Facebook and another 1,719,446 views through Instagram. There was an average of 182 clicks per day on the P4L website. CONCLUSIONS: This is the first study investigating public responses to a health equity and sex-positive social marketing campaign for PrEP. Overall, the campaign reached millions of individuals. More studies of PrEP social marketing are needed to evaluate the relationship of targeted public health campaigns on stigma and to guide future PrEP promotion strategies.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".