Adaptation and Reach of a Pre-Exposure Prophylaxis Social Marketing Campaign for Latino, Latina, and Latinx Populations: Development Study
Bibliographic record
Abstract
BACKGROUND: Latino, Latina, and Latinx (Latino/a/x) individuals remain disproportionately impacted by HIV, particularly sexual minority men and transgender women. Pre-exposure prophylaxis (PrEP) is an effective means of biomedical HIV prevention, but awareness and uptake remain low among marginalized Latino/a/x populations. Social marketing campaigns have demonstrated promise in promoting PrEP in other populations but are poorly studied in Latino/a/x sexual minority men and transgender women. OBJECTIVE: This study aims to (1) adapt and pilot a PrEP social marketing campaign tailored to Latino/a/x populations with a focus on sexual minority men and transgender women through community-based participatory research (CBPR) and (2) evaluate the reach and ad performance of the adapted PrEP social marketing campaign. METHODS: We used the ADAPT-ITT (assessment, decision, adaptation, production, topical experts-integration, training, and testing) framework for adapting evidence-based interventions for new settings or populations. This paper presents how each phase of the ADAPT-ITT framework was applied via CBPR to create the PrEPárate ("Be PrEPared") campaign. Key community engagement strategies included shared ownership with community partners, focus groups to guide content, crowdsourcing to name the campaign, design by local Latino/a/x artists, and featuring local influencers as the faces of PrEPárate. We evaluated campaign reach and advertisement performance using social media platform metrics (paid and organic reach, impressions, unique clicks, and click-through rates [CTR]) and website use statistics from Google Analytics. RESULTS: The PrEPárate campaign ran in Cook County, Illinois, from April to September 2022. The campaign reached over 118,750 people on social media (55,750 on Facebook and Instagram [Meta Platforms Inc] and 63,000 on TikTok [ByteDance Ltd]). The Meta ads performed over the industry benchmark with ads featuring local transgender women (2% CTR) and cisgender sexual minority men (1.4% CTR). Of the different Grindr (Grindr Inc) ad formats piloted, the interstitial Grindr ads were the highest performing (1183/55,479, 2.13% CTR). YouTube (Google) ads were low performing at 0.11% (153/138,337) CTR and were stopped prematurely, given limits on sexual education-related content. In the first year, there were 5006 visitors to the website. CONCLUSIONS: Adaptation of an existing evidence-based intervention served as an effective method for developing a PrEP social marketing campaign for Latino/a/x audiences. CBPR and strong community partnerships were essential to tailor materials and provide avenues to systematically address barriers to PrEP access. Social marketing is a promising strategy to promote PrEP among underserved Latino/a/x populations.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".