Identifying Effective Components of a Social Marketing Campaign to Improve Engagement With Express Sexual Health Services Among Gay, Bisexual, and Other Men Who Have Sex With Men: Case Study
Bibliographic record
Abstract
Background: Little is known about how best to reach people with social marketing messages promoting use of clinical HIV and sexually transmitted infection (STI) services. Objective: We evaluated a multiplatform, digital social marketing campaign intended to increase use of HIV/STI testing, treatment, and prevention services among gay, bisexual, and other men who have sex with men (MSM) at an LGBTQ+ (lesbian, gay, bisexual, transgender, queer, and/or questioning) community health center. Methods: We evaluated engagement with a social marketing campaign launched by Open Door Health, the only LGBTQ+ community health center in Rhode Island, during the first 8 months of implementation (April to November 2021). Three types of advertisements encouraging use of HIV/STI services were developed and implemented on Google Search, Google Display, Grindr, and Facebook. Platforms tracked the number of times that an advertisement was displayed to a user (impressions), that a user clicked through to a landing page that facilitated scheduling (clicks), and that a user requested a call to schedule an appointment from the landing page (conversions). We calculated the click-through rate (clicks per impression), conversion rate (conversions per click), and the dollar amount spent per 1000 impressions and per click and conversion. Results: Overall, Google Search yielded the highest click-through rate (7.1%) and conversion rate (7.0%) compared to Google Display, Grindr, and Facebook (click-through rates=0.4%-3.3%; conversion rates=0%-0.03%). Although the spend per 1000 impressions and per click was higher for Google Search compared to other platforms, the spend per conversion-which measures the number of people intending to attend the clinic for services-was substantially lower for Google Search (US $48.19 vs US $3120.42-US $3436.03). Conclusions: Campaigns using the Google Search platform may yield the greatest return on investment for engaging MSM in HIV/STI services at community health clinics. Future studies are needed to measure clinical outcomes among those who present to the clinic for services after viewing campaign advertisements and to compare the return on investment with use of social marketing campaigns relative to other approaches.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".