Targeted Facebook Advertising is a Novel and Effective Method of Recruiting Participants into a Human Papillomavirus Vaccine Effectiveness Study
Bibliographic record
Abstract
BACKGROUND: Targeted advertising using social networking sites (SNS) as a recruitment strategy in health research is in its infancy. OBJECTIVE: The aim of this study was to determine the feasibility of targeted Facebook advertisements to increase recruitment of unvaccinated women into a human papillomavirus (HPV) vaccine effectiveness study. METHODS: Between September 2011 and November 2013, females aged 18 to 25 years, residing in Victoria, Australia, were recruited through Facebook advertisements relating to general women's health. From November 2013 to June 2015, targeted advertising campaigns were implemented to specifically recruit women who had not received the HPV vaccine. Consenting participants were invited to complete an online questionnaire and those who had ever had sexual intercourse were asked to provide a self-collected vaginal swab. The HPV vaccination status of participants was confirmed from the National HPV Vaccination Program Register (NHVPR). RESULTS: The campaign comprised 10 advertisements shown between September 2011 and June 2015 which generated 55,381,637 impressions, yielding 23,714 clicks, at an overall cost of AUD $22,078.85. A total of 919 participants were recruited. A greater proportion of unvaccinated women (50.4%, 131/260) were recruited into the study following targeted advertising, compared with those recruited (19.3%, 127/659) prior to showing the modified advertisement (P<.001). A greater proportion of the total sample completed tertiary education and resided in inner regional Victoria, compared with National population census data (P<.001), but was otherwise representative of the general population. CONCLUSIONS: Targeted Facebook advertising is a rapid and cost-effective way of recruiting young unvaccinated women into a HPV vaccine effectiveness study.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".