Facebook ads to the rescue? Recruiting a hard to reach population into an Internet-based behavioral health intervention trial
Bibliographic record
Abstract
OBJECTIVE: Facebook (FB) ads are touted as a way to facilitate recruitment of hard to reach participants into digital health research but the evidence has been mixed. This study aimed to empirically evaluate the impact and cost-effectiveness of paid ads for recruitment into a national trial testing an Internet-based, coached intervention for parents of children with Fetal Alcohol Spectrum Disorders. METHODS: Post hoc analysis of FB ad data and Google analytics on the online trial consent site (myStudies) were conducted on 11 campaigns employing static image/text ads. Standard metrics (e.g., click through rate, cost per 1000 impressions, cost per consent) were calculated and descriptive statistics comparing FB ad engagement and enrolled participants over time were conducted. RESULTS: Ad campaigns were active for a combined 115 days over 58 weeks resulting in 1533 links to the online recruitment site. During the ad campaigns, the mean rate of enrolment was 1 participant every 2 days. The first 3 ad campaigns were the most cost-effective. Mean cost per enrolment was $19.27 (Canadian dollars). CONCLUSIONS: FB ads were efficient and cost-effective in broad dissemination of trial information, but more research is needed to explore the impact of saturation (how often ads are posted), design (what is in the ad), and individual determinants (who is likely to respond to an ad) on converting FB ad engagement into enrolment. Avoiding a reductionist approach to analytics will help ensure appropriate and targeted strategies remain the priority for digital health research recruitment through social media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".