Facebook ads to the rescue? Recruiting a hard to reach population into an Internet-based behavioral health intervention trial
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it