Strategies for improving recruitment of pregnant women to clinical research: An evaluation of social media versus traditional offline methods
Bibliographic record
Abstract
Objectives: To evaluate the recruitment of pregnant women for a clinical trial in Vancouver, Canada, via social media versus offline methods and to explore optimization of social media campaigns. Methods: Facebook was used to run nine social media campaigns (15 weeks total, CA$675). Offline methods were used concurrently over 64 weeks (printing costs: CA$300). The cost, rate of recruitment and conversion rate in each group was calculated. Performance metrics of social media campaigns (reach, impressions, clicks, inquiries, enrolments) were recorded. Linear regression was used to explore the association between metrics and dollars spent per campaign. Results: In total, n = 481 inquiries were received: n = 51 (11%) via offline methods and n = 430 (89%) via social media. Enrolees (n = 60 total) included n = 24 (40%) and n = 36 (60%) via offline and social media methods, respectively. Gestational weeks upon inquiry (n = 251; mean ± SD) were not different among groups (offline: 13.3 ± 4.7; social media: 13.2 ± 5.6). Direct cost per enrolee was CA$13 and CA$19 via offline and social media methods, respectively (however, this does not include cost of labour). The rate of recruitment was approximately six times faster via social media. However, the conversion rate was higher via offline methods than social media (47% vs. 8%). The amount spent per campaign was significantly associated with improved clicks and inquiries, but not enrolments. Conclusions: Social media was more efficient and effective than offline methods. We gained numerous insights for optimization of social media campaigns (dollars spent, attribution setting, photo testing, automatic optimization) to increase clicks and inquiries, however, this does not necessarily increase enrolments, which was more dependent on study-specific factors (e.g. time of year, study design).
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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 | 0.058 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".