The role of social media in recruiting for clinical trials in pregnancy
Bibliographic record
Abstract
Objective Recruitment of pregnant or planning women to clinical studies using traditional advertising is difficult and slow. Given the widespread use of the internet as a source for medical information and research, we analyze the impact of social media as the primary recruitment tool in an ongoing randomized, open‐label clinical trial among pregnant women. Methods Recruitment through traditional sources, such as referrals from medical establishments, between April 2007–November 2011 was compared to supplementary online social media recruitment approaches used between December 2011–May 2012. Yearly recruitment and recruitment rates in the two arms were compared using the Mann Whitney U test. Results Between 2007–2011, with over 56 months of recruitment using traditional sources, 35 women were enrolled in the study, resulting in a mean rate of ±0.62 recruits per month. In the 6 months implementing recruitment primarily through social media‐based strategies, 45 women were recruited, for a 12‐fold higher rate of ±7.5 recruits per month (p<0.0001). Attrition rates as a function of total recruitment remained constant, suggesting that social media mainly had a positive impact on recruitment. Conclusions Clinicians and scientists recruiting for clinical studies should learn how to use online social media platforms to improve recruitment rates, thus increasing efficiency and cost‐effectiveness.
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How this classification was reachedexpand
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 | 0.033 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".