Social Media Recruitment Strategies to Recruit Pregnant Women into a Longitudinal Observational Cohort Study: An Evaluation (Preprint)
Bibliographic record
Abstract
BACKGROUND The use of social media for study recruitment is increasingly common. Previous studies have typically focused on using Facebook; however, there is limited data to support the use of other social media platforms for participant recruitment, notably in the context of a pregnancy study. OBJECTIVE This study aimed to evaluate the effectiveness of Facebook, Twitter, and Instagram for recruiting a representative sample of pregnant women into a longitudinal pregnancy cohort study in Calgary, Alberta between September 27, 2021 and April 24, 2022. METHODS Paid advertisements were targeted to 18 to 50 year-old women in Calgary with interests in pregnancy. Data regarding reach, link clicks and cost were collected through Facebook Ads Manager and Twitter Analytics. The feasibility of each platform for recruitment was assessed based on recruitment rate and cost-effectiveness. Demographic characteristics of the participants recruited through each source were compared using chi-square tests. RESULTS Paid advertisements reached 159,778 social media users, resulting in 2390 link clicks and 324 recruited participants. Facebook reached and recruited the most participants, while Instagram saw the highest number of link clicks relative to the number of users who saw the advertisement (2.11%). Facebook and Instagram advertisements were cost-effective with an average cost-per-click of $0.61 and cost-per-completer of $7.89. Twitter advertisements were less successful in terms of recruitment and cost. Demographic characteristics of participants did not differ based on recruitment source except for educational attainment and income, where more highly educated and higher income participants were recruited through Instagram or Twitter. CONCLUSIONS Paid social media advertisements (especially Facebook and Instagram) were feasible and cost-effective methods for recruiting a large sample of pregnant women for survey research. However, future researchers should be aware of the potential for fraudulent responses when using social media for recruitment.
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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 | 0.123 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".