Enhancing Recruitment of Adolescents Aged 16-18 Years in a Web-Based Peer Network Study Through Financial Reimbursements: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Peers are known to influence the health behaviors and attitudes of adolescents, yet recruitment of these networks is challenging. Previous studies have used web-based respondent-driven sampling methods to recruit this population, yet none have experimentally investigated the impact of financial reimbursements. Objective: This study aimed to (1) compare the effectiveness of two financial reimbursement strategies for recruiting adolescents and their peer networks and (2) explore factors associated with successfully recruiting peers. Methods: A parallel-design randomized controlled trial was conducted in which participants (seeds) were randomly allocated to a fixed cash reimbursement (control) or scaled reimbursement (experimental) group as a strategy to be recruited into a web-based peer network study. Seeds aged 16 to 18 years were recruited through social media advertisements and an online student panel. They completed a web-based survey, which assessed eligibility and included questions about their friends (peers). Allocation occurred through a survey platform using a simple randomization method. In the fixed group, all participants in a peer network received AUD $5 (US $3.29); in the scaled group, all participants in a peer network received an additional AUD $5 (US $3.29) per peer who successfully completed the survey (up to AUD $30 each [US $19.72]). Participants and researchers were not blinded to intervention groups. The primary outcome was recruitment of peers to complete the web-based survey (proportion of nominated peers). The number of peers recruited was a secondary outcome. In secondary analyses, we identified peer-, relationship-, and seed-level variables associated with successfully recruiting peers. Results: Of 463 seeds allocated to an intervention (scaled n=221 and fixed n=242), 319 (68.9%) had complete data for analysis (scaled n=157, 71% and fixed n=162, 67%). A total of 11.9% of seeds successfully referred peers (18.5% scaled group and 5.6% fixed group). Those in the scaled reimbursement intervention were 3.80 times more likely to successfully recruit their peers than those in the fixed reimbursement intervention (proportion ratio 3.80, 95% CI 1.78-8.09). Similarly, the average number of peers recruited differed by 0.19 (95% CI 0.11-0.28) per seed between the scaled and fixed intervention groups. Peer recruitment success was similar regardless of the gender, age, education level, and network size of seeds or the gender, age, and closeness of peers. Seeds recruited through social media were more likely to successfully recruit their nominated peers than those recruited through a research panel (proportion ratio 2.20, 95% CI 1.06-4.55). Conclusions: Scaled reimbursements resulted in significantly greater recruitment of peers than fixed reimbursements; however, the total number of peers recruited was low. Greater-value incentives and stronger initial recruitment through social media may be needed to recruit large numbers of friend networks.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".