Giving schools a nudge: can behavioural insights improve recruitment of schools to randomised controlled trials?
Bibliographic record
Abstract
OBJECTIVE: It is widely acknowledged that recruitment to randomised controlled trials (RCTs) is challenging, particularly trials that operate across multiple sites. A research area in need of further high-quality evaluation, including RCTs, is school-based mental health interventions for refugee children and adolescents. However, engaging schools with interventions and associated evaluations can be challenging. This paper explores the application of behavioural insights, i.e. evidence-based understanding of how people behave and make decisions, to RCT recruitment at the school level via email communications. A pilot study of applying behavioural insights to mail outs attempting to recruit schools to a RCT of a trauma-focused group intervention for refugee children and adolescents experiencing symptoms of post-traumatic stress is reported. Rates of school involvement between the behavioural insights approach (n = 31) and a standard outreach approach (n = 65) are compared. RESULTS: Schools were more likely to give a positive response to the mail out designed using the behavioural insights framework than standard outreach. Accounts of recruitment strategies such as this are valuable additions to the literature on RCT methodology given the potential for recruitment issues to affect trial operations.
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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 | 0.699 | 0.813 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier 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".