A systematic review and meta-analysis of the benefits of school-based, peer-led interventions for leaders
Bibliographic record
Abstract
The aim of our systematic review and meta-analysis was to quantitatively synthesise the effects of school-based peer-led interventions on leaders' academic, psychosocial, behavioural, and physical outcomes. Eligible studies were those that: (i) evaluated a school-based peer-led intervention using an experimental or quasi-experimental study design, (ii) included an age-matched control or comparison group, and (iii) evaluated the impact of the intervention on one or more leader outcomes. Medline, Sportdiscus, Psychinfo, Embase, and Scopus online databases were searched on the 24th of October, 2022 which yielded 13,572 results, with 31 included in the narrative synthesis and 12 in the meta-analysis. We found large positive effects for leaders' attitudes toward bullying (d = 1.02), small-to-medium positive effects for leaders' literacy (d = 0.39), and small positive effects for leaders' self-esteem (d = 0.18). There were mixed findings for behavioural outcomes and null effects for physical outcomes. Notable limitations of this research are the inclusion of a relatively small number of studies, and high heterogeneity in those included. Our findings have the potential to inform educational practice, but also highlight the need for further research examining the mechanisms that might account for the observed effects. Our systematic review was prospectively registered with PROSPERO (CRD42021273129).
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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 | 0.031 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.030 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".