A scoping review of school-based expressive writing implementation reporting practices: missed opportunities and new research directions
Bibliographic record
Abstract
BACKGROUND: Expressive writing (EW) interventions are an effective, flexible, and cost-efficient option for mental health promotion, making them ideally suited for resource-limited school settings. However, the effectiveness of EW interventions varies greatly across studies, which may be partly explained by how EW interventions are implemented. As school-based EW interventions become increasingly popular and more widely used, rigorous reporting of implementation can help advance this emerging field by informing how variation in implementation across studies influences intervention outcomes. PURPOSE: The purpose of this scoping review was to evaluate the implementation reporting practices of EW interventions in school settings as they can profoundly impact EW effectiveness. METHODS: The present scoping review assessed the current state of fidelity of implementation (implementation) reporting in the school-based EW literature and identified areas where more rigorous reporting is needed. Out of an initial sample of 367 studies, 19 were eligible for inclusion in the review. Data were analyzed for critical issues and themes derived from Cargo et al.'s (2015) Checklist for Implementation (Ch-IMP). RESULTS: Overall, the results of this scoping review indicate that researchers who implement EW in school settings have not consistently assessed key implementation domains such as dose received and fidelity. CONCLUSIONS: To address this problem, the present review adds a unique contribution to the literature by identifying how rigorous reporting of implementation can strengthen the evidence base for school-based EW interventions. Specifically, researchers can support the use of EW interventions in schools through increased implementation reporting to better understand how variability in fidelity of implementation affects treatment outcomes.
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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 | 0.197 | 0.423 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.029 | 0.033 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".