Development and modelling of a school-based mental health intervention: the co-production of the R.E.A.C.T. programme
Bibliographic record
Abstract
Abstract The lack of effective school-based interventions for addressing mental health issues and psychological well-being in young people, particularly those with stakeholder involvement, for reducing test anxiety in adolescents has caused a call for interventions to be developed through the process of co-production with the key stakeholders, i.e. teachers and students. The purpose of this paper is to present the development and modelling of a coproduced school-based intervention to improve mental health and psychological well-being in adolescents in the post-primary setting. The intervention was developed through a six step co-production model. This included an extensive evidence review, interviews (n = 7), focus groups (n = 6), observations in three school settings and initial modelling of the intervention programme and resources in the co-research partner school. Findings were used to identify the preferred structure and content of the intervention. A six-week intervention for 12–14 year olds was co-produced along with relevant teacher resources and student work books. The intervention consisting of a psycho-educational component and physical activity component underpinned by cognitive, behavioural and self-regulation theories aimed to reduce test anxiety and improve psychological well-being. The co-production model was a successful series of six steps used to create and refine the intervention. The programme represents a theoretically informed intervention comprising multiple components. This study contributes to a better understanding of the determinants of mental health issues among young people and how an intervention can be effectively co-produced. The results suggest that a feasibility study is warranted with teachers delivering the programme.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".