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Record W4406052885 · doi:10.1080/03323315.2024.2441172

School-based mental health interventions: a feasibility study of the R.E.A.C.T. programme

2025· article· en· W4406052885 on OpenAlexfundno aff
Ruth D. Neill, Katrina Lloyd, Paul Best, Mark A. Tully

Bibliographic record

VenueIrish Educational Studies · 2025
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsMental healthPsychological interventionPsychologyMedical educationMedicinePsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

School examinations and assessments in the school setting can cause increased anxiety and may lead to the development of mental health issues. Early intervention has been suggested to combat stress and mental health issues in adolescents. The present study investigated the feasibility of implementing the multi-component R.E.A.C.T. (Reducing Exam Anxiety through Activity and Coping Techniques) intervention, an educational and physical activity-based programme aimed at reducing test anxiety and improving well-being. Outcomes were measured at baseline and post-intervention. The feasibility was explored through programme feedback, observations, a focus group and interviews. Data analysis was conducted through thematic analysis and statistical analysis (descriptive and Wilcoxon Rank Tests). A total of 199 students and 3 teachers from Northern Ireland took part in the study. Findings indicated that the intervention was feasible to be delivered with teachers. Both students and teachers found the programme engaging and described it as enjoyable and beneficial, reporting that it facilitated discussion on mental health and that students felt less anxious around examination periods. Finally, the R.E.A.C.T. programme was shown to be acceptable to be delivered as part of pastoral care or personal development programmes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.134
GPT teacher head0.463
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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