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Record W4407574296 · doi:10.1111/ejed.70018

Evaluation of a Wellness Programme for Preservice Teachers in Hong Kong: Promoting Educational Excellence Through Resilience to Stress ( <scp>PEERS</scp> )

2025· article· en· W4407574296 on OpenAlexaff
Arita W. Y. Chan, TJ Leigh, Bilun Naz Böke, Hui Wang, Cynthia So, Nancy L. Heath

Bibliographic record

VenueEuropean Journal of Education · 2025
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsMcGill University
FundersWYNG Foundation
KeywordsExcellenceResilience (materials science)PsychologyPsychological resilienceStress (linguistics)PedagogyMedical educationPolitical scienceMedicineSocial psychology

Abstract

fetched live from OpenAlex

ABSTRACT This study aimed to evaluate the effectiveness of a wellness programme for preservice teachers, Promoting Educational Excellence through Resilience to Stress (PEERS) . The intervention group participants, who took part in the 4‐week PEERS programme, and the comparison group participants were recruited online. A battery of self‐report scales assessed various psychological constructs, including coping self‐efficacy, resilience, mental health, well‐being, mindfulness and self‐compassion. Data were collected at three time points: before the intervention (T1), immediately after programme completion (T2) and 4 weeks after programme completion (T3). Results suggested that the intervention group participants reported significant improvements in coping self‐efficacy, resilience and the nonjudging facet of mindfulness. High programme satisfaction was also reported, with 96% of the participants rating the overall programme as good or excellent. Therefore, the results demonstrate the effectiveness and positive impact of the PEERS programme on the resilience and coping efficacy of preservice teachers in Hong Kong.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.057
GPT teacher head0.429
Teacher spread0.372 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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