Leveraging digital programming to support pre-service teachers’ stress management and well-being: evidence of effectiveness and acceptability
Bibliographic record
Abstract
The aim of this study was to evaluate a digital stress management and well-being programme for pre-service teachers in terms of its effectiveness and acceptability. Participants were 52 Bachelor of Education learners (Mage = 24.04 years; 94.2% women) who completed the programme in March 2023, as well as online measures before (T1) and after (T2) programme completion and at a 1-month follow-up (T3). Significant improvements in mental health, well-being, coping self-efficacy, mindfulness, and impairment in functioning were reported, although perceived stress and teacher self-efficacy did not significantly change over time. Results also revealed excellent programme satisfaction, with 93.5% of learners indicating that the sessions were informative and understandable, and 80.5% noting that they presented valuable strategies and techniques. Findings demonstrate that digital, self-paced stress management and well-being instruction for pre-service teachers produces worthwhile benefits and is well-received, providing additional incentive to integrate such instruction into teacher education programmes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".