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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".