Enhancing Coping Self-efficacy and Well-being: A Multi-context Study of an Emotion Regulation Program for Preservice Teachers
Bibliographic record
Abstract
Abstract Globally, pre-service and early career teachers report high levels of stress and emotion regulation difficulties, leading to high rates of burnout and attrition in the first five years of the profession. Therefore, there is an urgent need for the development and evaluation of programming to support pre-service teachers’ emotion regulation and stress management and examine the relevance of the program across cultural contexts. The present study investigated the effectiveness of a program for preservice teachers’ emotion regulation and stress management in Montreal-Canada and Hong Kong-China. Participants were 378 preservice teachers, with 158 from Hong Kong (81.6% women; program group: n = 70) and 220 from Canada (85.9% women; program group: n = 157). Data were collected at three points: before the program, immediately after the program, and two to four weeks after the program. The findings from the latent growth models suggested that both Canadian and Hong Kong program groups displayed significant improvements in coping self-efficacy after participating in the program whereas no changes were detected for well-being. Moreover, the Canadian sample demonstrated significantly higher baseline coping self-efficacy compared to the Hong Kong Chinese sample. Gender and age, considered as covariates in the study, did not yield any significant findings. The study provides valuable insights into the potential benefits of emotion regulation programming for preservice teachers’ coping abilities and emphasizes that such benefits can be comparable across contexts.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".