Prevalence and Correlates of High Stress and Low Resilience among Teachers in Three Canadian Provinces
Bibliographic record
Abstract
Objective: High stress levels can be problematic for teachers and indirectly affect students. Resilience may be a protective factor for overcoming stress. Knowledge about the prevalence and correlates of high stress and low resilience will provide information about the extent of the problem among teachers in Canada. Methods: This is a cross-sectional study among teachers in Alberta, Nova Scotia, and Newfoundland and Labrador in Canada. Participants self-subscribed to the Wellness4Teachers supportive text messaging program and completed the online survey on enrollment. Baseline data collection occurred from 1 September 2022 to 30 August 2023. Resilience and stress were, respectively, assessed using the Brief Resilience Scale (BRS) and the Perceived Stress Scale (PSS-10). The data were analyzed with SPSS version 28 using chi-squared tests and binary logistic regression analysis. Results: A total of 1912 teachers subscribed to the Wellness4Teachers program, and 810 completed the baseline survey, yielding a response rate of 42.40%. Most of the participants, 87.8%, were female, and 12.2% were aged 18 to 61 and above. The prevalence of low resilience was 40.1%, and high stress had a prevalence of 26.3%. After controlling for all the other variables in the logistic regression model, participants with low resilience were 3.10 times more likely to experience high-stress symptoms than those with normal to high resilience (OR = 3.10; 95% CI: 2.18–4.41). Conversely, participants who reported high stress were 3.13 times more likely to have low resilience than those with low to moderate stress (OR = 3.13; 95% CI: 2.20–4.44). Additionally, junior and senior high school teachers were, respectively, 2.30 times (OR = 2.30; 95% CI: 1.25–4.23) and 2.12 times (OR = 2,12; 95% CI: 1.08–4.18) more likely to have low resilience compared to elementary school teachers. Conclusions: Our study findings suggest a high prevalence of stress and low resilience among teachers in the three Canadian provinces. Administrators, policymakers in the educational field, school boards, and governments should integrate stress management and resilience-building strategies into teachers’ training and continuing professional development programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".