Evaluating the Prevalence and correlates of high stress and low resilience among Educators
Bibliographic record
Abstract
Introduction High-stress levels can be problematic for teachers and indirectly affect students. Knowledge about the prevalence and predictors of high-stress and low resilience will provide information about the extent of the problem among teachers in Canada. Objectives To examine the prevalence and correlates of perceived stress and low resilience among Alberta, Nova Scotia, Newfoundland and Labrador teachers. Methods This is a cross-sectional study. Participants self-subscribed to the Wellness4Teachers text-messaging program and completed the online survey on enrollment. Data collection occurred from September 2022 to August 2023. Resilience and stress were respectively assessed using the Brief Resilience Scale (BRS) and the Perceived Stress Scale (PSS-10). Data was analyzed with SPSS version 28. Results A total of 1912 teachers subscribed to the Wellness4Teachers program, and 810 completed the baseline survey, yielding a response rate of 42.40%. The prevalence of high stress and low resilience were respectively 26.3%, and 40.1%. 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). Conclusions Our study findings infer there’s an incidence of high levels of stress and low resilience among teachers in the three Canadian provinces. Governments and policymakers in the education field should integrate stress management and resilient building strategies into teachers’ ongoing professional development programs to help prevent and address high stress. Disclosure of Interest None Declared
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 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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".