Exploring Educator Burnout: Experiences and Perceptions of Teachers and Principals in Canadian K-12 Seventh-day Adventist Schools
Bibliographic record
Abstract
Problem Burnout among educators is a growing concern, particularly within faith-based school systems where teachers often balance high professional expectations with spiritual commitments. In K-12 Canadian Seventh-day Adventist (SDA) schools, limited research has explored how burnout manifests, the factors contributing to it, and its impact on teacher retention. This study aimed to examine the levels and causes of burnout among SDA educators, as well as the systemic factors influencing their experiences. Method This study employed an explanatory sequential mixed-methods design. Quantitative data were collected using the Maslach Burnout Inventory – Educators Survey (MBI-ES) to measure emotional exhaustion, depersonalization, and reduced personal accomplishment among SDA educators across Canada. This study used both MANOVA and ANOVA for the statistical analysis of variables. Based on the survey results, a purposive sample of participants was selected for qualitative interviews. Thematic analysis was conducted to explore educators’ lived experiences and identify contextual contributors to burnout. Results Quantitative findings revealed moderate to high levels of burnout risk, with emotional exhaustion being the most prevalent dimension. Qualitative analysis identified key contributing factors, including workload, inadequate organizational and leadership support, poor work-life balance, and a perceived disconnect between school-level staff and conference leadership. Participants also expressed tension between their commitment to the mission of Adventist education and the institutional pressures they face. Conclusions Burnout among educators is both a personal and systemic issue that is an ongoing concern across all Adventist schools in Canada. Addressing burnout requires more than individual coping strategies; it also needs to include systemic changes to alleviate the problem before it becomes unsustainable. Key recommendations include reducing workload demands, strengthening school and conference-level leadership support, enhancing access to mental health services, and cultivating a school culture that prioritizes educator well-being and longevity.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".