Teachers’ Perceptions of the Organizational Factors Affecting Teacher Burnout in Ontario’s Public Schools
Bibliographic record
Abstract
Extensive research emphasizes the critical role of teacher quality in education and highlights the far-reaching impact of teacher burnout on educators, students, and entire education systems. Despite the global prevalence and significant consequences of burnout, teacher well-being remains largely overlooked in policy discussions. In Ontario, where mental health concerns are the leading cause of disability claims among teachers, this issue demands urgent attention. This study explores teachers' perceptions of organizational factors contributing to burnout in Ontario's publicly funded schools, emphasizing the systemic nature of these challenges. Through qualitative interviews with teachers, it identifies key organizational stressors such as misaligned leadership priorities, increased workloads, and insufficient resources, which are associated with emotional exhaustion, reduced professional efficacy, and attrition. By centering on the voices of teachers, this research provides actionable insights and strategies for mitigating burnout through systemic changes.Keywords Teacher, Teacher burnout, Organizational factors, Impact, Well-being, Ontario, Educators, Public schools, Stress, Leadership, Conditions, Job Demands-Resources (JD-R) Model
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".