Facilitators and Barriers to Mental Health Leaves and Return to Work Among Canadian Teachers
Bibliographic record
Abstract
In our qualitative study, we interviewed 45 Canadian teachers about mental health issues, taking a leave of absence, and their subsequent return to work. We found that doctors, supportive principals, supportive colleagues, supportive human resource departments, supportive school boards, and the teaching unions were facilitators for taking a leave, while stigma, unsupportive administration, preparation, and the process of taking a leave were barriers. In returning to work, principals and administrators, and preparation to return were cited as barriers, while colleagues, principals and administrators, doctors, unions and a change in work were facilitators. We interpret these findings through a synthesized framework combining Allegro and Veerman's theory of sickness absence and D'Amato and Zijsrtra's theory of work resumption, highlighting individual, organizational, and societal factors shaping leave and return decisions. With the high cost of teacher absences and critical staffing shortages, we discuss the impacts of these facilitators and barriers and make recommendations for practice for a healthy teaching workforce.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".