Canadian teachers with mental health issues and workplace accommodations
Bibliographic record
Abstract
Our quantitative research study aimed to explore Canadian teachers’ experiences with formal workplace accommodations for mental health issues by directly surveying 461 Canadian teachers (kindergarten to Grade 12, mean age of 41.7 years, 82.6% women) who self-reported having experienced a mental health issue. We found that only a small percentage, 15.7%, requested a formal accommodation. About half of the teachers who requested a formal accommodation had difficulty both requesting and obtaining the accommodation. The majority of teachers (73.6%) who made an accommodation request ultimately did receive the accommodation. A change in the number of hours worked was the most frequently requested accommodation, followed by a change in grade/course/role/position, and then a change in school. Teachers reported receiving varying levels of support from their supervisors for the accommodation (19.7% very unsupportive, 21.1% unsupportive, 21.1% neither supportive nor unsupportive, 23.9% supportive, 14.1% very supportive). Implications for teacher self-advocacy, the process for accommodations, the duty to accommodate, as well as principal support for accommodations, are discussed. By being the first known Canadian quantitative study on the topic, our research fills a gap in the literature on workplace accommodations for teachers dealing with mental health issues, offering quantitative insights into the self-reported experiences of teachers.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".