The association between teacher distress and student mental health outcomes: a cross-sectional study using data from the school mental health survey
Bibliographic record
Abstract
Abstract Background Few studies have examined the inter-relationships between teacher and student mental health. We aimed to examine associations between teacher distress and student mental health difficulties and if student perceptions of school safety moderate these associations. Method Data from 23,568 students in grades 6–12 and 1,478 teachers from 268 schools participating in the School Mental Health Surveys in Ontario, Canada, were used. Three-level (student, classroom, school) multivariable linear regression models were fit to examine associations between teacher distress and student internalizing and externalizing symptoms by elementary (grades 6–8) and secondary (grades 9–12) school. Statistical interactions were used to evaluate effect modification. Results Small but statistically significant, positive associations were found between teacher distress and internalizing (b = 0.02; 95% CI [0.01, 0.04], p < 0.05) and externalizing symptoms (b = 0.03; 95% CI [0.01, 0.05], p < 0.001) among elementary students only. Student perceptions of school safety moderated the association between teacher distress and externalizing symptoms among elementary students, whereby the positive association was magnified among students reporting lower school safety. Conclusions Findings from this study highlight the importance of concurrently addressing the mental health needs of educators and students. School safety represents a modifiable target for prevention and intervention efforts in schools that could serve to promote student mental health and mitigate potential risk factors in schools.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".