Mental Health Experiences of Teachers: A Scoping Review
Bibliographic record
Abstract
Teacher mental health continues to be of concern in elementary and secondary schools; however, supporting teacher wellbeing is understudied (Parker et al., 2012; Roffey, 2012), particularly from a gender perspective (Bourgeault et al., 2021). Among professionals, teachers exhibit one of the highest levels of job stress and burnout on the job. (Hakanen et al., 2006; Stoeber & Rennert, 2008). This scoping review investigates and consolidates the existing research on teacher mental health, leaves of absences, and return-to-work. Work context and personal factors/family context contribute to teacher stress and attrition and by extension may impact temporary leaves of absence (Pressley, 2021). Several articles report on interventions with moderate success to reduce teacher stress, but no studies evaluated return-to-work interventions (Ebert, 2014; Kwak et al., 2019). The amount of stress teachers are experiencing and the pressure that is causing them to burn out is the most common narrative present in the literature. The review highlights gaps in the literature surrounding teacher mental health, leaves of absence, and return-to-work and a notable gap regarding the role of gender.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".