Emotional Intelligence & Mental Health in the Classroom: Experiences of Canadian Teachers
Bibliographic record
Abstract
Teaching has been noted to be one of the most demanding careers, yet, there is limited research exploring teachers’ experiences with respect to mental health or wellness. Research suggests that emotional exhaustion and mental health concerns may be on the rise among teachers; this not only has a negative impact on teachers’ well-being, but also on students’ learning, academic engagement and stress levels. While there is promising research that identifies emotional intelligence (EI) may be a protective factor in teacher wellness, there is a paucity of research exploring possible connections. The current study explores EI in relation to teacher mental health and burnout. Data was collected through an online survey via two teacher organizations in Canada. Findings revealed that as emotional intelligence increases, mental health concerns and feelings of burnout decrease. Caregiving responsibilities outside of work were not found to influence this relationship. Additionally, the well-being component of EI was identified as the most important predictor of mental health in teachers. These results highlight the importance of building EI skills for teachers as well as adjusting educational policies to support teachers’ well-being.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".