Psychological problems among elementary and high school educators in Canada: association with sick days in the prior school year
Bibliographic record
Abstract
Background: Increased sick leave among educators can detrimentally impact students' productivity, and academic achievement. It remains unknown whether the number of sick days taken by educators in the preceding school year correlates with the prevalence or severity of psychological problems among educators in the subsequent school year. Objective: This study aimed to examine the number of self-reported sick days taken by educators in three Canadian provinces during the 2021/2022 academic year and its association with measures of stress, burnout, low resilience, depression, and anxiety during the 2022/2023 school year. Methods: Data was collected from educators in three Canadian provinces, Alberta, Nova Scotia, and Newfoundland and Labrador, from September 1, 2022, to August 30, 2023. The Maslach Burnout Inventory-Educator Survey (MBI-ES), the Brief Resilience Scale (BRS), and the Perceived Stress Scale were used to assess burnout, resilience, and stress, respectively. Likely Generalized Anxiety Disorder (GAD) and likely Major Depressive Disorder (MDD) were assessed using the Generalized Anxiety Disorder-7 and Patient Health Questionnaire-9 scales, respectively. Statistical analysis was conducted using SPSS version 28. Results: A total of 763 subscribers completed all the demographic, professional questions, and clinical scales, giving a response rate of 39.91%. Of these, there were 94 (12.3%) males and 669 (87.7%) females. Educators who reported taking 11 or more sick days in the previous academic year were at least three times more likely to exhibit high stress, emotional exhaustion, likely GAD, low resilience, and likely MDD than educators with no sick days during the preceding year. Similarly, educators with 11 or more sick days had significantly higher mean scores on the GAD-7 scale, the PHQ-9 scale, the PSS-10, the MBI Emotional Exhaustion subscale, and the MBI Depersonalization subscale than those with zero sick days. Conclusion: This study demonstrates a significant association between sick days and the prevalence and severity of high stress, low resilience, burnout, anxiety, and depression among educators. Short-term sick leave can escalate into long-term absences without adequate support for teachers. Governments and policymakers in the education sector must foster a supportive environment that enables teachers to thrive and effectively perform their professional role without taking prolonged sick days, which can undermine student learning and achievement.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".