Changes in emotional distress among Ontario education workers during the COVID-19 pandemic: 2021–2023
Bibliographic record
Abstract
INTRODUCTION: Education workers experienced increased stress during the COVID-19 pandemic adapting to changing work locations, workload, and pedagogical approaches as well as dealing with pandemic-induced personal life stress. METHODS: The goal of this Canadian prospective cohort study was to determine whether levels of distress, as measured by the Kessler Psychological Distress Scale (K10), varied significantly over the course of the study (February 18, 2021 to December 22, 2023) among Ontario education workers after adjusting for demographic, work-related, and temporal factors. Exposure data were collected at enrollment and updated annually while vaccination and illness surveys were completed as needed. The K10 was completed periodically throughout the study. Linear mixed effects models were used to assess factors associated with changes in K10 scores. RESULTS: On average, K10 scores fell 0.5% every four weeks over the 34-month long study. However, the mean score, that decreased from 23.1 at study start to 19.3 by study end, remained above the cut-off of 15, indicating no ongoing emotional distress. Lower distress scores were also associated with non-winter seasons, older age, being male, and being in very good/excellent health. Alternatively, higher K10 scores were associated with being on medications to reduce anxiety, depression, or sleeping problems, return-to-workplace periods after school lockdowns, and periods of intense non-pharmacological interventions. CONCLUSION: Early identification of people most likely to experience distress is needed so that stress remediation strategies can be quickly implemented. Younger, female education workers with lower rated subjective health, who are taking medications to reduce anxiety, depression, or sleeping problems are likely to be at the highest risk. Recognizing periods that intensify stressful situations, such as pandemics and intervals within them, is important to anticipate the need for assistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".