Evaluating the mental health status, help-seeking behaviors, and coping strategies of Canadian essential workers versus non-essential workers during COVID-19: a longitudinal study
Bibliographic record
Abstract
OBJECTIVE: This study examined mental health symptoms, help-seeking, and coping differences between Canadian essential workers (EWs) versus non-EWs, as well as common COVID-related concerns and longitudinal predictors of mental health symptoms among EWs only. DESIGN: = 821; RR = 53.7%). METHODS: Cross tabulations and chi-square analyses examined sociodemographic, mental health, and coping differences between EWs and non-EWs. Frequencies evaluated common COVID-related concerns. Linear regression analyses examined associations between baseline measures with mental health symptoms six months later amongst EWs. RESULTS: EWs reported fewer mental health symptoms and avoidance coping than non-EWs, and were most concerned with transmitting COVID-19. Both groups reported similar patterns of help-seeking. Longitudinal correlates of anxiety and perceived stress symptoms among EWs included age, marital status, household income, accessing a psychologist, avoidant coping, and higher COVID-19-related distress. CONCLUSIONS: COVID-19 has had a substantial impact on the mental health of Canadian EWs. This research identifies which EWs are at greater risk of developing mental disorders, and may further guide the development of pandemic-related interventions for these workers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".