Exploring Health-Seeking Behaviors Among Healthcare Workers and the General Population During the COVID-19 Pandemic: A Retrospective Quantitative Study
Bibliographic record
Abstract
Background/objectives: Mental health issues are prevalent among healthcare workers, but help-seeking behavior in this groups remains under-researched. The purpose of this study was to explore predictors of and barriers to mental health help-seeking among healthcare workers in Canada, compared to workers from other sectors. Design: This quantitative study analyzed cross-sectional data from Mental Health Research Canada (MHRC) from October 2022 to January 2024. Methods: The total sample consisted of 8,191 workers from various sectors, including 419 healthcare workers. We examined prevalence of help-seeking, barriers to accessing mental health support, and predictors of help seeking using descriptive and inferential statistics. A multivariate logistic regression analysis was performed to explore the relationship between sociodemographic factors and help-seeking. Results: Healthcare workers were more likely to seek mental help support compared to workers from other sectors (OR 1.73, 95% CI: 1.35, 2.20). Healthcare workers least likely to seek mental health support were male (OR 0.58, CI 0.52, 0.66), residing in Quebec (OR 0.49, 95% CI: 0.41, 0.59), or of older age (OR 0.40, 95% CI: 0.30, 0.52). Key barriers to mental health help-seeking identified among healthcare workers included concerns about exposure to COVID-19 (33%), preference for self-management (25%), concerns about the safety of care options (18%), and lack of knowledge on how or where to seek help (13%). Conclusions: This study provides valuable insight into the barriers and predictors of mental help-seeking behavior among healthcare workers. Findings underscore the need for workplaces to foster safe, supportive, and inclusive environments to better support healthcare workers facing mental health challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".