Determinants of burnout in Canadian health care workers during the COVID-19 pandemic
Bibliographic record
Abstract
Background: Health care workers (HCWs) are among the most vulnerable groups to experience burnout during the coronavirus (COVID-19) pandemic. Understanding the risk and protective factors of burnout is crucial in guiding the development of interventions; however, the understanding of burnout determinants in the Canadian HCW population remains limited.Objective: Identify risk and protective factors associated with burnout in Canadian HCWs during the COVID-19 pandemic and evaluate organizational factors as moderators in the relationship between COVID-19 contact and burnout.Methods: Data were drawn from an online longitudinal survey of Canadian HCWs collected between 26 June 2020 and 31 December 2020. Participants completed questions pertaining to their well-being, burnout, workplace support and concerns relating to the COVID-19 pandemic. Baseline data from 1029 HCWs were included in the analysis. Independent samples t-tests and multiple linear regression were used to evaluate factors associated with burnout scores.Results: HCWs in contact with COVID-19 patients showed significantly higher likelihood of probable burnout than HCWs not directly providing care to COVID-19 patients. Fewer years of work experience was associated with a higher likelihood of probable burnout, whereas stronger workplace support, organizational leadership, supervisory leadership, and a favourable ethical climate were associated with a decreased likelihood of probable burnout. Workplace support, organizational leadership, supervisory leadership, and ethical climate did not moderate the associations between contact with COVID-19 patients and burnout.Conclusions: Our findings suggest that HCWs who worked directly with COVID-19 patients, had fewer years of work experience, and perceived poor workplace support, organizational leadership, supervisory leadership and ethical climate were at higher risk of burnout. Ensuring reasonable work hours, adequate support from management, and fostering an ethical work environment are potential organizational-level strategies to maintain HCWs’ 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".