Factors Associated with Intent to Leave and Burnout among Canadian Nurses Amidst the COVID-19 Pandemic: A Quantitative Analysis of the Survey on Health Care Workers’ Experiences During the Pandemic
Bibliographic record
Abstract
BackgroundThe increased demands and stressors from the COVID-19 pandemic led to widespread burnout and job stress, prompting concerns about retention rates. This study identifies demographic and occupational predictors of Canadian nurses' intent to leave their jobs due to burnout and job stress during the COVID-19 pandemic.MethodsData was utilized from the Survey on Health Care Workers' Experiences During the Pandemic conducted by Statistics Canada. Multivariate logistic regression models were generated to analyze the associations between demographic and occupational factors and nurses' intent to leave.ResultsA total of 12,246 eligible participants responded to the survey (54.9% response); however, the analysis was restricted to 1138 nurses after excluding participants of other healthcare occupations. Younger nurses were significantly more likely to consider leaving their jobs [OR = 9.95, 95% CI: (5.92-16.73)], as well as nurses living in Alberta [OR = 3.16, 95% CI: (1.58-6.32)] and British Columbia [OR = 3.16, 95% CI: (1.66-6.03)]. Moreover, nurses with less work experience [OR = 3.91, 95 CI = (2.53-6.05)], work in acute care [(OR = 3.31, 95 CI = (1.69-6.51)], experienced changes in workload [OR = 2.69, 95% CI: (1.58-4.57)], had increased work hours [OR = 1.92, 95% CI: (1.27-2.92)], and lacked emotional support [OR = 3.43, 95 CI = (2.31-5.09)] had greater odds of intending to leave.ConclusionThe findings underscore the need for strategies to mitigate stress and burnout among nurses, particularly during public health crises. Implementing measures to address these factors could help improve retention rates and ensure a stable nursing workforce during future pandemics.
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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 | 0.005 |
| 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) | 0.001 | 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".