Identifying risk factors for burnout-driven turnover in Canadian healthcare workers during the Covid-19 pandemic
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: The COVID-19 pandemic has tested health systems worldwide, exposing significant weaknesses and vulnerabilities, particularly its toll on healthcare workers (HCWs). This study aimed to identify risk factors leading Canadian HCWs to consider leaving their positions due to stress or burnout during the pandemic. METHODS: Data from the 2022 Survey on Healthcare Workers' Experiences During the Pandemic (SHCWEP) were analyzed using the Shanafelt and Noseworthy (2017) framework. We hypothesized that factors such as workload, work-life balance, resource availability, social and community support at work, and job environment-including organizational culture, values, and flexibility-could influence HCWs' intentions to leave due to stress or burnout. Multivariable logistic regression models were employed to identify significant risk factors for each HCW group. RESULTS: The SHCWEP survey had a 54.9% response rate, with 12,139 HCWs participating. Of these, 3,034 HCWs (25%) expressed an intention to leave their current job, and within this group, 1,350 cited stress or burnout as their reason, representing 11% of the total participants and 44% of those intending to leave. Factors associated with HCWs considering leaving due to stress and burnout included being younger to middle-age, increased workload, longer working hours, financial difficulties, conflicts with colleagues or management, non-adherence to PPE/IPC protocols, and lack of professional emotional support. CONCLUSION: The findings underscore systemic issues exacerbated by the pandemic, highlighting the need for targeted interventions to address workload, organizational culture, and emotional support to mitigate stress and burnout and improve healthcare worker retention.
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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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".