Why Are Healthcare Providers Leaving Their Jobs? A Convergent Mixed-Methods Investigation of Turnover Intention among Canadian Healthcare Providers during the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Staffing shortages across the healthcare sector pose a threat to the continuity of the Canadian healthcare system in the post-COVID-19 pandemic era. We sought to understand factors associated with turnover intention as well as Canadian healthcare providers' (HCPs) perspectives and experiences with turnover intention as related to both organizational and professional turnover. METHOD: A convergent questionnaire mixed-methods design was employed. Descriptive statistics and ordinal logistic regressions were used to analyze quantitative data and ascertain factors associated with turnover intention. Thematic analysis was used to analyze qualitative open-field textbox data and understand HCPs' perspectives and experiences with turnover intention. RESULTS: Quantitative analyses revealed that 78.6% of HCPs surveyed (N = 398) reported at least a 25% turnover likelihood regarding their organization, with 67.5% reporting at least a 25% turnover likelihood regarding their profession. Whereas regression models revealed the significant impact of years worked, burnout, and organizational support on turnover likelihood for organizations, age, sex, burnout, and organizational support contributed to the likelihood of leaving a profession. Patterns of meaning drawn from participants' qualitative responses were organized according to the following four themes: (1) Content to stay, (2) Drowning and no one cares, (3) Moral stressors, and (4) Wrestling with the costs and benefits. CONCLUSIONS: Many HCPs described weighing the costs and benefits of leaving their organization or profession during the COVID-19 pandemic. Although challenging working conditions, moral stressors, and burnout may play a significant role in HCPs' experiences of turnover intention, there is ample room to intervene with organizational support.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".