Exploring the impact of rural health system factors on physician burnout: a mixed-methods study in Northern Canada
Bibliographic record
Abstract
BACKGROUND: Burnout among physicians is a consequence of chronic occupational stresses and emotionally intense work demands. However, much of the evidence exploring burnout is derived from urban settings and may not reflect the work and social contexts of physicians in Indigenous communities or in rural and resource-constrained areas. We sought to characterize health system factors that influence burnout among physicians practicing in the three northern territories of Canada. METHODS: We conducted a mixed-methods study that included an online survey and qualitative interviews with physicians practicing in Nunavut, Northwest Territories, or Yukon in 2019. The survey adapted content from the Maslach Burnout Inventory. Results were analyzed with logistic regression to assess the association between health system factors and burnout. We conducted in-depth interviews with 14 physicians. Qualitative data was coded and analyzed for themes using the ATLAS.ti software. RESULTS: Thirty-nine percent of survey respondents (n = 22/57) showed features associated with burnout. Factors associated with burnout included use of electronic medical records (β = - 0.7, p < .05), inadequate financial remuneration (β = - 1.0, p < .05), and cross-cultural issues (β = - 1.1, p < .05). Qualitative analysis further identified physician perceptions of lack of influence over health system policies, systemic failures in cultural safety, discontinuity of care, administrative burden, and physician turnover as important drivers of burnout. CONCLUSIONS: Physicians practicing in northern regions in Canada experience stress and burnout related to health system factors and cross-cultural issues. The relationship between cross-cultural issues and burnout has not previously been reported. This work may have implications for physician wellbeing and workforce attrition in other resource-constrained or culturally diverse clinical settings.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 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".