System-Based Interventions to Address Physician Burnout: A Qualitative Study of Canadian Family Physicians’ Experiences During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Medical professionals experienced high rates of burnout and moral distress during the COVID-19 pandemic. In Canada, burnout has been linked to a growing number of family physicians (FPs) leaving the workforce, increasing the number of patients without access to a regular doctor. This study explores the different factors that impacted FPs' experience with burnout and moral distress during the pandemic, with the goal of identifying system-based interventions aimed at supporting FP well-being and improving retention. METHODS: We conducted semi-structured qualitative interviews with FPs across four health regions in Canada. Participants were asked about the roles they assumed during different stages of the pandemic, and they were also encouraged to describe their well-being, including relevant supports and barriers. We used thematic analysis to examine themes relating to FP mental health and well-being. RESULTS: We interviewed 68 FPs across the four health regions. We identified two overarching themes related to moral distress and burnout: (1) inability to provide appropriate care, and (2) system-related stressors and buffers of burnout. FPs expressed concern about the quality of care their patients were able to receive during the pandemic, citing instances where pandemic restrictions limited their ability to access critical preventative and diagnostic services. Participants also described four factors that alleviated or exacerbated feelings of burnout, including: (1) workload, (2) payment model, (3) locum coverage, and (4) team and peer support. CONCLUSION: The COVID-19 pandemic limited FPs' ability to provide quality care to patients, and contributed to increased moral distress and burnout. These findings highlight the importance of implementing system-wide interventions to improve FP well-being during public health emergencies. These could include the expansion of interprofessional team-based models of care, alternate remuneration models for primary care (ie, non-fee-for-service), organized locum programs, and the availability of short-term insurance programs to cover fixed practice operating costs.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".