You’re doing everything you possibly could do, and you know it’s not enough”: Family physician narratives of moral distress
Bibliographic record
Abstract
Context: Family physicians working with patients experiencing inequities have witnessed their patients’ health care needs proliferate during the pandemic. This increase in patient need, in addition to increased workload demands rooted in current remuneration structures, has led to proliferating reports of family physician burnout and possible experiences of moral distress. The moral distress of physicians who cannot provide adequate care due to systemic deficits is not foregrounded in contemporary discussions about health care access and quality. Objective: The purpose of this study was to understand how family physicians describe their experiences of moral distress in providing care to patients affected by health needs related to social inequities. Study Design and Analysis: This study was a critical narrative inquiry informed by the analytic lens of moral distress. Setting: This research was conducted with family physicians working across Ontario, Canada. Population Studied: Family physicians who identify as working with patients experiencing health needs related to social inequities. Intervention/Instrument: Each participant was invited to participate in two unstructured narrative interviews. Outcome Measures: Physician narratives of moral distress in providing care to patients affected by health needs related to social inequities. Results: Twenty family physicians were recruited, and their stories of moral distress were linked to policies governing physician remuneration, scope of practice, and the availability of social welfare programs, as these structural elements rendered them unable to get patients the supports and resources they need. Conclusions: Family physician stories of moral distress were in relation to structural and systemic factors such as racism, colonialism, and drug, mental health, and housing policy. This finding provides impetus for critically interrogating how health and social welfare systems must be reformed to both improve the health of patients, and to improve the professional quality of life for family physicians in an effort to increase retention and recruitment.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".