Family physicians’ moral distress when caring for patients experiencing social inequities: a critical narrative inquiry in primary care
Bibliographic record
Abstract
BACKGROUND: Family physicians (GPs) working with patients experiencing social inequities have witnessed patients' healthcare needs proliferate. Alongside increased workload demands fostered within current remuneration structures, this has generated concerning reports of family physician attrition and possible experiences of moral distress. AIM: To explore stories of moral distress shared by family physicians caring for patients experiencing health needs related to social inequities. DESIGN AND SETTING: A critical narrative inquiry, informed by the analytic lens of moral distress, conducted in Ontario, Canada. METHOD: Twenty family physicians were recruited through purposive and snowball sampling via word of mouth and email mailing lists relevant to addictions and mental health care. Physicians participated in two narrative interviews and had the opportunity to review the interview transcripts. RESULTS: Family physicians' accounts of moral distress were linked to policies governing physician remuneration, scope of practice, and the availability of social welfare programmes. These structural elements left physicians unable to get patients much needed support and resources. CONCLUSION: This study provides evidence that physicians experience moral distress when unable to offer crucial resources to improve the health of patients with complex social needs resulting from structural features of the Canadian health and social welfare system. Further research is needed to critically interrogate how health and social welfare systems around the world can be reformed to improve the health of patients and increase family physicians' professional quality of life, potentially improving retention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".