The role of racial and socioeconomic status bias on medication prescribing practices: a qualitative study of family physician perspectives
Bibliographic record
Abstract
BACKGROUND: Physicians' prescribing practices can be influenced by racial and socioeconomic status (SES) biases in a manner that impacts the well-being of patients. Little is known about this issue in the Canadian context. In this study, we set out to understand how family physicians perceive the role of racial and SES bias in prescription conversations and prescribing decisions for patients who have chronic diseases. METHODS: In this qualitative study, we recruited family physicians working at hospital-affiliated family medicine clinics in Toronto. Recruitment occurred through department-wide emails sent by their colleagues who were co-investigators in the study. Participants included men and women, and those who self-identified as minority racialized groups. Semi-structured in-depth interviews explored participant thoughts about racial and SES bias in prescribing practices. Participants were also presented with US-based studies on this topic and asked to reflect about the Canadian context. Interviews and field notes were recorded, transcribed verbatim, and coded. The research team interpreted and analyzed the data using a combined inductive and deductive approach. RESULTS: Thirteen family physicians ranging in age (25 to 50 years) and years of practice (> 1 to < 25) were recruited. Most participants acknowledged awareness of their own bias; when asked, only a few discussed treating all patients equally irrespective of race. Participants perceived bias to affect their prescribing practices both positively and negatively. They explained bias through the lens of power differentials and cultural differences, emphasizing how a lack of diversity among family physicians exacerbates racial and SES bias. CONCLUSIONS: Understanding the influence of bias from physicians' perspective might help improve prescription practices for patients with chronic diseases and make care more equitable or fair for racialized people, people with lower SES, and individuals at the intersection of these social locations. These findings highlight the need for targeted interventions, such as increased diversity in medical practice, to promote more equitable prescribing practices and improve patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".