Approaches to diversity in family medicine: "I have always tried to be colour blind".
Bibliographic record
Abstract
OBJECTIVE: To explore family physicians' perceptions of and experiences with patient diversity, including differences in sex, race, ethnicity, social class, sexual orientation, and abilities or disabilities. DESIGN: Semistructured, in-depth, qualitative interviews. SETTING Halifax metropolitan region, Nova Scotia. PARTICIPANTS: Twenty-two family physicians who ranged in age (25 to 65 years) and in years of practice (< 5 to > 20). Participants included both sexes, members of racialized minority groups, and those who self-identified as gay, lesbian, or bisexual. METHODS: Physicians were recruited through information letters distributed by mail and through professional networks. Interviews and field notes were recorded, transcribed verbatim, and coded using data analysis software. Weekly team discussions enhanced interpretation and analysis. MAIN FINDINGS: Family physicians employed 5 main approaches to diversity: maintaining that differences do not matter, accommodating sociocultural differences, seeking to better understand differences, seeking to avoid discrimination, and challenging inequities. Quotes from interviews illustrate these themes. CONCLUSION: Most approaches assume that both medicine (as a profession) and physicians are and should be socially and culturally neutral; some acknowledge that the sociocultural background of patients can raise tensions. Most participants in our study seek to treat patients as individuals in order to not stereotype, which hinders recognition of the ways in which sociocultural factors-both patients' and physicians'-influence health and health care. Critical reflexivity demands that physicians understand social relations of power and where they fit within those relations.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.056 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| 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".