“I get to know them as a whole person”: family physician stories of proximity to patients experiencing social inequity
Bibliographic record
Abstract
Canadians’ health outcomes are inextricably tied to social inequities. While family medicine is aptly situated to provide care that addresses social factors through longitudinal knowledge of patients and their contexts, family physicians have come under increased pressure to do more for their patients with less time and resources due to financial and resource demands within primary care. Nursing scholar Ruth Malone has argued that remaining proximal, or close to patients, is a form of resistance to these demands. Using a critical narrative methodology, we conducted 36 interviews with 20 family physicians working with persons experiencing health needs related to social inequity in Ontario, Canada, whose stories expressed and expanded upon Malone’s proximity. Notions of proximity were invoked through descriptions of the role of family physicians in: i) generating physical proximity based on the patients’ needs for more time, space, and care; ii) developing narrative proximity through storytelling over time, both between colleagues and patient communities; and iii) engaging in moral proximity, or recognizing the vulnerabilities of their patients, by going “above and beyond” in their care and advocacy roles inspired by the needs of their patients. The findings add theoretical depth to proximity, extending this conceptualization into a new clinical context. These stories also complement current health services and health policy research that advocates for collaborative primary care approaches, as elements of these approaches are conducive to establishing proximity with patients who need care the most.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.027 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| 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".