How early-career family physicians integrate social accountability into practice
Bibliographic record
Abstract
OBJECTIVE: To explore how early-career family physicians integrated social accountability into their practices, how it shaped their practice choices, and the challenges they encountered. DESIGN: A secondary analysis of qualitative interview data. SETTING: British Columbia, Ontario, and Nova Scotia. PARTICIPANTS: Early-career family physicians. METHODS: Initially a deductive analysis was conducted using a framework for categorizing 3 different levels of social accountability (individual patient [micro], community [meso], and system [macro]). An inductive analysis was then undertaken to explore how social accountability informs practice choice and to understand challenges encountered. A reflexive thematic analysis guided the inductive process. MAIN FINDINGS: Social accountability was most commonly discussed at individual and community levels, with more limited system-level examples. Many early-career family physicians valued providing holistic care and derived professional satisfaction from meeting patient and community needs. These values, which are consistent with social accountability, informed their choice to pursue medicine and family medicine specifically. Available practice and payment models were described as barriers to socially accountable practice. Participants believed they lacked the knowledge, skills, and power to influence policy. CONCLUSION: There is a need to support practice environments that are conducive to socially accountable practice and for curricula that can provide physicians with tools to engage with community- and system-level policy issues.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".