Strengthening Primary Care Through Family Medicine Around the World
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is a limited evidentiary base on the development of family medicine in different contexts and countries. The lack of evidence impedes our ability to compare and characterize family medicine models and identify areas of success that have led to the effective provision of care. This paper offers a comparative compilation and analysis of the development of family medicine training programs in seven countries: Brazil, Canada, Ethiopia, Haiti, Indonesia, Kenya, and Mali. METHODS: Using qualitative case studies, this paper examines the process of developing family medicine programs, including enabling strategies and barriers, and shared lessons. An appreciative inquiry framework and complex adaptive systems thinking inform our qualitative study. RESULTS: Committed partnerships, the contribution of champions, health policy, and adaptability were identified as key enablers in all seven case studies. The case studies further reveal that some enablers were more salient in certain contexts as compared to others, and that it is the interaction of enablers that is crucial for understanding how and why initiatives succeeded. The barriers that emerged across the seven case studies include: (1) resistance from other medical specialties, (2) lack of resources and capabilities, (3) difficulty in sustaining support of champions, and (4) challenges in brokering effective partnerships. CONCLUSIONS: A key insight from this study is that the implementation of family medicine is nonlinear, dynamic, and complex. The findings of this comparative analysis offer insights and strategies that can inform the design and development of family medicine programs elsewhere.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it