Developing the evidentiary basis for family medicine in the global context: The Besrour Papers: a series on the state of family medicine in the world.
Bibliographic record
Abstract
OBJECTIVE: To provide an overview of the main methodologic challenges to finding definitive evidence of the positive effects of family medicine and family medicine training on a global scale. COMPOSITION OF THE COMMITTEE: In 2012, 2013, and 2014, the College of Family Physicians of Canada hosted the Besrour Conferences to reflect on its role in advancing the discipline of family medicine globally. The Besrour Papers Working Group, which was struck at the 2013 conference, was tasked with developing a series of papers to highlight the key issues, lessons learned, and outcomes emerging from the various activities of the Besrour collaboration. The working group comprised members of various academic departments of family medicine in Canada and abroad who attended the conferences. METHODS: We performed a scoping review to determine the methodologic obstacles to understanding the positive effects of family medicine globally. REPORT: The main obstacle to evaluating family medicine globally is that one of its core dimensions and assets is its local adaptability. Family medicine takes on very different roles in different health systems, making aggregation of data difficult. In many countries family medicine competes with other disciplines rather than performing a gatekeeping role. Further, most research that has been conducted thus far comes from industrialized contexts, and patient continuity and its benefits might not be achievable in the short term in developing countries when clinical demands are great. We must find frameworks to permit strengthening the evidentiary basis of the discipline across different contexts without sacrificing its beneficial adaptability. CONCLUSION: We believe that developing family medicine and its attributes is one of the keys to achieving global health. These attributes—including its comprehensiveness, adaptability, and attention to both local and patient needs—are key to advancing global health priorities, but make common evaluative frameworks for the discipline a challenge. The spread of family medicine over the past decades is indirect evidence of its utility, but we need to generate more evidence. We present some of the initial challenges to a broader and more rigorous evaluative framework.
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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 | 0.141 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".