Family Practice- World Perspective American And French Family Physicians: A Comparative Profile
Bibliographic record
Abstract
An organization of physicians in France called UNAFORMEC invited 22 academic and private practicing family physicians, 18 from the United States and 4 from Quebec, Canada, to serve as consultants to the organization at a meeting in Paris on 22-24 May 1992. UNAFORMEC, which exists for the purpose of providing and promoting continuing medical education, com-prises representatives from all specialties, al-though 70 percent of the members are general practitioners. We met with our French counter-parts to compare the medical careers and the medical educational systems (undergraduate, graduate, and continuing) in France and the US. Our observations and comparison of the two sys-tems are presented. In recent years French physicians have come to realize that the body of knowledge and skills necessary to serve as the point of access to the medical system for families and to provide con-tinuing comprehensive care go beyond the educa-tion currently attained by the French generalist physician. In short, they are realizing, as did medi-cal educators in our nation in the 196Os, that family practice is a specialty in its own right and thereby requires its own unique graduate medical educa-tion to prepare its specialists for medical practice. Hindered by negative stigmata of several ori-gins (e.g., low pay, low patient volumes, and usu-ally lacking hospital privileges), French general practitioners are undergoing self-examination before formatting a long-range plan to improve
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".