EDITORIAL What Do Family Medicine Residency Graduates Do?
Bibliographic record
Abstract
Physician workforce policy is a challenging arena, littered over the years with various mispro-nouncements. At the moment, there seems to be agreement that the United States has a surplus of physicians and too many specialist physicians. I There is less certainty about the primary care physician supply, with some suggesting serious shortages and others suggesting we already have about the right number.2 Regardless of whether we have the right number of primary care physi-cians, we know we have not solved distribution problems of the existing workforce, specifically neglecting some populations such as rural com-munities.1 Family physicians are unequivocally trained to be primary care clinicians and as a group represent a highly versatile physician ca-pacity, deployable in behalf of improved health care for people of all ages, in all walks of life, and in all types of communities. To plan for balanced health care systems that are more effective than we now have, we need to know more about what family physicians actually do after they complete their training. One quarter of a century after establishing family medicine residencies, it has become possi-ble to describe and analyze at least the first por-tions of the careers of residency-trained family physicians in the United States. The report in this issue of the Journal by West et aP uses the 1991
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".