One Size Does Not Fit All: Balancing Individual and System Needs in Primary Care and Beyond
Bibliographic record
Abstract
In this issue, Dewan and Norcini invite readers to reconsider the basic minimum standards for independent primary care practice. Their willingness to push boundaries, question turf wars, and suggest innovative ways forward is laudable. Although their piece is timely and provocative, it does not fully consider the interplay between individual and system factors that influence people to pursue different kinds of degrees and practice in this context. In this Invited Commentary, the authors discuss imperatives that are underacknowledged by Dewan and Norcini: the importance of diversity in health system planning; status, power, and privilege; the extension of their argument beyond primary care; the conflation of time in training with competence; and important issues of distribution of health care resources. Ultimately, the authors argue that there may be strength in diversity, one that should not be obscured by attempts to normalize training time.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.080 | 0.054 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".