In Reply to Fyfe and Douglass
Bibliographic record
Abstract
We thank Drs. Fyfe and Douglass for their comments on our Perspective. The authors have pinpointed an important issue that has plagued medical school directors as well as anyone interested in the physician shortages in underserved settings, particularly in rural locations. Certainly, the finding that dissatisfaction (due to stress and family considerations) increases the likelihood of leaving rural practices is an issue of concern.1,2 However, we would argue that we still need to recruit not only from but also for setting. As there has been a documented inadequacy in the number of rural and culturally diverse applicants to many medical schools,3 we simply do not have sufficient numbers to determine whether those physicians who are unhappy with their rural placement were actually recruited from that area to be for that area. To try to unravel this problem of achieving a truly diverse and equitable workforce with a high degree of job satisfaction, we need to understand the distinction between the physicians from a privileged background of higher socioeconomic status and urbanism who were incentivized to practice in an underserved setting and those who were successful in coming from the targeted setting and are now working for that setting. What are their career expectations and satisfaction? How much influence did the urban medical schools have in influencing those expectations? There is good evidence that rural-born physicians are more than 4 times as likely to practice in rural areas and physicians who train at rural medical schools or who have rural training experiences are more likely to practice in rural settings.4,5 As we move forward to address this key doctor shortage and training research agenda, these will be priority questions. We thank Drs. Fyfe and Douglass for bringing a crucial nuance to this fundamental problem. Melanie Raffoul, MDAttending physician, assistant professor, Department of Emergency Medicine, and assistant medical director, Tisch Observation/Short Stay Unit, NYU Langone Health, New York, New York. At the time of writing, she was health policy fellow, Robert Graham Center, Washington, DC; [email protected]Gillian Bartlett-Esquilant, PhDProfessor and associate chair research director, Department of Family Medicine, McGill University, Montreal, Quebec.Robert L. Phillips, MD, MSPHExecutive director, Center for Professionalism and Value in Health Care, Washington, DC.
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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 | 0.014 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.046 | 0.062 |
| Insufficient payload (model declined to judge) | 0.007 | 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".