How Medical Schools Can Encourage Students’ Interest in Family Medicine
Bibliographic record
Abstract
The discipline of family medicine is essential to improving quality and reducing the cost of care in an effective health care system. Yet the slow growth of this field has not kept pace with national demand. In their study, Rodríguez and colleagues report on the influence of the social environment and academic discourses on medical students' identification with family medicine in four countries-the United Kingdom, Canada, France, and Spain. They conclude that these factors-the social environment and discursive activity within the medical school-influence students' specialty choices. While the discourses in Canada, France, and Spain were mostly negative, in the United Kingdom, family medicine was considered a prestigious academic discipline, well paying, and with a wide range of practice opportunities. Medical students in the United Kingdom also were exposed early and often to positive family medicine role models.In the United States, academic discourses about family medicine are more akin to those in Canada, France, and Spain. The hidden curriculum includes negative messages about family medicine, and "badmouthing" primary care occurs at many medical schools. National education initiatives highlight the importance of social determinants in medical education and the integration of public health and medicine in practice. Other initiatives expose students to family medicine role models and practice during their undergraduate training and promote primary care practice through new graduate medical education funding models. Together, these initiatives can reduce the negative effects of the social environment and create a more positive discourse about family medicine.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.040 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".