Bibliographic record
Abstract
Must it always be only in rueful valedictory repentance that the gatekeepers of medical education come to see what they have been doing? —Henry Dicks, 19651 To the Editor: Whitcomb’s2 article on the looming crisis facing graduate medical education (GME) echoes many of the comments that Dicks has made about GME. Dicks, however, was writing not in 2011 or even 2000 but, rather, 1965. Clearly, the problem of developing a GME system to meet the health needs of the population has confounded policy makers for generations. Why is it so difficult, and can we do any better in the future? The simple answer is that it is not a simple problem and that many conflicting forces can have unforeseen and sometimes perverse effects on the system; some of these can take years to take effect. One such force is the financial one. Whitcomb rightly points out the financial disincentives that discourage nonteaching hospitals from developing more GME programs. Perhaps a solution could be found by examining why such GME programs are so expensive. Defining the costs would be a useful first step. Frenk et al3 have pointed out that U.S. and Canadian undergraduate medical education is the most expensive in the world. It would not be surprising if GME in the United States and Canada is similarly expensive. If we define the costs and break them down, it may be that we can identify certain elements that contribute to costs but do not contribute to the competencies required of a fully qualified specialist. In times of economic constraint, federal and state/provincial officials would surely welcome such an approach. Another force affecting GME is the health needs of the population. Here, the needs of the next 40 years are becoming clearer. Patients of the future will be older and will have more chronic diseases; they will need primary care physicians. So it is good news that nonteaching hospitals are driving forward new GME programs in selected specialties such as primary care. Teaching primary care in GME, in both teaching and nonteaching hospitals, is likely to be both more “care-effective” and more cost-effective. Kieran Walsh, FRCPI Editor, BMJ Learning, the medical education service of the BMJ Publishing Group, London, United Kingdom; [email protected]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.020 | 0.040 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".