Bibliographic record
Abstract
Today, medical schools graduate doctors, not physicians. Thousands of doctors who are U.S. citizens and graduates of U.S. and international medical schools will never become physicians because they do not obtain a residency position. Doctors need at least one year of residency to become a licensed physician. However, 4,099 applicants in 2018 and 4,170 in 2019 failed to get a position through the National Resident Matching Program Main Match; about 1,000 students get positions after the Main Match each year. The personal and societal cost is enormous: each year, approximately 3,000 nonphysician doctors cannot use 12,000 education years and three-quarters of a billion dollars they invested in medical education and cannot mitigate the shortfall of 112,000 physicians expected in 2030.To ameliorate this problem, medical schools could guarantee one year of residency. This is affordable: despite federally funded slots being capped, residency positions have increased for 17 consecutive years (20,602 in 2002 to 32,194 in 2019) because residents are cost-effective additions to the workforce. Alternatively, a 3-year curriculum plus required fourth-year primary care residency is another option. The salary during the residency year could equal other first-year residents', or there could be a token amount for this "internship." Both models decrease the cost of medical education; the second financially unburdens the hospital.Since the Flexner Report (when there was no formal postgraduate training), the end point of medical education has moved from readiness for independent medical practice (physician) to readiness for postgraduate training (doctor). To benefit individuals and society, medical education must take steps to ensure that all graduates are physicians, not just doctors.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.046 | 0.036 |
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".