Bibliographic record
Abstract
Riding concerns about physician shortages, Canadian medical schools have expanded their annual enrolment by 80% over the last 13 years.Including rapid growth in net immigration, the annual "crop" has nearly doubled.(Population has grown by about 14%.)The physician workforce is just now showing the impact -a 6% increase in physicians per capita in the last three years.In the last decade, medical expenditure per physician has also risen, by nearly 35% above general inflation.The drivers are rapidly increasing diagnostic testing and imaging, and the growth of alternative payment programs.more doctors, combined with growing expenditure per doctor, will have serious cost implications.What benefits are we buying?Résumé face aux préoccupations en termes de pénuries de médecins, les écoles de médecine du Canada ont augmenté de 80 pour cent leurs admissions annuelles au cours des 13 dernières années.En tenant compte de la croissance rapide de l'immigration nette, la « récolte annuelle » a presque doublé.(La croissance de la population a été d' environ 14 pour cent.)On commence à peine à en voir l'impact dans la main-d' œuvre médicale -une croissance de 6 pour cent de médecin par habitant pour les trois dernières années.Au cours des dix dernières années, les dépenses par médecin ont également augmenté de près de 35 pour cent au-dessus du taux The Sorcerer' s ApprenticesLes apprentis sorciers
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.235 | 0.091 |
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".