Medical Workforce Policy-Making in Canada, 1993???2003: Reconnecting the Disconnected
Bibliographic record
Abstract
The authors set out to review Canadian medical workforce policies for 1993 to 2003 and assess if data existed in the 1990s that could have reversed the policy decision to curtail the supply of physicians from Canada's medical schools just as Canada was about to experience a developing shortage. The authors reviewed existing descriptive data sources regarding Canadian physician workforce size and activity from 1986 to 2003, including the Canadian Medical Association workforce database. The review indicated that a significant loss of physicians to retirement was imminent. Physician workforce productivity had started to fall by the early 1990s. Emigration to the United States had risen above traditional levels in the early 1990s and remained higher into the late 1990s. Despite these existing findings, an integrated adjustment to physician workforce policies taken in 1993-94 only occurred after 1999. The authors recommend that policy makers and managers must monitor the numbers from existing sources. To optimize these sources, planned data tracking and linkages are essential. The period in question demonstrated major disconnects in coordinating implementation, wherein subject experts monitoring data trends were not adequately utilized by policy makers. Finally, in complex systems with regional differences, policy decisions based on normative data are insufficient.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".