Bibliographic record
Abstract
PURPOSE: To examine the proportion of Canada's physicians who are foreign-trained (non-Canada, non-US), and to determine if there was a relationship between this number and the net change in physicians of each province as affected by inter-provincial migration. METHODS: Data were obtained from the Canadian Medical Association, based on information contained within the Southam Medical Database of the Canadian Institute for Health Information (1987-2003). Information on the net change in the number of physicians lost or gained due to inter-provincial migration was obtained for each province, as well as the percentage of physicians that are foreign-trained (non-Canada, non-US). A correlation between the net change in physician supply and the proportion of foreign-trained physicians was explored. RESULTS: Foreign-trained physicians comprised from 19% (Prince Edward Island) to 55% (Saskatchewan) of the provincial physician supply. There was a strong linear correlation between the net change in physician supply due to inter-provincial migration and the proportion of foreign-trained physicians (r2 0.546; P=0.0146). DISCUSSION: Canada continued to rely heavily on foreign-trained physicians. This was particularly true for provinces which lost the greatest number of physicians to inter-provincial migration. Such 'poaching' of physicians may have important ramifications for the source countries.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".