Bibliographic record
Abstract
Introduction: Although data on new graduates are available and typically included in the health workforce planning (HWP) model, information on their interprovincial migration pattern is less known. This paper aims to understand the mobility pattern of recent healthcare graduates - family physicians and regulated nurses - across the different Canadian jurisdictions. Methodology: Health workforce data from the Canadian Institute for Health Information (CIHI) were used to identify recent family physician and regulated nurse graduates. We identified new graduates (between 2015 and 2019) in a particular province and distributed them according to the province/territory in which they registered to practise. Results: The jurisdiction where they are trained is a key factor in determining their migration rates. For both professions, Ontario and British Columbia have the lowest rates of new graduate out-migration and the highest rates of in-migration, leaving them with a positive net interprovincial migration. Discussion: This analysis can be used to inform better HWP at the jurisdictional level in these professions. Conclusion: Working and community conditions matter to keep and attract new graduates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".