Canadian family physicians' intentions to migrate: associated factors.
Bibliographic record
Abstract
OBJECTIVE: To ascertain the short-term intentions of Canadian clinically active family physicians (CAFPs) to change their practice locations. DESIGN: Secondary analysis of the 2004 National Physician Survey (NPS) data. SETTING: Canada. PARTICIPANTS: All Canadian CAFPs who responded to the 2004 NPS survey. MAIN OUTCOME MEASURES: Physicians' self-reported intentions to move their practice locations to other provinces or other countries. Variables included age, sex, marital status, having children, professional satisfaction, practice region (British Columbia, Alberta, the Prairies [Saskatchewan and Manitoba], Ontario, Quebec, or the Atlantic Provinces) and work setting (urban, small town, rural, etc). Logistic and regression tree analyses were used to find predictors of intention to move out of province. RESULTS: The 2004 NPS was completed by 21 296 physicians, 11 041 of whom were family physicians. Of these, 8537 satisfied our study inclusion criteria. A total of 3.6% of those CAFPs planned to relocate their practices to other provinces and 3.0% planned to relocate to other countries within the next 2 years (from the time of the survey). Practising in the Prairies and, to a lesser extent, in the Atlantic Provinces were the most powerful predictors of planned interprovincial migration. Dissatisfaction with professional life was the most powerful predictor of planning migration abroad as well as being a predictor of planned interprovincial migration. Other common and statistically significant predictors of interprovincial migration and migration abroad were age, sex, and marital status. CONCLUSION: Patients in the Prairie and Atlantic regions are at greater risk of having their family physicians migrate to other provinces than those in British Columbia, Ontario, and Quebec are. As interprovincial migration profiles differ according to region of practice, they could be used by provincial health human resource planners to understand and predict the movement of health care workers out of their respective provinces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".