The “Brain Drain”: Factors influencing physician migration to Canada
Bibliographic record
Abstract
Context: Higher income countries have an average physician density of 300 physicians per 100,000 people. In stark contrast, lower income countries have an average physician density of 17 physicians per 100,000 people. A major cause of this discrepancy is the migration of healthcare professionals from lower income to higher income countries, a phenomenon colloquially known as the “brain drain”. Objective: To explore factors that led International Medical Graduate (IMG) physicians to leave their home countries and migrate to Canada. Methods: An anonymous questionnaire with a mix of open- and close-ended questions was sent to 500 randomly selected IMG physicians practicing in Ontario, Canada. Results were analyzed using a mixed-method design utilizing both descriptive statistics and a thematic analysis approach. Results: 39 physicians met inclusion criteria and completed the survey. The majority were 50 years or older, and over 60% were male. The most common reason for emigration from their home country was the socioeconomic and/or political situation, and the most common reason for selecting Canada was family issues. Suggestions for how brain drain could be stemmed fell into three broad categories: 1) more accurate information about lack of opportunities in Canada, 2) more continuing medical education opportunities in home countries, and 3) address issues such as safety and quality of life in home countries. Conclusions: This survey provides insights into the reasons for emigration and immigration for international medical graduates. The results of this survey can assist stakeholders in working toward appropriate and acceptable solutions to the brain drain.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 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 | 0.000 |
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 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".