“Brain drain” and “brain waste”: experiences of international medical graduates in Ontario
Bibliographic record
Abstract
Aisha Lofters,1–4 Morgan Slater,2 Nishit Fumakia,2 Naomi Thulien51Department of Family and Community Medicine, University of Toronto, Toronto; 2Department of Family and Community Medicine, St Michael's Hospital, Toronto; 3Centre for Research on Inner City Health, The Keenan Research Centre in the Li Ka Shing Knowledge Institute of St Michael's Hospital, Toronto; 4Canadian Institutes of Health Research Strategic Training Fellowship, Transdisciplinary Understanding and Training on Research – Primary Health Care Program, London; 5Lawrence S Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, CanadaBackground: “Brain drain” is a colloquial term used to describe the migration of health care workers from low-income and middle-income countries to higher-income countries. The consequences of this migration can be significant for donor countries where physician densities are already low. In addition, a significant number of migrating physicians fall victim to “brain waste” upon arrival in higher-income countries, with their skills either underutilized or not utilized at all. In order to better understand the phenomena of brain drain and brain waste, we conducted an anonymous online survey of international medical graduates (IMGs) from low-income and middle-income countries who were actively pursuing a medical residency position in Ontario, Canada.Methods: Approximately 6,000 physicians were contacted by email and asked to fill out an online survey consisting of closed-ended and open-ended questions. The data collected were analyzed using both descriptive statistics and a thematic analysis approach.Results: A total of 483 IMGs responded to our survey and 462 were eligible for participation. Many were older physicians who had spent a considerable amount of time and money trying to obtain a medical residency position. The top five reasons for respondents choosing to emigrate from their home country were: socioeconomic or political situations in their home countries; better education for children; concerns about where to raise children; quality of facilities and equipment; and opportunities for professional advancement. These same reasons were the top five reasons given for choosing to immigrate to Canada. Themes that emerged from the qualitative responses pertaining to brain waste included feelings of anger, shame, desperation, and regret.Conclusion: Respondents overwhelmingly held the view that there are not enough residency positions available in Ontario and that this information is not clearly communicated to incoming IMGs. Brain waste appears common among IMGs who immigrate to Canada and should be made a priority for Canadian policy-makers.Keywords: global health, human resources
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".