Labor Market Integration of High-Skilled Immigrants in Canada: Employment Patterns of International Medical Graduates in Alternative Jobs
Bibliographic record
Abstract
Background: International medical graduates (IMGs) in Canada are individuals who received their medical education and training outside Canada. They undergo a complex licensing procedure in their host country and compete for limited opportunities available to become practicing physicians. Many of them cannot succeed or do not have the resources or interest to undergo this complex and unpredictable career pathway and seek alternative career options. In this study, we aimed to understand how IMGs integrate into the alternative job market, their demographic characteristics, and the types of jobs they undertake after moving to Canada. Methods: An anonymous cross-sectional, online, nationwide, and open survey was conducted among IMGs in Canada. In addition to demographic information, the questionnaire included information on employment status, types of jobs, professional experience, and level of medical education and practice (e.g., specialties, subspecialties, etc.). We conducted a survey of 1740 IMGs in total; however, we excluded responses from those IMGs who are currently working in a clinical setting, thus limiting the number of responses to 1497. Results: Of the respondents, 43.19% were employed and 56.81% were unemployed. Employed participants were more likely to be older males, have stayed longer in Canada, and had more senior-level job experience before moving to Canada. We also observed that the more years that had passed after graduation, the higher the likelihood of being employed. The majority of the IMGs were employed in health-related nonregulated jobs (50.45%). The results were consistent across other demographic characteristics, including different provinces, countries of origin, gender, time since graduation, and length of stay in Canada. Conclusions: This study found that certain groups of IMGs, such as young females, recent immigrants, recent graduates, and less experienced IMGs had a higher likelihood of being unemployed. These findings will inform policymakers, immigrant and professional service organizations, and researchers working for human resources and professional integration of skilled migrants to develop programs and improve policies to facilitate the employment of IMGs through alternative careers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".