Alternative Careers toward Job Market Integration: Barriers Faced by International Medical Graduates in Canada
Bibliographic record
Abstract
International Medical Graduates (IMGs), who completed their medical degree and training outside Canada constitute a notable portion of the skilled migrants of the country. However, due to a long and uncertain licensure process and limited opportunities many IMGs look for alternative career pathways where they can utilize their learned skills. Alternative careers in the health and wellness sector may offer such opportunities; however, IMGs' success in these pathways were also less evident despite their high potential. In this study, we investigated the barriers that IMGs stated to face when attempting alternative jobs in Canada. Eight focus groups with 42 IMGs in Canada were conducted. Using a thematic analysis approach, we identified that IMGs encounter these barriers in different stages of their resettlement journey in Canada, including both the pre-migration and post-migration phases. In the pre-migration phase, IMGs were not aware of the success rates of the licensing pathways and did not have sufficient information regarding potential alternative careers. In the post-migration phase, the lack of information continues to affect IMGs where IMGs exhaust their resources pursuing alternative careers without proper guidance and support. Further, IMGs struggle with taking preparation for alternative careers by obtaining further certifications and completing other prerequisites for some barriers, such as financial constraints. While looking for jobs, some IMGs perceived systemic discrimination such as non-recognition of their credentials and experience. Furthermore, the mismatch of expectations and limited growth opportunities offered by potential careers serve to disincentivize IMGs from pursuing an alternative career. Addressing the current employment inequity experienced by IMGs in Canada warrants research collaborations between organizations supporting IMGs and policymakers that target known barriers to the pursuit of alternative careers by IMGs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".