Alternative Career Opportunities Available for Internationally Trained Physicians Living in Canada: An Overview of the Job Profiles
Bibliographic record
Abstract
Background: Internationally trained physicians (ITPs) or international medical graduates (IMGs) living in Canada are those who have received their academic and professional training outside of Canada or the USA. Current rules and regulations have made it extremely difficult for IMGs to get the license to practice in Canada as a physician regardless of their citizenship status and level of training. After going through a highly competitive process, approximately only 15% of Alberta IMG applicants obtain a residency position based on the number of seats allocated to IMGs via AIMGP (263 eligible candidates; 39 seats). This bottleneck situation and the time and cost of re-applying gradually increase frustration among IMGs. Our interest was to look at the employment situation of IMGs in Canada and what viable pathways existed for them if interested in pursuing an alternative career in health. Our research was conducted to identify the factors that IMGs took into consideration when exploring an alternative career and to identify suitable alternative career pathways available for IMGs within Canada in order to advise them accordingly. Methods: To begin with, we conducted a survey on IMGs regarding their interests in and preferences for alternative careers. Keeping the survey results in mind, we searched for job advertisements and systematically reviewed job descriptions and their qualifications (i.e. regulated versus non-regulated). We also conducted focus groups to extract the key decision-making factors for IMGs in order to identify the alternative jobs that match their interest and skills. Results: In total, we have identified 192 unique job positions comprising 47 NOC codes that could be suitable for IMGs seeking to begin an alternative career based on the short, intermediate, and long-term goals. These jobs primarily fall into two different categories: clinical (35.42%) and non-clinical (64.58%) jobs. Interestingly, we have found that around 7.35% of clinical and 29.84% of non-clinical job categories do not require any sort of license or approval from any regulatory bodies. Although most other jobs require a certain level of training, certificate, or license from the respective licensing authorities, obtaining those regulatory approvals is more tangible compared to the license to practice as a physician. Strikingly, there are approximately 17.71%, 51.04%, 29.17% and 2.08% unique categories that fall into the entry, middle, and senior-level job positions. We have further classified these job categories according to the job searching preferences of IMGs and collected information on how to qualify for these jobs. Conclusion: Although more work in this area is needed to integrate IMGs with the job market, we expect that our findings and resources will help IMGs decide on alternative career pathways.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".