Choosing Alternative Career Pathways after Immigration: Aspects Internationally Educated Physicians Consider when Narrowing down Non-Physician Career Choices
Bibliographic record
Abstract
Many developed countries admit internationally educated physicians (IEPs) as highly skilled migrants. The majority of IEPs arrive with the intention of becoming licensed physicians to no avail, resulting in underemployment and underutilization of this highly skilled group of people. Alternative careers in the health and wellness sector provide IEPs opportunities to use their skills and reclaim their lost professional identity; however, this path also includes great challenges. In this study, we determined factors that affect IEPs' decisions regarding their choice of alternative jobs. We conducted eight focus groups with 42 IEPs in Canada. Factors affecting IEPs' career decisions were related to their individual situations and tangible aspects of career exploration, including resources and skills. A number of factors were associated with IEPs' personal interests and goals, such as a passion for a particular career, which also varied across participants. Overall, IEPs interested in alternative careers took an adaptive approach, largely influenced by the need to earn a living in a foreign country and accommodate family needs and responsibilities.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".