Alternative career pathway decision-support job database for international medical graduates in Canada
Bibliographic record
Abstract
OBJECTIVES: Canadian regulations have made it challenging for the international medical graduates (IMGs) to get jobs in their original profession as physicians. Consequently, alternative careers are gaining interest among IMGs to avoid underemployment or unemployment. We conducted research to identify the factors that IMGs consider for taking up an alternative career in Canada. Based on those understandings, we aimed to create a database where information about health-related alternative jobs is presented in a searchable way, which can aid IMGs' strategic job search. DATA DESCRIPTION: We first determined job searching preferences and constraints for IMGs regarding alternative career through focus groups. We used their preferred and constraining factors for collecting job-specific information through systematically reviewing job advertisements. Using this information, we created a database that contains available alternative career pathways for IMGs living in Canada. In total, we have identified 1374 job titles under 192 unique job categories comprising 47 National Occupational Classification (NOC) codes that could be suitable for IMGs seeking an alternative career based on their own short, intermediate, and long-term career goals. We expect that this database will help IMGs in deciding on 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".