An informed transition? International medical graduates settling in the united states and canada
Bibliographic record
Abstract
ABSTRACT International medical graduates (IMGs) are medical professionals who have immigrated to the United States (US) or Canada (Ca) in hopes of integrating into the labor market. IMGs can be a very helpful resource supplying a diverse background and expertise to the medical system in the host country [Chen et al., ]. However, immigration and integration into a new country can be difficult processes due to differences in cultural norms, information sources, and information dissemination. In this study, we investigate the nature of information in the lived experiences of IMGs as they make a new life for themselves and their families in either the US or Canada. By so doing, we contribute to the limited body of research on this population by providing an informational perspective. Semi‐structured interviews were conducted with 20 IMGs residing in the US or Canada. Our findings indicate that IMGs spend an inordinate amount of time searching for occupational and employment‐related information, which includes information about retraining and residency programs, along with varied strategies to make sense of the new information landscapes. IMGs use various strategies to identify signposts and become conversant in the new landscape. Despite the limited sample, it becomes clear that one's ability to become literate in these new information environments leads to more positive outcomes (i.e., integrating the labor market, overall well‐being, belonging).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".