Exploring how immigrant international medical graduates successfully manage complex sociocultural challenges
Bibliographic record
Abstract
Background: While immigrant international medical graduates (I-IMGs) contribute significantly to the physician workforce in North America, researchers have highlighted the myriad of ways sociocultural challenges can negatively impact their success. Conceptual understanding that unpacks the complex processes of how I-IMGs effectively manage sociocultural challenges is relatively sparse. In addressing this critical knowledge gap, this study explored how I-IMGs successfully manage sociocultural differences as postgraduate residents. Methods: We interviewed eleven I-IMGs from diverse backgrounds who are in training or recently trained in a distributed multi-site postgraduate medical training program in Canada. We used the lens of sociocultural learning theory to gain insights into the processes of how I-IMGs describe successful management of sociocultural challenges. Results: The overarching storyline of participants emphasized that their experiences were humbling as they grappled with inner struggles, emotions, and vulnerabilities while embracing the ambiguity of not knowing what was expected of them. The following dominant themes from their narratives encapsulate the salient processes for how I-IMGs conceptualize and successfully manage sociocultural challenges: 1) successfully navigating transitions; 2) resisting or altering elements of prior sociocultural norms while embracing the new; 3) living and being in community and having supportive social networks; 4) risk taking to self-advocate and actively seek help. Conclusion: Understanding the strengths and positive strategies for how I-IMGs interface with complex sociocultural challenges has application for medical training institutions. Our insights suggest the need for practical, effective, and continuous assistance within I-IMG training programs to better support future trainees dealing with sociocultural challenges.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".