International Medical Graduates Who Have Been Disciplined: Further Causes and Methods to Improve Quality of Care
Bibliographic record
Abstract
To the Editor: The article by Dr. Alam and colleagues1 based on a comparison of the rate and nature of offences that received disciplinary action between North American medical graduates (NAMGs) and international medical graduates (IMGs) was of interest to me as an IMG. The authors offered an explanation for causal criteria of disciplinary actions, including discrimination, language barriers, and cultural differences, all of which include certain variability. Other contributing factors not discussed by Dr. Alam and colleagues could include socioeconomic criteria, which could be due to transition periods characterized by no income, no housing, a totally new environment, isolation, and other challenges faced by IMGs,2 which could cause mental stress leading to poor performance and, ultimately, disciplinary action. Additional issues such as heavy workload, which can lead to adverse outcomes, like emotional exhaustion, physical fatigue, and cognitive weariness, affect IMGs and NAMGs alike and can negatively affect quality of care.3 Considering the limitations of the data, the authors were unable to determine when the physicians in their sample began practicing in Canada. Further, there was no discussion of information bias; that is, Alam and colleagues did not explain the main causes of the disciplinary ac tions, nor any actions IMGs took to remedy the situations. It would have been helpful to report location of training, which could have influenced the results. If physicians were trained according to Canadian requirements, that might affect the findings as well. Other studies have described confounding adjustments for disciplinary actions. Newly qualified NAMGs and IMGs alike improve their skills through the organizational culture of training environment along with regular training, as they are skilled in medicine but unable to look after patients’ safety and care.4 The standard of care can get better through different strategies like orientation programs, through which practical challenges are overcome for both IMGs and NAMGs.5 Additionally, mentorship to all graduates through teaching, supervision, guidance, and regular performance assessment allows IMGs to integrate more easily into their new communities,6 which alleviates transitional challenges. Particular courses designed to meet the needs of IMGs with respect to overcoming barriers like language, culture, socialization, and hospital environment can also help.7 Alam and colleagues should have explored these and other potential contributors to their findings. Sadia Hyder, MScResearch student, Memorial University of Newfoundland Faculty of Medicine, St. John’s, Newfoundland, Canada; [email protected]
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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 | 0.015 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".