Re-thinking Integration of Foreign Immigrant Medical Doctors in Postgraduate Medical Education
Bibliographic record
Abstract
This presentation is based on a research study of CaRMS-eligible “immigrant” medical doctors (known as IMGs) from racialized background, and their experiences with securing a medical license in Canada. As an Administrative Assistant in postgraduate residency education for 20 years (1988-2008), involved in CaRMS recruitment, interviewing, rotation scheduling, evaluation, and accreditation, I saw the challenges from both the IMGs and the medical residency perspectives. One question that lingered almost after each CaRMS match was, “what would these highly qualified IMGs do if they cannot practice medicine? I also thought about the conversations I had with my parents, their elderly friends, other immigrants in my community who expressed fear of going to the doctor because their English was poor and doctors did not seem to understand their creole description and hand gestures of their pain, symptoms and illnesses. Sustaining health and equitable access to healthcare is a global threat that leaders and politicians in every continent struggle with. As thousands of new immigrant doctors arrive in Canada each year, draining their country of precious human resources, nearly six million people in Canada do not have a family doctor. As Canada plans to welcome 500,000 immigrants per year by 2025, each of whom will require a doctor in a dire doctor-shortage situation, thousands of IMGs struggle to survive and hold on to their life-long passion and career. The famous Martin Luther King said, “of all the forms of inequality, injustice in health is the most shocking and the most inhuman.” This qualitative research study with 15 immigrant doctors from 13 different non-English speaking countries and between the ages of 35-55+, asked the following questions: What motivates you to immigrate to Canada? How does denial of medical recertification impact your professional, personal and social life? What recommendations do you have for the medical profession? Using a purposeful and snowball sampling, face-to-face 1:1 interview with open-ended questions and grounded theory framework, the results show that professional career and caring for the sick were foremost for IMGs. This presentation will discuss alternative options to transform the waste of IMGs’ human capital into supporting equitable care to the vastly diverse and multi-ethnic patient population in Canada.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".