A Study of Immigrant International Medical Graduates’ Re-Licensing in Ontario: Their Experiences, Reflections, and Recommendations
Bibliographic record
Abstract
This study is about the lived re-licensing experiences of immigrant international physicians in Ontario who are commonly referred to as International Medical Graduates (IMGs). The study had a two-fold purpose, the first purpose was to investigate whether the conclusions of the previous research were still relevant at the time of this study, and the second purpose was to examine the re-licensing experiences of a specific subset of Immigrant IMGs. A comprehensive systematic review identified 12 selected papers on IMG issues (2008 - 2017), and these research papers highlighted that some Immigrant IMGs were successful in becoming re-licensed and working as physicians in Ontario. However, many of them had to make unpalatable choices such as transitioning into other health-related careers, accepting entry-level jobs or returning to their home country due to the systemic and financial barriers, and the lack of training opportunities and residency positions. This study used mixed methods design with two research instruments (survey and individual interviews), two theoretical frameworks (Self-Determination Theory and Transformative Learning Theory), two sampling methods (purposeful sampling and snowball sampling), and two samples of Immigrant IMGs for two research purposes. The 31 participants in the survey were a sample of the heterogeneous Immigrant IMG population in Ontario whereas the four interview participants were a sample of subset Immigrant IMG population in Ontario who had not been researched in the previous literature. The findings of this research verified that the barriers documented in the previous empirical literature still exist. The qualitative component of this research revealed some additional barriers. The interview participants' perceptions of how the embedded systemic barriers disadvantaged them and set them up for failure added new information to the research. Based on the participants’ experiences, reflections and recommendations presented in this research, it seems that the current re-licensing system should undergo a fairness review in the light of Canadian/Ontarian policies of equality, inclusion and human rights to help Immigrant IMGs survive and thrive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".