Recertifying as a doctor in Canada: international medical graduates and the journey from entry to adaptation
Bibliographic record
Abstract
OBJECTIVE: Globalisation and severe doctor shortages in many countries have resulted in increased numbers of international medical graduates (IMGs) in medical training programmes in major recipient countries such as Canada. Much of the literature on IMGs is written from the perspective of the doctor workforce. Less is known about the recertification training experiences of IMGs in recipient countries. This study aims to describe the recertification training experiences of IMGs in Canada in order to help medical training programmes understand how to facilitate the integration of IMGs into recipient medical communities. METHODS: A phenomenological (qualitative) research approach was undertaken for this study. International medical graduates undergoing recertification training in order to practise in Canada were individually interviewed about their experiences. Data collection and analysis followed the procedures of interpretive phenomenology. RESULTS: Twelve IMGs participated. Analysis of the interviews revealed 4 themes that typified IMG recertification training experiences: training entry barriers; and a 3-phase process of loss, disorientation and adaptation. International medical graduates must complete this 3-phase process in order to feel fully integrated into their professional environments. CONCLUSIONS: This study provided a description of IMGs' training experiences during certification for practice in Canada and revealed that these experiences were characterised by a 3-phase process of adjustment. Using this framework, a series of recommendations were proposed for medical training programmes to help IMGs with this process.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".