Challenges with international medical graduate selection: finding positive attributes predictive of success in family medicine residency
Bibliographic record
Abstract
BACKGROUND: Criteria to select residents most likely to succeed, other than proficiency of their medical knowledge, is a challenge facing preceptors. International Medical Graduates (IMGs) play an integral role in mitigating the high demand for family medicine physicians across Canada. Thus, selecting IMG candidates that have a high probability of succeeding in Canadian educational settings is important. The purpose of this study is to elucidate qualitative attributes that positively correspond to success in residency, to ultimately assist in the selection of IMG residents most likely to achieve family medicine residency. METHODS: Interviews of 13 family medicine preceptors from some of the largest IMG training sites in Canada were performed to collect original data. The data was coded in tandem sequences using standardized coding techniques to increase robustness of results. RESULTS: The identified positive predictors of an IMG residents' success are: presence of a positive attitude, proficient communication skills, high level of clinical knowledge, trainability. CONCLUSIONS: The results provide adequate guidelines to assist in selection of IMG residents. Canada is a unique sociocultural setting where standardized selection methods of IMGs have not been employed. By selecting IMG residents who possess these attributes upon inception of residency, benefits of instruction will be maximized and result in residents developing increased aptitudes for patient care.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".