Is it really a challenge to find positive attributes for international medical graduates predictive of success in family medicine residency?
Bibliographic record
Abstract
In their paper "Challenges with international medical graduate selection: finding positive attributes predictive of success in family medicine residency," (BMC Prim Care 23(256):2-9, 2022) the authors report on their research into qualitative attributes that positively correspond to success in residency with the objective of assisting in the selection of International Medical Graduate (IMG) residents most likely to achieve success in family medicine residency. The authors found that positive predictors of IMG residents' success were: presence of a positive attitude, proficient communication skills, high level of clinical knowledge, and trainability. The authors conclude that selecting IMG residents who possess these attributes will result in residents developing increased aptitudes for patient care. A careful reading of the paper raises a number of concerns. MacFarlane (Can Med Educ J 12(4):132-40, 2021) points out that IMGs are already marginalized in the residency selection process. Our concern is that this paper may contribute to this marginalization through a tone of negativity or bias against IMGs and the use of biased language throughout the paper that tends to cast IMGs as being inferior and somehow less well prepared for residency than Canadian Medical Graduates (CMGs). We argue that the proposed predictors are generic and equally relevant to both CMGs and IMGs. In focusing on these predictors in IMGs specifically, the paper appears to imply, without evidence, that IMGs are inadequate in the identified areas. After reviewing the paper's references, the existing literature, and an analysis of language used, we conclude that IMGs are capable candidates for residency, and that the qualitative attributes outlined in the paper offer little utility for the selection of IMG residents relative to CMG residents.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".