Effectiveness of a Clinically Relevant Educational Program for Improving Medical Communication and Clinical Skills of International Medical Graduates
Bibliographic record
Abstract
Background: To assess the efficacy of a 16 week, intensive, full-time medical communication and clinical skills educational program – Medical Communication Assessment Project (M-CAP) – at the Universities of Calgary and Alberta for improving medical communication, clinical skills and professionalism of international medical graduates (IMGs). There is an 8 week didactic course (language instructors, standardized clinical case scenarios) and an 8 week supervised clinical placement.Method: In Study 1, 39 IMGs (mean age = 35.6) participated in the M-CAP program and were assessed in English language proficiency in a pre- post-test design and on an in training evaluation report (ITER) by practicum physicians. In Study 2, there were 235 IMGs (mean age = 39.2). In addition to the pre- post-test design, there was a comparison group analysis on OSCE data employing multivariate analysis of variance (MANOVA). Pre- post-test data were also collected on ITERs during the practicum as was IMG reported program efficacy data.Results: The findings show that the participants in the M-CAP program have 1) very large gains in language proficiency (listening and speaking, reading and writing; p < .001), and 2) high ratings on scales from the practicum physicians. The between group analyses showed that M-CAP participants outperformed the non M-CAP participants on clinical skills and professionalism (p < .05). The IMGs gave very positive ratings to the M-CAP program.Conclusions: IMGs who participated in a clinically relevant educational program improved their English language proficiency, clinical skills and professionalism for medical practice in a host country.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".