How Do Graduates of Longitudinal Integrated Clerkships Fare on the Medical Council of Canada Qualifying Exam Part ll?
Bibliographic record
Abstract
The longitudinal integrated clerkship (LIC) model has recently become a popular educational model for training clinical clerks. LICs permit students to train in multiple disciplines concurrently and typically in rural practice sites. Because little is known about how graduates of LIC programs fare in residency, the purpose of this study was to compare the clinical performance of residents who graduated from rural longitudinal integrated and urban rotation-based clerkships on the Medical Council of Canada Qualifying Exam Part ll (MCCQE Part ll) taken 16 months into residency. Participants included medical school graduates from the classes of 2009, 2010 and 2011 at the University of Calgary. Each of the 34 LIC students were prospectively matched (first on Medical Skills ll course performance, then grade point average) with 4 students from the traditional rotation-based (RB) stream to serve as controls (n = 136). A dataset containing 170 graduates was forwarded to the Medical Council of Canada (MCC) who subsequently supplied MCCQE Part ll pass/ fail status and total score for each resident, and returned the dataset for our analysis. Data were analyzed using chi-square and analysis of variance. The final dataset for analysis consisted of 30 (88%) LIC graduates and 115 (85%) RB graduates. Analysis revealed a similar MCCQE Part ll pass rate for LIC (28/30; 93.3%) and RB (107/115; 93.0%) graduates, p > 0.05. The MCCQE Part ll mean total score for the LIC graduates (M = 527.4; SD = 64.3) did not differ from the mean total score (M = 529.9; SD = 61.4) reported by the RB graduates, F = 0.04, p = 0.85. Completing the majority of clerkship in a rural community over an extended period allowed LIC graduates to perform as well as their peers on a measure of clinical skills taken 16 months into residency.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".