Benchmarking a Canadian anesthesiology resident research program against national norms using a logic model framework: a quality improvement study.
Bibliographic record
Abstract
Background: Canadian specialty training programs are expected to deliver curriculum content and assess competencies related to the CanMEDS Scholar role. We evaluated our residency research program and benchmarked it against national norms for quality improvement purposes. Methods: In 2021 we reviewed departmental curriculum documents and surveyed current and recently graduated residents. We applied a logic model framework to assess if our program's inputs, activities, and outputs addressed the relevant CanMeds Scholar competencies. We then descriptively benchmarked our results against a 2021 environmental scan of Canadian anesthesiology resident research programs. Results: Local program content was successfully mapped to competencies. The local survey response rate was 40/55 (73%). In benchmarking, our program excelled in providing milestone-related assessments, research funding, administrative, supervisory, and methodologic support, and requiring a literature review, proposal presentation, and local abstract submission as output. Acceptable activities to meet research requirements vary greatly among programs. Balancing competing clinical and research responsibilities was a frequently reported challenge. Conclusions: The logic model framework was easily applied and demonstrated our program benchmarked well against national norms. National level dialogue is needed to develop specific, consistent scholar role activities and competency assessments to bridge the gap between expected outcome standards and education practice.
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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 | 0.093 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".