Assessing postgraduate trainees in Canada: Are we achieving diversity in methods?
Bibliographic record
Abstract
BACKGROUND: Resident evaluation is a complex and challenging task, and little is known about what assessment methods, predominate within or across specialties. AIMS: To determine the methods program directors in Canada use to assess residents and their perceptions of how evaluation could be improved. METHODS: We conducted a web-based survey of program directors from The Royal College of Physicians and Surgeons of Canada (RCPSC)-accredited training programs, to examine the use of the In-Training Evaluation Report (ITER), the use of non-ITER tools and program directors' perceived needs for improvement in evaluation methods. RESULTS: One hundred forty-nine of the eligible 280 program directors participated in the survey. ITERs were used by all but one program. Of the non-ITER tools, multiple choice questions (71.8%) and oral examinations (85.9%) were most utilized, whereas essays (11.4%) and simulations (28.2%) were least used across all specialties. Surgical specialties had significantly higher multiple choice questions and logbook utilization, whereas medical specialties were significantly more likely to include Objective Stuctured Clinical Examinations (OSCEs). Program directors expressed a strong need for national collaboration between programs within a specialty to improve the resident evaluation processes. CONCLUSIONS: Program directors use a variety of methods to assess trainees. They continue to rely heavily on the ITER, but are using other tools.
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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 | 0.037 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".