Medical-ethics teaching in Canadian physical medicine and rehabilitation residency training programs.
Bibliographic record
Abstract
BACKGROUND: Medical-ethics education is a required component of all Royal College residency training programs in Canada. There have been no studies to determine how and to what extent this teaching is done in physical medicine and rehabilitation training programs. OBJECTIVE: To assess the state of medical-ethics teaching in Canadian physical medicine and rehabilitation residency training programs. METHODS: A six-question survey was faxed to the program directors of the 10 physiatry residency training programs in Canada. We asked whether medical-ethics teaching is being done, who is doing the teaching, which pedagogic methods is used, how many hours are dedicated to the topic, which topics are taught, and what evaluation method is used. RESULTS: The response rate was 90 per cent. The study confirmed that medical-ethics teaching is done in all Canadian physiatry training programs. However, the person doing the teaching, the number of hours allocated to ethics education, the pedagogic method used, and the topics being taught vary from program to program. CONCLUSION: Although medical-ethics teaching is done in all programs, there is a need for more standardization in the curriculum and in evaluation. The curriculum should focus on ethical issues that are most likely to be encountered in daily physiatric practice. Small-group, case-based teaching should be used for maximum effectiveness. Whenever possible, teaching should be done by a physiatrist.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".