Identifying Bioethics Learning Needs: A Survey of Canadian Emergency Medicine Residents
Bibliographic record
Abstract
Objectives: Emergency medicine (EM) postgraduate training programs must prepare residents for the ethical challenges of clinical practice. Bioethics curricula have been developed for EM residents, but they are based on expert opinion rather than resident learning needs. Educational interventions based on identified learning needs are more effective at changing practice than interventions that are not. The goal of this study was to identify the bioethics learning needs of Canadian EM residents. Methods: A survey-based needs assessment of Canadian EM residents was performed between July 2000 and June 2001. Residents were asked to identify their learning needs by rating bioethics topics and by relating their clinical experiences. Physicians and nurses who work with residents were surveyed in a similar manner and also asked to identify the residents' bioethics learning needs. Results: A total of 129 EM residents (77% of eligible residents), 94 physicians, and 87 nurses responded. Residents, physicians, and nurses all identified issues in end-of-life care as the greatest bioethics learning needs of the residents. Other areas identified as learning needs included negotiating consent, capacity assessment, truth telling, and breaking bad news. A learning need identified by nurses, but not residents, was the manner in which residents interact with patients and colleagues. Conclusions: This needs assessment provides valuable information about the ethical challenges EM residents encounter and the ethical issues they believe they have not been prepared to face. This information should be used to direct and shape ethics education interventions for EM residents.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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 | 0.000 |
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".