Teaching ethics in neonatal and perinatal medicine: What is happening in Canada?
Bibliographic record
Abstract
Ethically challenging clinical situations are frequently encountered in neonatal and perinatal medicine (NPM), resulting in a complex environment for trainees and a need for ethics training during NPM residency. In the present study, the authors conducted a brief environmental scan to investigate the ethics teaching strategies in Canadian NPM programs. Ten of 13 (77%) accredited Canadian NPM residency programs participated in a survey investigating teaching strategies, content and assessment mechanisms. Although informal ethics teaching was more frequently reported, there was significant variability among programs in terms of content and logistics, with the most common topics being 'The medical decision making process: Ethical considerations' and 'Review of bioethics principles' (88.9% each); lectures by staff or visiting staff was the most commonly reported formal strategy (100%); and evaluation was primarily considered to be part of their overall trainee rotation (89%). This variability indicates the need for agreement and standardization among program directors regarding these aspects, and warrants further investigation.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".