How we teach ethics and communication during a Canadian neonatal perinatal medicine residency: An interactive experience
Bibliographic record
Abstract
BACKGROUND: Ethically challenging clinical situations frequently confront health care professionals in neonatology. These situations require neonatologists to exercise professionalism by communicating effectively throughout evolving physician-parent relationships in order to arrive at shared decisions for care that are in the best interest of the neonate and grounded solidly in ethical precepts. AIM: This article describes the process by which a well-delineated, interactive program to teach ethical reasoning and skillful communication with parents was implemented at the University of Ottawa, Canada. METHODS: A revised ethics program implemented in 2009 identified competencies that should be demonstrated at the end of the Neonatal-Perinatal Medicine (NPM) residency. Several seminars were refined while new workshops, problem-based learning in ethics, and a personal portfolio were added. RESULTS: All teaching strategies were well received based on the average level of satisfaction (5.8 out of 7, SD 0.4). We are now moving forward by formally assessing our program including the impact on knowledge acquisition and behavior. CONCLUSION: A dedicated, interactive competency-based neonatal ethics teaching program is vital to support NPM trainees in learning how to integrate ethical thinking with competencies in communication.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".