Neonatal Ethics Teaching Program – Scenario-Oriented Learning in Ethics: Antenatal Consultation at the Limit of Viability
Bibliographic record
Abstract
Abstract Scenario Oriented Learning in Ethics uses standardized patients (SPs) as a teaching tool to impart knowledge on the principal and the three key competencies of the Neonatal Ethics Teaching Program that trainees are expected to acquire before completing their Neonatal-Perinatal Medicine (NPM) training at the University of Ottawa. Furthermore, this workshop provides trainees the opportunity to practice and learn how they would interact with a true patient in a given clinical scenario. The goal of this workshop is to help trainees show improvement in their communication skills and demonstrate appropriate application of ethical principles when they have to interact with parents in delicate, difficult, and ethically charged situations regarding their child. The educational methods used by the NPM program to facilitate adult learning and to teach communication skills include experiential learning, relational learning, small group workshops, scenarios involving SP, and activities structured to promote self-reflection and inclusion of the family perspective. Based on these methods we integrated a SP in our small-group scenario-oriented workshops to allow for exposure to different approaches to the task and multiple different reactions from the SP. By including the SP posing as the “families” of the patient, clinician trainees can be trained and formally evaluated on their approach to distressed families, while negotiating all aspects of the encounter. The debriefing is conducted as a group process where all the participants are deemed to be on the same professional level and equally respected. At the end of one rotation between the SP and a trainee (i.e., before starting with a new trainee), the supervisor guides the debriefing, concentrating on learning outcomes. Enough time is provided for participants to verbalize their feelings and completely debrief. At the conclusion of the scenario, the SPs share their feedback by summarizing two or three points that are valuable to the trainees. This time is also used for the participant to summarize and identify their strengths and areas needing improvement in order to be able to become a better communicator and professional.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".