Neonatal Ethics Teaching Program - Problem Based Learning in Ethics (PBLE): Critically Ill Newborn in the NICU
Bibliographic record
Abstract
A PBLE is a teaching tool that imparts knowledge on some of the competencies of the Neonatal Ethics Teaching Program that the NICU fellows are expected to acquire before completing their Neonatal-Perinatal Medicine training at the University of Ottawa. Furthermore, a PBLE provides trainees the opportunity to practice and learn how they would interact with a true patient in a given clinical scenario. This helps trainees improve their communication skills and application of ethical principles when they have to interact with parents in delicate, difficult, and ethically charged situations regarding either their unborn or born child. Trainees are encouraged to refer to a procedural form that outlines the steps they should follow during a one on one medical encounter and use the standardized patient as a teaching tool. This PBLE is specific to the critically ill newborn in the NICU scenario where trainees are taught to distinguish the three parent rationales behind the question: “If my baby was yours, what would you do?” and explain the appropriate response to the parent question: “Have you done everything you can for my baby?”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".