Developing a Knowledge Test for a Neonatal Ethics Teaching Program
Bibliographic record
Abstract
Objective The innovative Neonatal-Perinatal Medicine (NPM) Ethics Teaching Program at the University of Ottawa provides NPM trainees with vital foundational knowledge required to manage ethically contentious clinical scenarios frequently encountered in practice. In this study, our aim was to develop a knowledge test to assess the impact of the NPM Ethics Teaching Program on trainees' knowledge about ethics. Study design Using an iterative four-step process, we developed a test for assessing pre- and post-training knowledge of NPM ethics. We first created a blueprint of the test, identifying its purpose, length, and format. We then weighted the learning outcomes of the NPM Ethics Teaching Program sessions to determine the number of questions that would be asked to assess to each learning outcome. Next, we populated the question bank and constructed a draft test. We obtained feedback from content experts on the draft test and piloted the draft test with former trainees from the NPM Ethics Teaching Program. Results We developed a pre- and post-knowledge test in NPM ethics consisting of 44 multiple choice questions (MCQs), each with five response options. The test takes approximately 60 minutes to complete. It took roughly 15 months to design and pilot the NPM ethics test. Conclusions This test can aid in the assessment of the amount of NPM ethics gained by trainees and contribute to the identification of areas for improvement in teaching and in the overall ethics program. Further iterations of the test will allow for additional assessment of its validity and the efficacy of the teaching program. Given the lack of structured evaluative ethics teaching programs in NPM nationally, this project will act as another step towards the introduction of our NPM Ethics Teaching Program to other Canadian NPM residencies.
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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 | 0.029 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".