Developing Morally Sensitive Policy in the NICU: Donation after Circulatory Determination of Death
Bibliographic record
Abstract
Policy development is an important activity for the practice of healthcare. Policies, after all, may cultivate common practices and ensure that best available evidence is employed in clinical decision making. Qualitative research and individuals with expertise in qualitative research methods have much to offer policy makers. We were confronted with the situation of developing policy for donation after circulatory death (DCD) for our newborn intensive care program. Due the moral-ethical complexities surrounding DCD, and the limited experience with DCD in this context, we approached policy development from an iterative design perspective employing qualitative methods. We describe our experience in employing this approach and the methodological implications of design as a method for policy development.
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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 | 0.154 | 0.173 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.028 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".