NICU Language, Everyday Ethics, and Giving Better News: Optimizing Discussions about Disability with Families
Bibliographic record
Abstract
The Neonatal Intensive Care Unit (NICU) has a language and culture that is its own. For professionals, it is a place of intense and constant attention to microdetails and cautious optimism. For parents, it is a foreign place with a new and unique language and culture. It is also the setting in which they are introduced to their child and parenthood for this child. This combination has been referred to as an emotional cauldron. The neonatal ethics literature mainly examines complex ethical dilemmas about withholding/drawing life sustaining interventions for fragile children. Rarely are everyday ethics or mundane ethics discussed. Microethics describe the mundane, discrete moments that occur between patients/families and clinicians. A key piece of these microethics is the language used to discuss patient care. Perception of prognoses, particularly around long-term neurodevelopmental outcome, is shaped with the language used. Despite this, clinicians in the NICU often have no specific training in the long-term neurodevelopment outcomes that they discuss. This paper focuses on the microethics of language used to discuss long-term neurodevelopmental outcomes, the developmental neuroscience behind language processing, and offers recommendations for more accurate and improved communication around long-term outcomes with families with critically ill neonates.
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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 | 0.034 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".