Moral Distress in Neonatology
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To longitudinally examine the nature of moral distress (MoD) experienced by clinicians caring for extremely low gestational age neonates. METHODS: Neonatologists, medical trainees, and nurses were surveyed at regular intervals on their experience of MoD and their preferred level of care in relation to 99 neonates born <28 weeks' gestational age managed from birth until discharge or death in 2 tertiary NICUs. Clinicians reporting significant distress (≥6 of 10 on Wocial's Moral Distress Thermometer) were asked to provide open-ended responses on why they experienced MoD. Descriptive statistics were used to analyze frequency and intensity of MoD across different clinician characteristics. Open-ended responses were analyzed by using mixed methods. RESULTS: Over 18 months, 4593 of 5332 surveys (86% response rate) were collected. MoD was reported on 687 (15%) survey occasions; 91% of neonates elicited MoD during their hospitalization. In their open-ended answers, clinicians invoked 5 main themes to explain their distress: (1) infant-centered reasons (83%), including illness severity, predicted outcomes, and disproportionate care; (2) management plans (26%); (3) family-centered reasons (19%); (4) parental decision-making (16%); and (5) provider-centered reasons (15%). MoD was strongly associated with the perception of "parents wanting too much." Neonatologists experienced less distress and were more likely than nurses and trainees to align preferred levels of care with family wishes. CONCLUSIONS: The majority of preterm infants will generate some MoD; however, it is rarely shared and of a sustained nature. The main constraint reported by clinicians was "parents wanting too much," leading to disproportionate care.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".