Moral distress in the resuscitation of extremely premature infants
Bibliographic record
Abstract
OBJECTIVE: To increase our understanding of moral distress experienced by neonatal registered nurses when directly or indirectly involved in the decision-making process of resuscitating infants who are born extremely premature. DESIGN: A secondary qualitative analysis was conducted on a portion of the data collected from an earlier study which explored the ethical decision-making process among health professionals and parents concerning resuscitation of extremely premature infants. SETTING: A regional, tertiary academic referral hospital in Ontario offering a perinatal program. PARTICIPANTS: A total of 15 registered nurses were directly or indirectly involved in the resuscitation of extremely premature infants. METHODS: Interview transcripts of nurses from the original study were purposefully selected from the original 42 transcripts of health professionals. Inductive content analysis was conducted to identify themes describing factors and situations contributing to moral distress experienced by nurses regarding resuscitation of extremely premature infants. ETHICAL CONSIDERATIONS: Ethical approval was obtained from the research ethics review board for both the initial study and this secondary data analysis. RESULTS: Five themes, uncertainty, questioning of informed consent, differing perspectives, perceptions of harm and suffering, and being with the family, contribute to the moral distress felt by nurses when exposed to neonatal resuscitation of extremely premature infants. An interesting finding was the nurses' perceived lack of power and influence in the neonatal resuscitation decision-making process. CONCLUSION: Moral distress continues to be a significant issue for nursing practice, particularly among neonatal nurses. Strategies are needed to help mediate the moral distress experienced by nurses, such as debriefing sessions, effective communication, role clarification, and interprofessional education and collaboration.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".