Life-sustaining treatment decisions in pediatric intensive care: an Italian survey on ethical concerns
Bibliographic record
Abstract
OBJECTIVES: To investigate how life-sustaining treatment (LST) decisions are made and identify problematic ethical concerns confronted by physicians and nurses in pediatric intensive care within Italy. METHODS: An 88-question online survey was created, based on a previous qualitative study conducted by this team. The survey was designed to identify how LST decisions were managed; contrasting actual practices with what participants think practices should be. Replies from physicians and nurses were compared, to identify potential inter-professional ethical tensions. The study also identified participants' principal ethical concerns. Moreover, open-ended questions elicited qualitative perspectives on participants' views. The survey was pilot-tested and refined before initiation of the study. RESULTS: 31 physicians and 65 nurses participated in the study. Participants were recruited from pediatric intensive care units across five Italian cities; i.e., Florence, Milan, Padua, Rome, Verona. Statistically significant differences were identified for (a) virtually all questions contrasting actual practices with what participants think practices should be and (b) 14 questions contrasting physician replies with those of nurses. Physicians and nurses identified the absence of legislative standards for LST withdrawal as a highly problematic ethical concern. Physicians also identified bearing responsibility for LST decisions as a major concern. Qualitative descriptions further demonstrated that these Italian pediatric intensive care clinicians encounter significantly distressing ethical problems in their practice. CONCLUSIONS: The results of this study highlight a need for the development of (a) strategies for improving team processes regarding LST decisions, so they can be better aligned with how clinicians think decisions should be made, and (b) Italian LST decision-making standards that can help ensure optimal ethical practices.
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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 | 0.010 | 0.024 |
| 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 | 0.001 | 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".