Survey highlights the need for specific interventions to reduce frequent conflicts between healthcare professionals providing paediatric end‐of‐life care
Bibliographic record
Abstract
AIMS: This study explored how paediatric healthcare professionals experienced and coped with end-of-life conflicts and identified how to improve coping strategies. METHODS: A questionnaire was distributed to all 2300 professionals at a paediatric university hospital, covering the frequency of end-of-life conflicts, participants, contributing factors, resolution strategies, outcomes and the usefulness of specific institutional coping strategies. RESULTS: Of the 946 professionals (41%) who responded, 466 had witnessed or participated in paediatric end-of-life discussions: 73% said these had led to conflict, more frequently between professionals (58%) than between professionals and parents (33%). Frequent factors included professionals' rotations, unprepared parents, emotional load, unrealistic parental expectations, differences in values and beliefs, parents' fear of hastening death, precipitated situations and uncertain prognosis. Discussions with patients and parents and between professionals were the most frequently used coping strategies. Conflicts were frequently resolved by the time of death. Professionals mainly supported designating one principal physician and nurse for each patient, two-step interdisciplinary meetings - between professionals then with parents - postdeath ethics meetings, bereavement follow-up protocols and early consultations with paediatric palliative care and clinical ethics services. CONCLUSION: End-of-life conflicts were frequent and predominantly occurred between healthcare professionals. Specific interventions could target most of the contributing factors.
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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 | 0.014 | 0.049 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".