Ethics consultation in paediatric and adult emergency departments: an assessment of clinical, ethical, learning and resource needs
Bibliographic record
Abstract
OBJECTIVE: We sought to understand ethics and education needs of emergency nurses and physicians in paediatric and adult emergency departments (EDs) in order to build ethics capacity and provide a foundation for the development of an ethics education programme. METHODS: This was a prospective cross-sectional survey of all staff nurses and physicians in three tertiary care EDs. The survey tool, called Clinical Ethics Needs Assessment Survey, was pilot tested on a similar target audience for question content and clarity. RESULTS: Of the 123 participants surveyed, 72% and 84% of nurses and physicians fully/somewhat agreed with an overall positive ethical climate, respectively. 69% of participants reported encountering daily or weekly ethical challenges. Participants expressed the greatest need for additional support to address moral distress (16%), conflict management with patients or families (16%) and resource issues (15%). Of the 23 reported occurrences of moral distress, 61% were associated with paediatric mental health cases. When asked how the ethics consultation service could be used in the ED, providing education to teams (42%) was the most desired method. CONCLUSIONS: Nurses report a greater need for ethics education and resources compared with their physician colleagues. Ethical challenges in paediatric EDs are more prevalent than adult EDs and nurses voice specific moral distress that are different than adult EDs. These results highlight the need for a suitable educational strategy, which can be developed in collaboration with the leadership of each ED and team of hospital ethicists.
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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 | 0.009 | 0.026 |
| 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 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".