Bibliographic record
Abstract
Background and aims: The increasing complexity of critical illness, new life sustaining technologies and evolving societal expectations have spurred new ethical challenges for paediatric critical care providers in recent years. A bioethics consultation service is an essential resource to help address these ethical issues. Aims: We report on the role of a bioethics service, and accompanying interventions, designed to establish and sustain an ethical community of practice in one paediatric critical care unit. Methods: Interventions were targeted for specific objectives including; enhancing provider awareness of ethical dilemmas, articulation of moral tensions and improved integration of bioethics expertise into everyday practice. Interventions ranged from increasing the supports offered by existing Bioethics services (event debriefing, case-based discussion), to explicit leadership support for bioethics referrals. An education program was established with concurrent policy development. A point of care rounding process, Care And Reflective Ethics Dialogue (CARED), was developed bringing bioethics resources directly to front line staff. Results: Over a two-year period bioethics consultations have increased for both families and the health care team. Debriefing and case specific discussions have doubled. Bioethics representation on quality, bereavement and research committees has been established and bioethical concerns are addressed with increasing regularity in ward rounds. CARED has catalyzed case specific interventions achieving a regular ‘ethics pulse check’ of bedside staff. Conclusions: Bioethics support is most effective when providers recognize ethical tension, have access to bioethics resources and have a space in which to explore their emerging understandings. These multi-platform interventions have positively impacted and nurtured an ethical community of practice.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.663 | 0.500 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".