Best practices in clinical ethics consultation and decision-making
Bibliographic record
Abstract
The conference entitled ‘Best Practices in Clinical Ethics Consultation and Decision-Making’, held in London 8–9 July 2010, was the first of its kind dedicated to identifying best practices in clinical ethics consultation and decision-making. Academics, health and social care professionals, clinical ethics committee members, lawyers, service users and carers from the UK, USA, Europe, Canada, Australia and Asia attended lectures, workshops, parallel paper sessions and clinical ethics case discussions across adult, maternity, children's, older persons, mental health and learning disabilities settings. Seventy-eight best-practice points impacting on the quality of clinical ethics consultations and subsequent decisions were identified and grouped into eight themes: who tells the story; how the story is told; how the analysis, discussion and subsequent decisions are made; how values are weighted and balanced against one another; who decides; how the decision is made; how group dynamics and conflict are handled; how decisions are recorded.
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 | 0.453 | 0.414 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.014 | 0.072 |
| Scholarly communication | 0.039 | 0.024 |
| Open science | 0.008 | 0.033 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".