Project Examining Effectiveness in Clinical Ethics (PEECE): phase 1—descriptive analysis of nine clinical ethics services
Bibliographic record
Abstract
OBJECTIVE: The field of clinical ethics is relatively new and expanding. Best practices in clinical ethics against which one can benchmark performance have not been clearly articulated. The first step in developing benchmarks of clinical ethics services is to identify and understand current practices. DESIGN AND SETTING: Using a retrospective case study approach, the structure, activities, and resources of nine clinical ethics services in a large metropolitan centre are described, compared, and contrasted. RESULTS: The data yielded a unique and detailed account of the nature and scope of clinical ethics services across a spectrum of facilities. General themes emerged in four areas-variability, visibility, accountability, and complexity. There was a high degree of variability in the structures, activities, and resources across the clinical ethics services. Increasing visibility was identified as a significant challenge within organisations and externally. Although each service had a formal system for maintaining accountability and measuring performance, differences in the type, frequency, and content of reporting impacted service delivery. One of the most salient findings was the complexity inherent in the provision of clinical ethics services, which requires of clinical ethicists a broad and varied skill set and knowledge base. Benchmarks including the average number of consults/ethicist per year and the hospital beds/ethicist ratio are presented. CONCLUSION: The findings will be of interest to clinical ethicists locally, nationally, and internationally as they provide a preliminary framework from which further benchmarking measures and best practices in clinical ethics can be identified, developed, and evaluated.
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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 | 0.028 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".