Ethics policy review: a case study in quality improvement
Bibliographic record
Abstract
Policy work is often cited as one of the primary functions of Hospital Ethics Committees (HECs), along with consultation and education. Hospital policies can have far reaching effects on a wide array of stakeholders including, care providers, patients, families, the culture of the organisation and the community at large. In comparison with the wealth of information available about the emerging practice of ethics consultation, relatively little attention has been paid to the policy work of HECs. In this paper, we hope to advance the development of best practices in HEC policy work by describing the quality improvement process that we undertook at Hamilton Health Sciences, Hamilton, Ontario, Canada. In the first section of the paper we describe the context of our HEC policy work, and the shortcomings of our historical review process. In subsequent sections, we detail the quality improvement project we undertook in 2010, the results of the project and the specific tools we developed to enhance the quality of HEC policy work. Our goal in sharing this organisational case study is to prompt other HECs to publish qualitative descriptions of their policy work, in order to generate a body of knowledge that can inform the development of best practices for ethics policy review.
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 | 0.139 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.043 | 0.021 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".