Ethical frameworks for quality improvement activities: an analysis of international practice
Bibliographic record
Abstract
PURPOSE: To examine international approaches to the ethical oversight and regulation of quality improvement and clinical audit in healthcare systems. DATA SOURCES: We searched grey literature including websites of national research and ethics regulatory bodies and health departments of selected countries. STUDY SELECTION: National guidance documents were included from six countries: Ireland, England, Australia, New Zealand, the United States of America and Canada. DATA EXTRACTION: Data were extracted from 19 documents using an a priori framework developed from the published literature. RESULTS: We organized data under five themes: ethical frameworks; guidance on ethical review; consent, vulnerable groups and personal health data. Quality improvement activity tended to be outside the scope of the ethics frameworks in most countries. Only New Zealand had integrated national ethics standards for both research and quality improvement. Across countries, there is consensus that this activity should not be automatically exempted from ethical review but requires proportionate review or organizational oversight for minimal risk projects. In the majority of countries, there is a lack of guidance on participant consent, use of personal health information and inclusion of vulnerable groups in routine quality improvement. CONCLUSION: Where countries fail to provide specific ethics frameworks for quality improvement, guidance is dispersed across several organizations which may lack legal certainty. Our review demonstrates a need for appropriate oversight and responsive infrastructure for quality improvement underpinned by ethical frameworks that build equivalence with research oversight. It outlines aspects of good practice, especially The New Zealand framework that integrates research and quality improvement ethics.
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 | 0.360 | 0.434 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.022 | 0.036 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| 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".