Accommodating quality and service improvement research within existing ethical principles
Bibliographic record
Abstract
BACKGROUND: Quality and service improvement (QSI) research employs a broad range of methods to enhance the efficiency of healthcare delivery. QSI research differs from traditional healthcare research and poses unique ethical questions. Since QSI research aims to generate knowledge to enhance quality improvement efforts, should it be considered research for regulatory purposes? Is review by a research ethics committee required? Should healthcare providers be considered research participants? If participation in QSI research entails no more than minimal risk, is consent required? The lack of consensus on answers to these questions highlights the need for ethical guidance. MAIN BODY: Three distinct approaches to classifying QSI research in accordance with existing ethical principles and regulations can be found in the literature. In the first approach, QSI research is viewed as distinct from other types of healthcare research and does not require regulation. In the second approach, QSI research falls within regulatory guidelines but is exempt from research ethics committee review. In the third approach, QSI research is deemed to be part of the learning healthcare system and, as such, is subject to a different set of ethical principles entirely. In this paper, we critically assess each of these views. CONCLUSION: While none of these approaches is entirely satisfactory, we argue that use of the ethical principles governing research provides the best means of addressing the numerous questions posed by QSI research.
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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 | 0.319 | 0.453 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.068 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.079 | 0.086 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".