"Ethics? But It's Only Quality Improvement!"
Bibliographic record
Abstract
Many people assume that quality improvement (QI) projects pose no ethical issues in relation to participants or their rights. However, members of the Alberta Research Ethics Community Consensus Initiative (ARECCI) submit that all projects that generate knowledge, including QI projects, can create risks to participants that need to be identified, assessed and addressed in the context of the kind of project. The possibility of risk raises the question of ethical conduct in QI projects. Ethical considerations, such as the rights to respect and privacy, protection from harm and voluntary consent, may apply to QI projects, even if the participants are not regarded as research subjects. In this article, we use a case example to illustrate potential ethical issues raised by a QI project, and argue for an ethics review approach that is distinct from that used with research projects. We propose six considerations with guidelines to help assess (and ultimately minimize and mitigate) the risk for participants in QI projects and assist in the appropriate ethical management of these projects.
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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 | 0.038 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.018 |
| 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; 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".