How Quality Improvement Practice Evidence Can Advance the Knowledge Base
Bibliographic record
Abstract
Recommendations for the evaluation of quality improvement interventions have been made in order to improve the evidence base of whether, to what extent, and why quality improvement interventions affect chosen outcomes. The purpose of this article is to articulate why these recommendations are appropriate to improve the rigor of quality improvement intervention evaluation as a research endeavor, but inappropriate for the purposes of everyday quality improvement practice. To support our claim, we describe the differences between quality improvement interventions that occur for the purpose of practice as compared to research. We then carefully consider how feasibility, ethics, and the aims of evaluation each impact how quality improvement interventions that occur in practice, as opposed to research, can or should be evaluated. Recommendations that fit the evaluative goals of practice-based quality improvement interventions are needed to support fair appraisal of the distinct evidence they produce. We describe a current debate on the nature of evidence to assist in reenvisioning how quality improvement evidence generated from practice might complement that generated from research, and contribute in a value-added way to the knowledge base.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".