Evidence-Based Quality Improvement: The State Of The Science
Bibliographic record
Abstract
Routine practice fails to incorporate research evidence in a timely and reliable fashion. Many quality improvement (QI) efforts aim to close these gaps between clinical research and practice. However, in sharp contrast to the paradigm of evidence-based medicine, these efforts often proceed on the basis of intuition and anecdotal accounts of successful strategies for changing provider behavior or achieving organizational change. We review problems with current approaches to QI research and outline the steps required to make QI efforts based as much on evidence as the practices they seek to implement.
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 | 0.292 | 0.517 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".