Finding the Right Balance between Evidence for Judgment and Evidence for Quality Improvement
Bibliographic record
Abstract
"Evidence for judgment" asks whether healthcare practitioners did what they were supposed to do. "Evidence for improvement" asks whether quality is improving due to the deliberate efforts of managers and care providers. The former describes the gap between optimal and actual care, examines peer-to-peer comparisons, is retrospective and often measured only at a single point in time, and identifies problems but not necessarily solutions. Knowledge tends to flow from researchers to practitioners, and knowledge transfer mechanisms are unclear. Evidence for improvement is prospective, focuses on comparisons to self over time, uses longitudinal data, and includes evidence on "how" to improve. Knowledge transfer mechanisms are built into quality improvement projects. Both types of evidence have their role; evidence for judgment helps system leaders identify priorities for improvement, whereas evidence for improvement helps leaders assess whether their implementation strategies have been successful. Currently, however, evidence for judgment predominates. Funding mechanisms, data systems, measurement tools, publication guidelines, and health professional training programs will need to be modified if we want more evidence for improvement.
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 | 0.695 | 0.874 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.009 |
| Bibliometrics | 0.034 | 0.014 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.034 | 0.037 |
| Open science | 0.012 | 0.018 |
| Research integrity | 0.034 | 0.023 |
| 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; 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".