Performance Improvement Capability: Keys to Accelerating Performance Improvement in Hospitals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Organizations differ considerably in their rate of improvement. Since any improvement trajectory is the fruit of a series of improvement projects, the proximate cause of this variation among organizations lies in the varied ways these projects are managed. The success of these projects depends, however, not only on the goals and efforts of the project team, but also on the context within which the projects are undertaken - and, more specifically, on the competencies on which the projects can draw. It is variation in these competencies - the organization's underlying performance improvement capability (PIC) - that explain the substantial and sustained differences in rates of improvement across organizations. This article describes the efforts of several hospitals to strengthen their PIC through 5 key components: 1. skills, 2. systems, 3. structure, 4, strategy, and 5. culture.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it