Dissemination of performance information and continuous improvement
Bibliographic record
Abstract
PURPOSE: Developing a performance measure and reporting the results to support decision making at an individual level has yielded poor results in many health systems. The purpose of this paper is to highlight the factors associated with the dissemination of performance information that generate and support continuous improvement in health organizations. DESIGN/METHODOLOGY/APPROACH: A systematic data collection strategy that includes empirical and theoretical research published from 1980 to 2010, both qualitative and quantitative, was performed on Web of Science, Current Contents, EMBASE and MEDLINE. A narrative synthesis method was used to iteratively detail explicative processes that underlie the intervention. A classification and synthesis framework was developed, drawing on knowledge transfer and exchange (KTE) literature. The sample consisted of 114 articles, including seven systematic or exhaustive reviews. FINDINGS: Results showed that dissemination in itself is not enough to produce improvement initiatives. Successful dissemination depends on various factors, which influence the way collective actors react to performance information such as the clarity of objectives, the relationships between stakeholders, the system's governance and the available incentives. RESEARCH LIMITATIONS/IMPLICATIONS: This review was limited to the process of knowledge dissemination in health systems and its utilization by users at the health organization level. Issues related to improvement initiatives deserve more attention. PRACTICAL IMPLICATIONS: Knowledge dissemination goes beyond better communication and should be considered as carefully as the measurement of performance. Choices pertaining to intervention should be continuously prompted by the concern to support organizational action. ORIGINALITY/VALUE: While considerable attention was paid to the public reporting of performance information, this review sheds some light on a more promising avenue for changes and improvements, notably in public health systems.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".