Use of a knowledge synthesis by decision makers and planners to facilitate system level integration in a large Canadian provincial health authority
Bibliographic record
Abstract
PURPOSE: The study is an examination of how a knowledge synthesis, conducted to fill an information gap identified by decision makers and planners responsible for integrating health systems in a western Canadian health authority, is being used within that organization. METHODS: Purposive sampling and snowball technique were used to identify 13 participants who were interviewed about how they are using the knowledge synthesis for health services planning and decision-making. RESULTS: The knowledge synthesis is used by those involved in the strategic direction of the provincial healthcare organization and those tasked with the operationalization of integration at the provincial or local level. Both groups most frequently use the 10 key principles for integration, followed by the sections on integration processes, strategies and models. The key principles facilitate discussion on priority areas to be considered and provide a reference point for a desired future state. Perceived information gaps relate to a lack of detail on 'how to' strategies, tools and processes that would lead to successful integration. DISCUSSION AND CONCLUSION: The current project demonstrates that decision makers and planners will effectively use a knowledge synthesis if it is timely, relevant and accessible. The information can be applied at strategic and operations levels. Attention needs to be paid to include more information on implementation strategies and processes. Including knowledge users in identifying research questions will increase information uptake.
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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 | 0.078 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".