The Knowledge Exchange–Decision Support Model: application to cancer navigation programs
Bibliographic record
Abstract
PURPOSE: The Knowledge Exchange-Decision Support (KE-DS) Model provides a framework outlining essential components of knowledge generation and exchange. The purpose of this research was to illustrate how the Model makes explicit the different contextual aspects implicit in the planning and implementation of two cancer navigation programs in Canada. METHODS: The KE-DS Model guided the collection and analysis of interviews with program personnel and narrative data. A qualitative thematic analysis was conducted wherein we compared and contrasted the planning and implementation of these two navigation programs. RESULTS: The planning and implementation of these two programs was conceptualized differently and adapted to meet local contingencies. The KE-DS Model highlighted three factors that influenced program delivery. First, the structure of health services was shaped by the interaction of professionals and services operating in the region, and the existing health services influenced the program's approach to navigation. Second, while there were similarities in the professional roles and responsibilities of the navigators, these roles and responsibilities also reflected local context in their approaches to patient assessment, referral, education, coordination of services, and advocacy. Third, these two distinct approaches to navigation have responded to the needs of diverse populations being served by improving access to care. CONCLUSIONS: Evidence generated using the KE-DS Model could ensure a more robust and structured approach to the planning and implementation of future navigation programs. The Model prompts users to make explicit the different types of evidence utilized during program planning and implementation. The systematic collection of new information on program implementation using the KE-DS Model in future initiatives will contribute to an improved understanding of the science of knowledge exchange.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".