A Framework for the Dissemination and Utilization of Research for Health‐Care Policy and Practice
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to construct a comprehensive framework of research dissemination and utilization that is useful for both health policy and clinical decision-making. ORGANIZING CONSTRUCT: The framework illustrates that the process of the adoption of research evidence into health-care decision-making is influenced by a variety of characteristics related to the individual, organization, environment and innovation. The framework also demonstrates the complex inter-relationships among these characteristics as progression through the five stages of innovation namely, knowledge, persuasion, decision, implementation and confirmation occurs. Finally, the framework integrates the concepts of research dissemination, evidence-based decision-making and research utilization within the diffusion of innovations theory. METHODS: During the discussion of each stage of the innovation adoption process, relevant literature from the management field (i.e., diffusion of innovations, organizational management and decision-making) and health-care sector (i.e., research dissemination and utilization and evidence-based practice) is summarized. Studies providing empirical data contributing to the development of the framework were assessed for methodological quality. CONCLUSIONS: The process of research dissemination and utilization is complex and determined by numerous intervening variables related to the innovation (research evidence), organization, environment and individual.
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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 | 0.474 | 0.285 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.026 | 0.014 |
| Science and technology studies | 0.012 | 0.062 |
| Scholarly communication | 0.032 | 0.033 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".