Analysis of knowledge translation process: scenarios of application for LARIISA
Bibliographic record
Abstract
Knowledge Translation in health care has been adopted to describe the methods used for dissemination of scientific evidence to several other related social actors. The intention is for this evidence to become more accessible to its final users (patients, health professionals, organizations and health managers) to cooperate in making decision in health. In this manner, Knowledge Translation has as objective to reduce the temporal and conceptual gap that extends between knowledge production and practice for the final user. This is essential in order to obtain better results in health care, along with contributing to the reduction of costs in knowledge utilization, providing the correct knowledge, to the correct person, at the correct moment. This is an analytic study on the Knowledge Translation process in integrated health systems. The study was based on the Knowledge to Action model (KTA), developed by researchers at the University of Toronto and the Canadian Institute of Health Research – CIHR, which has served as reference for the Canadian health system. As conceptual proof of the analytic study performed, we used as application scenario, LARIISA (Laboratoire Application Réseaux Intelligence Intégration Santé), a platform for decision making in the governance of health systems. LARIISA uses, in its framework specification, the KTA model. It uses intelligent systems and context-awareness technology in its conception. Data collection method used was documental analysis from the ‘Knowledge Translation in Health Care’ book and the five application domains from the LARIISA platform: clinical-epidemiological, normative, knowledge management, administrative and shared management. This permitted the elaboration of conceptual maps that translate the local and global coverage of the LARIISA project, resulting in the construction of application scenarios. Thus, this study is characterized as the first analytic endeavor on knowledge management in the LARIISA platform, whose results propose alterations in the initial proposal of LARIISA framework.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".