Knowledge Translation in Health Care: Moving from Evidence to Practice. 2nd ed.
Bibliographic record
Abstract
Knowledge translation is the process by which research and information is synthesized, disseminated, and applied in a given context, in this case the health care system, to improve its efficiency, health outcomes, patient care, and quality of life.Knowledge translation is important because of the documented knowledge-to-action gaps in healthcare and in health policy making processes in Canada, which indicate that decisions are regularly made both at the individual and system level that are not informed by best evidence.In their book Knowledge Translation in Health Care: Moving from Evidence to Practice, Sharon E. Straus, Jacqueline Tetroe, Ian D. Graham, and their many contributors define knowledge translation (Section 1), explain knowledge creation in the context of health sciences (Section 2), and describe the translation process through the conceptual framework of the Knowledge to Action cycle and its phases: identifying knowledge to action gaps, adapting knowledge to the local context, identifying barriers and facilitators, selecting knowledge translation interventions, monitoring knowledge use, evaluating outcomes, and sustaining knowledge use (Section 3).The authors also discuss other Knowledge to Action theories (Section 4), evaluation models (Section 5), and finally ethics in the context of knowledge translation (Section 6).Meant as an introductory text for policy makers, researchers, clinicians, and trainees, this book does not use overly technical language and would be easily understood by these intended readers, despite their varied backgrounds.Although it would still be a useful text in other health systems, it is especially well suited to Canadian readers because of the predominance of Canadian contributors and the number of Canadian-focused examples and case studies described.These examples and case studies are one of the strongest aspects of the book, especially those provided in Chapter 3.7a.This chapter is entirely dedicated to representing the Knowledge to Action Cycle through a detailed step-bystep description of a knowledge translation program that was aimed at community care venous leg ulcer treatment in Ontario.In addition to being highly relevant, this case study and the other examples provided in each chapter make the content more accessible and clearly demonstrate how knowledge translation theory, frameworks, and approaches can become practice in a health care setting.In addition to the many examples provided, the transparency with which the contributors discuss the evidence used to support the theories and premises outlined in the book is also a great strength of this text.
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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 | 0.069 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".