Institutionalization of evidence-informed practices in healthcare settings
Bibliographic record
Abstract
BACKGROUND: The effective and timely integration of the best available research evidence into healthcare practice has considerable potential to improve the quality of provided care. Knowledge translation (KT) approaches aim to develop, implement, and evaluate strategies to address the research-practice gap. However, most KT research has been directed toward implementation strategies that apply cognitive, behavioral, and, to a lesser extent, organizational theories. In this paper, we discuss the potential of institutional theory to inform KT-related research. DISCUSSION: Despite significant research, there is still much to learn about how to achieve KT within healthcare systems and practices. Institutional theory, focusing on the processes by which new ideas and concepts become accepted within their institutional environments, holds promise for advancing KT efforts and research. To propose new directions for future KT research, we present some of the main concepts of institutional theory and discuss their application to KT research by outlining how institutionalization of new practices can lead to their ongoing use in organizations. In addition, we discuss the circumstances under which institutionalized practices dissipate and give way to new insights and ideas that can lead to new, more effective practices. SUMMARY: KT research informed by institutional theory can provide important insights into how knowledge becomes implemented, routinized, and accepted as institutionalized practices. Future KT research should employ both quantitative and qualitative research designs to examine the specifics of sustainability, institutionalization, and deinstitutionalization of practices to enhance our understanding of these complex constructs.
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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 | 0.290 | 0.436 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".