Organizational Knowledge Translation Strategies for Allied Health Professionals in Traumatology Settings: A Realist Review Protocol
Bibliographic record
Abstract
Abstract Background: Knowledge translation (KT) is an important means of improving health service quality. Most research on the effectiveness of KT strategies has focused on individual strategies, i.e., those targeting directly the modification of allied health professionals’ knowledge, attitudes, and behaviours, for example. In general, these strategies are moderately effective in changing practices (maximum 10% change). Effecting change in organizational contexts (e.g., change readiness, general and specific organizational capacity, organizational routines) is part of a promising new avenue to service quality improvement through the implementation of evidence-based practices.Methods: A realist review will be conducted to explore the various organizational KT strategy action mechanisms (how, why, for whom, to what extent and under what circumstances) with a view to facilitating the choice of effective strategies for sustainable implementation of evidence-based practice for allied health professionals in traumatology settings. The review will begin by presenting initial theories developed by the research team, followed by the search for evidence, the selection of literature on context-mechanism-outcome configurations related to organizational KT strategies, and end with a refinement of initial theories and a synthesis.Discussion: Using a systematic and rigorous method, this review will help guide decision makers and researchers in choosing the best organizational strategies to optimize the implementation of evidence-based practices.Registration: This protocol has been submitted for registration on the PROSPERO database on October 30, 2020 (ID: 216105).
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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 | 0.122 | 0.124 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.083 | 0.014 |
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".