Translating Knowledge in Rehabilitation: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Clinicians are faced with using the current best evidence to make treatment decisions, yet synthesis of knowledge translation (KT) strategies that influence professional practice behaviors in rehabilitation disciplines remains largely unknown. PURPOSE: The purposes of this study were: (1) to examine the state of science for KT strategies used in the rehabilitation professions (physical therapy, occupational therapy, speech-language pathology), (2) to identify the methodological approaches utilized in studies exploring KT strategies, and (3) to report the extent that KT interventions are described. DATA SOURCES: Eight electronic databases (MEDLINE, CINAHL, ERIC, PASCAL, EMBASE, IPA, Scopus, and CENTRAL) were searched from January 1985 to May 2013 using language (English) restriction. STUDY SELECTION: Eligibility criteria specified articles evaluating interventions or strategies with a primary purpose of translating research or enhancing research uptake into clinical practice. DATA EXTRACTION: Two reviewers independently screened the titles and abstracts, reviewed full-text articles, performed data extraction, and performed quality assessment. The published descriptions of the KT interventions were compared with the Workgroup for Intervention Development and Evaluation Research's (WIDER) Recommendations to Improve the Reporting of the Content of Behaviour Change Interventions. DATA SYNTHESIS: Of a total of 2,793 articles located and titles and abstracts screened, 26 studies were included in the systematic review. Eighteen articles reported interventions that used a multicomponent KT strategy. Education-related components were the predominant KT intervention regardless of whether it was a single or multicomponent intervention. Few studies used reminders or audit and feedback intervention (n=3). Only one study's primary outcome measure was an economic evaluation. No clear delineation of the effect on KT strategies was seen. LIMITATIONS: Diverse studies were included; however, the heterogeneity of the studies was not conducive to pooling the data. CONCLUSIONS: The modest-to-low methodological quality assessed in the studies underscores the gaps in KT strategies used in rehabilitation and highlights the need for rigorously designed studies that are well reported.
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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 | 0.057 | 0.216 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.010 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".