Knowledge translation in ABI rehabilitation: A model for consolidating and applying the evidence for cognitive-communication interventions
Bibliographic record
Abstract
PRIMARY OBJECTIVES: (1) To propose a model for consolidating and disseminating existing evidence relevant to cognitive-communication interventions after ABI. (2) To present the Cognitive-Communication Intervention Review Framework (CCIRF). (3) To outline future considerations for applying evidence to clinical practice. RESEARCH DESIGN: Employment of a model for knowledge translation. METHODS AND PROCEDURES: Application of evidence requires synthesis and dissemination of information in an accessible format for end users. A literature search identified 20 systematic reviews (1997-2007) with a complex array of 72 practice recommendations relevant to cognitive-communication interventions. The CCIRF was used to synthesize the evidence within 11 intervention categories. Reviews were analysed according to: organization, population, intervention, comparison and outcome, with a focus on communication outcomes. MAIN OUTCOMES AND RESULTS: Consolidated evidence revealed support for interventions relating to: social communication, behavioural regulation, verbal formulation, attention, external memory aids, executive functions and communication partner training. Research gaps were noted in the areas of comprehension (auditory/reading), written expression and vocational communication interventions. Similar recommendations emerge across reviews. CONCLUSIONS: Implementation of the growing body of evidence for cognitive-communication interventions is challenged by variability in study populations, interventions, and research focus on communication. The CCIRF provides a means of promoting consistency in knowledge translation and application.
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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 | 0.312 | 0.360 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.035 | 0.016 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".