Introducing the model of cognitive-communication competence: A model to guide evidence-based communication interventions after brain injury
Bibliographic record
Abstract
PRIMARY OBJECTIVE: Communication impairments associated with acquired brain injury (ABI) are devastating in their impact on family, community, social, academic, and vocational participation. Despite international evidence-based guidelines for communication interventions, evidence practice gaps include under identification of communication deficits, infrequent referrals, and inadequate treatment to realize functional communication outcomes. Evidence-informed communication intervention requires synthesis of abundant interdisciplinary research. This study describes the development of the model of cognitive-communication competence, a new model that summarizes a complex array of influences on communication to provide a holistic view of communication competence after ABI. RESEARCH DESIGN: A knowledge synthesis approach was employed to integrate interdisciplinary evidence relevant to communication competence. METHODS AND PROCEDURES: Development of the model included review of the incidence of communication impairments, practice guidelines, and factors relevant to communication competence guided by three key questions. This was followed by expert consultation with researchers, clinicians, and individuals with ABI. MAIN OUTCOMES AND RESULTS: The resulting model comprises 7 domains, 7 competencies, and 47 factors related to communication functioning and intervention. CONCLUSION: This model could bridge evidence to practice by promoting a comprehensive and consistent view of communication competence for evidence synthesis, clinical decision-making, outcome measurement, and interprofessional collaboration.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".