Development and Pilot Implementation of a Theory-Based Cognitive Rehabilitation Protocol for Adults With Chronic Cognitive Complaints After Mild Traumatic Brain Injury
Bibliographic record
Abstract
PURPOSE: The aim of this study was to describe the development of and pilot feasibility outcomes for a strategy-based, brief, intensive cognitive rehabilitation intervention delivered to U.S. service members and veterans with mild traumatic brain injury in a recently completed 3-year pragmatic clinical trial: Symptom-Targeted Approach to Rehabilitation for Concussion (STAR-C). METHOD: To develop STAR-C, we used the Rehabilitation Treatment Specification System to identify core elements and principles from a previous randomized clinical trial of cognitive rehabilitation, and incorporated principles of neuroplasticity (e.g., high-dose spaced practice of personally meaningful tasks), best clinical practices (e.g., client-centered goal setting), health psychology (e.g., a focus on self-efficacy and motivation), and community-based participation research (e.g., the protocol was co-designed by clinicians and researchers). Treatment was based on a resource-allocation theory of everyday cognitive challenges, which predicted that automatic strategy use would reduce cognitive demands of everyday activities and therefore reduce cognitive symptoms. Treatment was delivered by speech-language pathologists (SLPs) and occupational therapists (OTs), using a protocol that included a problem-focused intake questionnaire, manualized treatment, and clinician resources. Therapy was delivered individually in six to 10 virtual or in-person sessions over 3-4 weeks. Therapy focused on desired changes in function, scaled using Goal Attainment Scaling. RESULTS: Trained SLPs and OTs delivered STAR-C to 53 U.S. service members and veterans, with treatment fidelity > 95%. Participants and clinicians rated STAR-C as acceptable, feasible, and appropriate, and most participants attained and maintained targets. CONCLUSION: STAR-C appears to be a feasible method for improving everyday cognitive performance and efficacy should be tested in a controlled study. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.28222613.
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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 | 0.056 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".