Multi-method study of the implementation of Cognitive Symptom Management and Rehabilitation Training (CogSMART) in real-world settings
Bibliographic record
Abstract
Cognitive Symptom Management and Rehabilitation Training (CogSMART) and Compensatory Cognitive Training (CCT) are evidence-based compensatory cognitive training interventions that improve cognition in persons with a history of traumatic brain injury or other neuropsychiatric disorders. Despite demonstrated efficacy, use and effectiveness of CogSMART/CCT in real-world settings is not known.We used a multi-method design to collect and analyze quantitative and qualitative survey data from several domains of the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework to gather information about use of CogSMART/CCT in real-world settings from provider and patient perspectives. Surveys were sent to email addresses from persons who registered on the CogSMART website seeking access to training manuals and other resources. Descriptive statistics were generated, and we used Natural Language Processing methods to study the self-report free responses. Using n-gram analysis, we identified the most frequently reported responses.We found CogSMART/CCT was broadly used in real-world settings and delivered by a variety of providers for several patient groups with high attendance and overall high satisfaction. CogSMART/CCT seemed to be offered in VA- or university-related clinics more than in private practice or rehabilitation centers. The diversity of providers and variety of formats by which to deliver CogSMART/CCT (i.e., individual, group, telehealth) seemed to play a role in its widespread implementation, as did its adaptability. Most providers made adaptations to the intervention that reduced the length or number of sessions. These changes were most likely to be based on client characteristics. The low rates of formal training, however, may have contributed to lower levels of perceived helpfulness among patients.Reach and Adoption of a cognitive rehabilitation intervention improved by increasing access to the manuals. Attention to characteristics of dissemination and implementation in the design of an intervention may enhance its use in real-world settings. The relevant outcomes, easy access to training manuals, and adaptability of CogSMART/CCT seem to have been important factors in its use in a variety of settings and for several disorders with cognitive impairment. The adoption of CogSMART/CCT by a variety of providers other than neuropsychologists suggests its use may be broadened to other healthcare providers, if adequately trained, to increase access to an intervention with demonstrated efficacy for cognitive rehabilitation for several neuropsychiatric disorders.
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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 | 0.039 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".