Skill Generalization Following Computer-Based Cognitive Retraining Among Individuals with Acquired Brain Injury
Bibliographic record
Abstract
Individuals with acquired brain injury (ABI) often experience cognitive deficits. This creates many challenges in learning or relearning skills and generalizing skills among different contexts and task demands. Computer-Based Cognitive Retraining (CBCR) is a common intervention utilized by occupational therapists to help remediate cognitive deficits in individuals with ABI. Although research has shown that CBCR programs are effective at improving cognitive domains, there is limited evidence to support generalization of these skills to functional daily living tasks. Therefore, the primary purpose of this study was to assess the occurrence of generalizing gained skills in overall cognition, attention, and memory from a CBCR program to a medication-box task in individuals with ABI. This study utilized the Parrot Software for the CBCR intervention and evaluated changes in overall cognition, attention, and memory skills with the Montreal Cognitive Assessment (MoCA©), and generalization of those skills utilizing a performance-based medication-box task. The results indicated that the Parrot Software CBCR was effective at improving overall cognition, but not significantly in any particular cognitive domain. In addition, the gains in overall cognition failed to generalize to improved performance in the medication-box task. Extraneous variables did not affect the changes in cognition. However, participants without previous CBCR experience improved significantly when compared to participants with previous CBCR experience. Future areas of research should include interventions that can bridge the gap between CBCR and performance in daily living tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".