The Feasibility of Using Metacognitive Strategy Training to Improve Performance, Foster Participation, and Reduce Impairment Following Neurological Injury
Bibliographic record
Abstract
Executive function is central to our ability to learn and participate in everyday life activities and rehabilitation outcomes for individuals with executive dysfunction after neurological injury are poor. The impairments and performance challenges these individuals experience are typically not identified appropriately so they often do not receive adequate rehabilitation and can have significant challenges returning to complex everyday life activities. The vast majority of rehabilitation efforts to support individuals with neurological injuries with executive dysfunction are based on a restoration model that aims to improve cognitive function with the expectation that these gains will translate to everyday life. The available evidence suggests this translation is not happening as improvement in cognitive performance is often not leading to improvement in everyday life activities. Performance-based interventions that target improved engagement in everyday life activity are being developed with the expectation that this approach will remediate/mitigate impairments; however, these performance-based approaches have not been adequately evaluated. The purpose of this dissertation was to evaluate the feasibility and preliminary efficacy of a performance-based intervention approach, metacognitive-strategy training, on performance and impairment reduction in individuals with central neurological injury.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".