Executive function assessment and intervention post-stroke: building and translating the evidence into practice
Bibliographic record
Abstract
Deficits in executive functions (EF), such as planning, problem-solving and inhibition affect up to 75% of individuals with stroke and may compromise their ability to successfully return to community living and to work. Detection and effective treatment of these disorders is thus critical. Studies over the past decade have provided evidence of substantial gaps in our knowledge on how to effectively manage EF impairment post-stroke (Bayley et al., 2007; Canadian Stroke Network, 2008; Korner-Bitensky, Barrett-Bernstein, Bibas, & Poulin, 2011). To address these gaps there has been growing attention and research into the management of EF impairment post-stroke. The studies conducted as part of this thesis were designed to address some of these gaps specific to EF assessment and intervention research, and to promote increased use of evidence-based practices for the management of executive dysfunction post-stroke. The first manuscript provided a critical review of 17 performance-based EF tools that can be used across the continuum of stroke care to evaluate the daily consequences of executive dysfunction. The next step was to conduct a systematic review to identify and critically appraise the evidence for the use of specific EF interventions post-stroke. The systematic review of EF interventions described in the second manuscript identified different treatment approaches that were showing promise in helping persons with stroke to cope with EF deficits. The preliminary evidence on specific EF skill retraining suggested that structured, individualized and intense computerized EF training could improve targeted EF impairments (Stablum, Umilta, Mogentale, Carlan, & Guerrini, 2000; Westerberg et al., 2007). The evidence from studies on cognitive strategy training also supported the use of explicit strategies applied to ecologically relevant problems to improve some EF impairments (e.g., planning and problem-solving) and, possibly, real-world activities (Man, Soong, Tam, & Hui-Chan, 2006; Schweizer et al., 2008). However, further research was required to compare the impact of these different intervention approaches on a variety of outcomes. Accordingly, a pilot randomized controlled trial was conducted to determine the feasibility and preliminary efficacy of two promising interventions, a strategy-training approach – the Cognitive Orientation to daily Occupational Performance (CO-OP) approach which is based on the use of meta-cognitive problem-solving strategies to achieve self-selected functional goals – and a computer-based EF training program (see Manuscript 3). Our findings provide preliminary evidence supporting the feasibility and efficacy of using both CO-OP and Computerized EF training for select patients with executive dysfunction post-stroke. EF impairments and participation in everyday life were differentially impacted by the interventions.Finally, another important goal of my doctoral work was to enhance knowledge translation in the area of EF. As explained in the fourth manuscript, the thesis led to the creation of a series of web-based interactive learning modules on EF assessment and intervention, as well as user-friendly pocket cards designed to summarize EF rehabilitation best-practices for clinicians. These e-learning modules address the need to enhance expertise in the management of EF disorders post-stroke.
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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 | 0.104 | 0.289 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| 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".