Executive function assessment and intervention post-stroke: building and translating the evidence into practice
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,104 | 0,289 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,005 |
| Bibliométrie | 0,012 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,006 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».