Identifying clinicians’ priorities for the implementation of best practices in cognitive rehabilitation post-acquired brain injury
Notice bibliographique
Résumé
Purpose To identify clinicians’ perceptions of current levels of implementation of cognitive rehabilitation best practices, as well as individual and consensual group priorities for implementing cognitive rehabilitation interventions as part of a multi-site integrated knowledge translation initiative.Method A two-step consensus-building methodology was used, that is the Technique for Research of Information by Animation of a Group of Experts (TRIAGE), including a cross-sectional electronic survey followed by consensual in-person group discussions to identify implementation priorities from a list of evidence-based practices for cognitive rehabilitation following traumatic brain injury and stroke. Thirty-eight professionals from three rehabilitation teams (n = 9, 13 and 16) participated, including neuropsychologists, occupational therapists, speech-language pathologists, educators, clinical coordinators and program managers. Descriptive statistics were used to document the perceived levels of implementation as well as individual and consensual group priorities.Results Most of the best practices (81–100%) were perceived as at least partially implemented by a minimum of 50% of the participants but only 20–25% of the practices were considered fully implemented. Findings suggest that current practices are mostly consistent with general cognitive rehabilitation principles suggested in guidelines but that further efforts are needed to support the application of specific cognitive rehabilitation strategies and interventions. Executive function and self-awareness retraining, as well as interventions promoting the generalization of skills, were among the highest implementation priorities. Consensual in-person group discussions, included as part of the TRIAGE process, also helped to define and operationalize these best practices into more specific intervention components according to the teams’ needs and priorities.Conclusions TRIAGE consensus-building methodology can be used to engage stakeholders and support clinicians’ decision-making regarding the identification of implementation priorities in cognitive rehabilitation post-ABI in order to tailor the implementation process to local needs.IMPLICATIONS FOR REHABILITATIONThe Technique for Research of Information by Animation of a Group of Experts (TRIAGE) can be used to support clinicians’ decision-making regarding the identification of implementation priorities in cognitive rehabilitation post-ABI.The combination of individual consultations followed by consensual in-person group discussions, as part of the TRIAGE process, may help clinicians in defining and operationalizing best practices into more specific intervention components to implement.Effective implementation strategies are needed to support the use of specific cognitive rehabilitation interventions in prioritized areas, such as executive function and self-awareness retraining, as well as generalization of skills.Some differences in clinicians’ perceived priorities point up the importance of tailoring implementation to local needs and contexts from the early stages in the process.
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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 | 0,079 | 0,177 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».