What is the usage of the Brisbane Evidence Based Language Test in clinical practice?: A speech language therapy survey
Notice bibliographique
Résumé
BACKGROUND: A number of practice barriers (e.g., time constraints, patient comorbidities and competing demands) exist as regards the evidence-based assessment of adult language within the acute hospital setting. There is need for an evidence-based, diagnostically validated, adaptable, comprehensive and efficient aphasia assessment. The Brisbane Evidence Based Language Test (EBLT) was developed to meet this need for a clinically appropriate and diagnostically robust assessment measure. Since the release of the EBLT, there has been no detailed investigation into speech and language therapists' (SLT) use and perceptions of the assessment. To inform future research and improvements to the EBLT, a better understanding of current language assessment practices, use and thoughts on the EBLT are required. AIMS: To investigate SLTs' current language assessment practices; and to explore the current usage and future directions of the Brisbane EBLT. METHODS & PROCEDURES: A convergent parallel mixed-methods study design with an electronic survey was utilized. The survey consisted of 16 multiple-choice, multiple-answer and free-text questions that explored respondents' perceptions of current language assessment measures, ideal language assessment features, Brisbane EBLT use, positives and negatives, and future directions for the EBLT. The survey was developed on Qualtrics and disseminated via email, social media and professional networks. Snowball sampling was used. Study inclusion criteria required participants to be qualified SLTs with clinical experience working with patients with aphasia. Quantitative data were analysed via descriptive and correlative statistics, and qualitative data were analysed via content analysis. OUTCOMES & RESULTS: The survey was completed by 115 SLTs from Australia, New Zealand, the United States, the UK, Canada, France, Lebanon and Belgium. Many respondents identified that a range of assessments is required to meet SLT clinical needs in the assessment of aphasia. Key desirable assessment features reported were: comprehensive, efficient, evidence-based, responsive and flexible. The EBLT was the most frequently used standardized measure reported by respondents (used by 78.63%). The EBLT reportedly has many positive features; however, respondents indicated dislike of the form layout, scoring and responsiveness. The majority of respondents indicated that the development of additional EBLT tests (94.29%) and additional cut-off scores (95.15%) would benefit their clinical practice. CONCLUSIONS & IMPLICATIONS: The study findings indicate that SLT assessment of language is complex and multifaceted. While the EBLT is reportedly used widely by SLTs, respondents identified areas for further research which would optimize the test's usability within their practice, to ultimately improve patient outcomes. WHAT THIS PAPER ADDS: What is already known on the subject Previous studies have explored existing SLT adult language assessment practices within acute settings; however, the most recent comprehensive study was completed in 2010. In 2020, the development, diagnostic validation, intra- and interrater reliability of the Brisbane EBLT were published. As of yet there has not been an investigation into clinician use and perspectives of the assessment since its release. What this paper adds to existing knowledge This study provides contemporary data about international aphasia assessment practices, as well as descriptive and qualitative information on the current use of the Brisbane EBLT, and the positives, negatives, and future directions for the assessment measure. What are the potential or actual clinical implications of this work? The study suggests that no one language assessment currently satisfies all SLTs' assessment needs. It additionally implies that a large proportion of SLTs believe that it is best practice to use multiple language assessment measures and select these based on the patient's presentation and context. Further research is required to aid the development of additional EBLT test versions and cut-off scores to improve SLT adult language assessment practices.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,003 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».