Staff perceptions of using outcome measures in stroke rehabilitation
Notice bibliographique
Résumé
PURPOSE: The use of standardised outcome measures is an integral part of stroke rehabilitation and is widely recommended as good practice. However, little is known about how measures are actually used or their impact. This study aimed to identify current clinical practice; how healthcare professionals working in stroke rehabilitation use outcome measures and their perceptions of the benefits and barriers to use. METHOD: Eighty-four Health Care Professionals and 12 service managers and commissioners working in stroke services across a large UK county were surveyed by postal questionnaire. RESULTS: Ninety-six percent of clinical respondents used at least one measure, however, less than half used measures regularly during a patient's stay. The mean number of tools used was 3.2 (SD = 1.9). Eighty-one different tools were identified; 16 of which were unpublished and unvalidated. Perceived barriers in using outcome measures in day-to-day clinical practice included lack of resources (time and training) and lack of knowledge of appropriate measures. Benefits identified were to demonstrate the effectiveness of rehabilitation interventions and monitor patients' progress. CONCLUSIONS: Although the use of outcome measures is prevalent in clinical practice, there is little consistency in the tools utilised. The term "outcome measures" is used, but staff rarely used the measures at appropriate time points to formally assess and evaluate outcome. The term "measurement tool" more accurately reflects the purposes to which they were put and potential benefits. Further research to overcome the barriers in using standardised measurement tools and evaluate the impact of implementation on clinical practice is needed. IMPLICATIONS FOR REHABILITATION: • Health professionals working in stroke rehabilitation should work together to agree when and how outcome measures can be most effectively used in their service. • Efforts should be made to ensure that standardised tools are used to measure outcome at set time-points during rehabilitation, in order to achieve the anticipated benefits. • Communication between service providers and commissioners could be improved to highlight the barriers in using standardised measures of outcome.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».