Frailty Screening and Management for Older Australians in General Practice: Mixed Methods Evaluation
Notice bibliographique
Résumé
Background: Frailty increases with age and is associated with increased vulnerability to adverse health outcomes. International guidelines recommend screening for frailty in primary care; however, this is not routine practice in Australia. Once identified, frailty progression has the potential to be halted or reversed with early intervention. The FRAIL (Fatigue, Resistance, Ambulation, Illnesses, Loss of weight) Scale Tool, a simple and validated screening and management tool, offers a feasible approach for integration into the Australian health assessment for those aged 75 years and older (75+HA), which can be performed annually by primary care providers. Objective: This study explores the rates of frailty, resources required to support management, and the determinants of implementing frailty screening and providing management for older Australians at the 75+HA. Methods: A mixed methods evaluation was conducted in 24 general practices across 2 Australian Primary Health Network regions, Sydney North and Brisbane South. The FRAIL Scale Tool was implemented during the 75+ health assessment, and data were collected on FRAIL Scale scores, hospitalization rates, recommended frailty interventions, and barriers to frailty management. Practice staff perceptions of the long-term sustainment of the FRAIL Scale Tool were assessed using the Provider Report of Sustainment Scale. Semistructured qualitative interviews were conducted with practice staff and patients, exploring barriers and enablers to implementing frailty screening and management. Guided by the Consolidated Framework for Implementation Research, transcripts were coded and themes developed. Results: Of the 1484 patients aged ≥75 years who were screened, 223 (15%) patients were frail, 616 (41.5%) patients were prefrail, and 645 (43.5%) patients were robust. People who were frail were more likely to be female, older, and have more prescribed medications. Of those screened as frail, 23 (11%) had a nonelective hospitalization in the 3 months prior to screening compared with 28 (5%) who screened as prefrail and 5 (1%) who screened as robust (P=.012). Management recommendations commonly included medication reviews, aged care packages, assessment for depression, and exercise programs. Barriers identified to accessing interventions included health, transport, cost, and time. Survey and qualitative findings highlighted that the FRAIL Scale Tool was easy to use, integrated well into existing workflows as part of the 75+HA, and sustained use would be supported by software integration. Patients valued the assessment and tailored health support offered by trusted primary care providers. Conclusions: Incorporating the FRAIL Scale Tool into the annual health assessment for people aged 75 years and older provides a funded opportunity for addressing frailty in general practice. Patients and staff value the Tool's simplicity and the opportunity to raise awareness and manage frailty proactively. Incorporating the Tool into practice software systems would enhance adoption. Broader implementation research in diverse settings and with Aboriginal and Torres Strait Islander populations is needed to improve frailty prevention and management.
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 | 0,113 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».