Family member eating assistance and food intake in long‐term care: A secondary data analysis of the M3 Study
Notice bibliographique
Résumé
AIM: To determine if protein and energy intake is significantly associated with a family member providing eating assistance to residents in long-term care homes as compared with staff providing this assistance, when adjusting for other covariates. BACKGROUND: Who provides eating support has the potential to improve resident food intake. Little is known about family eating assistance and if this is associated with resident food intake in long-term care. DESIGN: Cross-sectional, secondary data analysis. METHODS: Between October and January 2016, multilevel data were collected from 32 long-term care homes across four Canadian provinces. Data included 3-day weighed/observed food intake, mealtime observations, physical dining room assessments, health record review, and staff report of care needs. Residents where family provided eating assistance were compared with residents who received staff-only assistance. Regression analysis determined the association of energy and protein intake with family eating assistance versus staff assistance while adjusting for covariates. RESULTS: Of those residents who required any physical eating assistance (N = 147), 38% (N = 56) had family assistance during at least one of nine meals observed. Residents who received family assistance (N = 56) and those who did not (N = 91) were statistically different in several of their physiological eating abilities. When adjusting for covariates, family assistance was associated with significantly higher consumption of protein and energy intake. CONCLUSION: Energy and protein intake is significantly higher when family provides eating assistance. Family are encouraged to provide this direct care if it is required. IMPACT: Residents who struggle with independent eating can benefit from dedicated support during mealtimes. Findings from this study provide empirical evidence that family eating assistance is associated with improved resident food intake and provides strong justification to encourage families to be active partners in the care and well-being of their relatives. Home administrators and nursing staff should support the specialized care that families can provide at mealtimes.
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 | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».