The advance directive prevalence in long‐term care: A comparison of relationships between a nurse practitioner healthcare model and a traditional healthcare model
Notice bibliographique
Résumé
PURPOSE: The purpose of this study was to examine rates of completion of advance directives (ADs) among institutionalized older adults in three geographically diverse areas of the country--Arizona, Georgia, and Massachusetts. Comparisons among four variables--gender, race, education, and type of healthcare model (Evercare vs. non-Evercare), related to AD completion rates were examined. DATA SOURCES: This study was a secondary data analysis using deidentified data from 11,775 older adults enrolled in the Evercare healthcare model to 91,443 non-Evercare older adults (Minimum Data Set) during the last quarter of 2004. Chi-square analysis was used to examine any differences in gender, race, education, and healthcare model associated with the completion rates of ADs. CONCLUSIONS: The Evercare healthcare model that used nurse practitioners (NPs) consistently had significantly higher (p < .001) completion rates of ADs compared to the non-Evercare healthcare model that did not use NPs. Black people and white people in the Evercare healthcare model had similar rates of AD completion (p > .001), which is contrary to previous findings where black people had a lower completion rate. Males and females in the Evercare healthcare model had similar rates of AD completion (p > .001), which is also contrary to previous findings where females had a higher completion rate. Finally, older adults with a high school education or less and older adults with greater than a high school education in the Evercare healthcare model had similar rates of AD completion (p > .001), which is contrary to previous findings where individuals with increased education had a higher completion rate. IMPLICATIONS FOR PRACTICE: With the increasing number of older adults in the general and the long-term care population, older adults should be encouraged to complete their ADs when discussing their medical decisions with their healthcare providers. Through the use of the Evercare healthcare model, NPs are well prepared to assist their clients and families in identifying these decisions. As a result, a significantly greater proportion of ADs have been completed by individuals enrolled in the Evercare healthcare model when compared to non-Evercare individuals living in long-term care settings. By using this model, Evercare NPs ensure that the specific medical choices of their patients are carried out.
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 | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».