A mixed-method evaluation of a volunteer navigation intervention for older persons living with chronic illness (Nav-CARE): findings from a knowledge translation study
Notice bibliographique
Résumé
BACKGROUND: Volunteer navigation is an innovative way to help older persons get connected to resources in their community that they may not know about or have difficulty accessing. Nav-CARE is an intervention in which volunteers, who are trained in navigation, provide services for older persons living at home with chronic illness to improve their quality of life. The goal of this study was to evaluate the impact of Nav-CARE on volunteers, older persons, and family participating across eight Canadian sites. METHODS: Nav-CARE was implemented using a knowledge translation approach in eight sites using a 12- or 18-month intervention period. A mixed method evaluation was used to understand the outcomes upon older person engagement; volunteer self-efficacy; and older person, family, and volunteer quality of life and satisfaction with the intervention. RESULTS: Older persons and family were highly satisfied with the intervention, citing benefits of social connection and support, help with negotiating the social aspects of healthcare, access to cost-effective resources, and family respite. They were less satisfied with the practical help available for transportation and errands. Older persons self-reported knowledge of the services available to them and confidence in making decisions about their healthcare showed statistically significant improvements (P < .05) over 12-18 months. Volunteers reported satisfaction with their role, particularly as it related to building relationships over time, and good self-efficacy. Volunteer attrition was a result of not recruiting older persons in a timely manner. There was no statistically significant improvement in quality of life for older persons, family or volunteers from baseline to study completion. CONCLUSIONS: Findings from this study support a developing body of evidence showing the contributions volunteers make to enhanced older person and family well-being in the context of chronic illness. Statistically significant improvements were documented in aspects of client engagement. However, there were no statistically significant improvements in quality of life scores even though qualitative data illustrated very specific positive outcomes of the intervention. Similar findings in other volunteer-led intervention studies raise the question of whether there is a need for targeted volunteer-sensitive outcome measures.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».