Predicting wellness among rural older Australians: a cross-sectional study
Notice bibliographique
Résumé
INTRODUCTION: Prior research on older people's wellbeing and quality of life has lacked clarity and consistency. Research examining older people's health has tended to use these different terms and measurement tools interchangeably, which might explain why the evidence is somewhat mixed. There is a paucity of research that uses the multi-dimensional construct of wellness in rural older people. Addressing both limitations, this study seeks to make a unique contribution to knowledge testing an ecological model of wellness that includes intrapersonal factors, interpersonal processes, institutional factors, community factors and public policy. METHODS: Six rural case study sites were chosen across two Australian sites, the states of Queensland and Victoria. A community saturation recruitment strategy was utilised. Telephone surveys were conducted with community-dwelling rural older people (n=266) aged ≥65 years across the sites. The central variable of the study was wellness as measured by the Perceived Wellness Survey. The ecological model developed included the following intrapersonal factors: physical and mental health, loneliness and social demographic characteristics (age, sex, marital status and financial capability). Interpersonal factors included a measure of social and community group participation, social network size and support provided. Institutional factors were measured by series of questions devised around the resource base environment and access to amenities and services. RESULTS: A hierarchical regression analysis was conducted to determine which variables in the model predict wellness. The results showed that a combination of intrapersonal factors (physical health, mental health, loneliness and financial capability) and interpersonal factors (size of social network and community participation) predicted wellness. However, institutional factors, the resource base environment, and access to amenities and services, contributed only marginally to the model. Community factors, including the personal and physical characteristics of community, also only made a marginal contribution. CONCLUSIONS: The study identified the usefulness of using an integrated model of measurement in wellness. This model recognised the interrelated physical, social and economic influences that impact on rural older people throughout their life course. The study found that physical health made the greatest contribution to perceived wellness, followed by mental health. These findings support a body of research that has found that rural older people experience poorer health outcomes than those in urban areas. Lower levels of loneliness were also a strong predictor of perceived wellness, thus supporting research that has examined the impact of loneliness on physical and mental health. The presence of social capital, as measured by social network size, and the degree of community participation, were also predictors of perceived wellness. Overall, the findings of the present study implications for policy as well as subsequent strategies designed to increase the capacity of wellness in rural older people. Such strategies need to consider the contribution of a range of factors.
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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 | 0,000 |
| 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 | 0,003 | 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 ».