A Photovoice Study Investigating Inequalities in Nutrition and Healthy Ageing in Older Black African Adults in the United Kingdom
Notice bibliographique
Résumé
Objectives: Older people from Black African communities (both born in the UK and migrants) often experience a complex nutrition landscape where there is maintenance of traditional diets but also adaptation of key features of the UK/Western diet and food culture, with negative outcomes for nutrition and health in older age. The aim of this study was to understand the factors that underlie inequalities in nutrition and health and to obtain insights to co-design innovative culturally tailored interventions to improve nutrition and health in older African adults. Methods: Qualitative data was collected using Photovoice, a visual, community-based participatory research (CBPR) method whereby participants take photographs to document, reflect upon health and social issues from their own perspective. A purposive sample of 12 participants were provided with cameras and encouraged to take photos describing their experiences and thoughts on factors that influence nutrition and healthy ageing in older African adults. Semi-structured interviews were conducted to gain insights into the photos. Thematic analyses using both deductive and inductive approaches were conducted to develop and refine emerging themes. Results: Participants were older African adults, 62±5.4 years and 75% female. The majority were married (58.3%), living with family (41.7%), educated to postgraduate degree level (50.0%) and fulltime employed (66.7%). Emerging themes influencing nutrition and healthy ageing included time, social isolation, health status, tradition, cooking methods, religious factors and finances. While participants exhibited a good level of nutrition knowledge and were able to characterize the features of unhealthy and healthy diets, there were still misconceptions of what constituted a healthy diet. Conclusions: This research provides the first evidence using photovoice, a novel participatory research method to investigate factors that underlie inequalities in nutrition, in older African adults. The findings highlight significant determinants that influence nutrition and healthy ageing and emphasizes the need for further research to co-create culturally tailored interventions that improve nutrition, healthy ageing and quality of life in older African adults. Funding Sources: Research is funded by the UKRI BBSRC/MRC Food4Years Network.
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 | 0,008 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| 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 | 0,001 |
| 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 ».