A Photovoice Study Investigating Inequalities in Nutrition and Healthy Ageing in Older Black African Adults in the United Kingdom
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".