An ecological systems analysis of food access barriers and coping strategies adopted by older adults in Ghana
Bibliographic record
Abstract
The 2030 Agenda for Sustainable Development commits to ending hunger and achieving food security for all. However, little is known about the food access experiences of older adults in Ghana. This paper explores if there are barriers to accessing food and if there are, what strategies have been adopted to negotiate these barriers. Through purposive sampling, a total of 123 older adults were recruited in Ghana to participate in sharing circles (n = 10) and semi‐structured interviews (n = 42). Data analyses were done in NVivo using a thematic analytical approach. Functional impairment and poor health, poverty, inadequate social support, lack of control over household resources, policy neglect, low crop yields, and sociocultural values were found to hinder older people's access to food. They navigate these barriers by rationing meals, engaging in income‐earning activities, seeking support from social networks, and begging. Our findings contribute to discourses on food security and highlight the need for a multilevel, comprehensive approach that targets both the individual and the broader human environment in addressing the food needs of older people.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".