Food Insecurity as a Social Determinant of Self-Rated Mental Health of Older Adults in Ghana
Bibliographic record
Abstract
This study examines the association between food security and the self-rated mental health of a representative sample (n = 1,073) of people 60 years and older in three regions in Ghana. A cross-sectional study design was employed, and data were analyzed using logistic regression techniques. Overall, 27% of the respondents rated their mental health as poor, while 64% reported food insecurity. Results from logistic regression analyses reveal that older people who were food insecure were more likely to rate their mental health as poor compared to those who are food secure, while accounting for theoretically relevant variables informed by the Social Determinants of Health framework (OR = 2.27; p < .001). This finding points to the link between food insecurity and poor mental health among older people in Ghana, drawing attention to food security as an important social determinant of mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".