Does social support modify the relationship between food insecurity and poor mental health? Evidence from thirty-nine sub-Saharan African countries
Bibliographic record
Abstract
OBJECTIVE: The present study aimed to determine the relationship among food insecurity, social support and mental well-being in sub-Saharan Africa, a region presenting the highest prevalence of severe food insecurity and a critical scarcity of mental health care. DESIGN: Food insecurity was measured using the Food Insecurity Experience Scale (FIES). Social support was assessed using dichotomous indicators of perceived, foreign perceived, received, given, integrative and emotional support. The Negative and Positive Experience Indices (NEI and PEI) were used as indicators of mental well-being. Multilevel mixed-effect linear models were applied to examine the associations between mental well-being and food security status, social support and their interaction, respectively, accounting for random effects at country level and covariates.ParticipantsNationally representative adults surveyed through Gallup World Poll between 2014 and 2016 in thirty-nine sub-Saharan African countries (n 102 235). RESULTS: The prevalence of severe food insecurity was 39 %. The prevalence of social support ranged from 30 to 72 % by type. In the pooled analysis using the adjusted model, food insecurity was dose-responsively associated with increased NEI and decreased PEI. Perceived, integrative and emotional support were associated with lower NEI and higher PEI. The differences in NEI and PEI between people with and without social support were the greatest among the most severely food insecure. CONCLUSIONS: Both food insecurity and lack of social support constitute sources of vulnerability to poor mental well-being. Social support appears to modify the relationship between food security and mental well-being among those most affected by food insecurity in sub-Saharan Africa.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".