Food Insecurity Over the Life Course and Intimate Partner Violence Among Women Living With <scp>HIV</scp> / <scp>AIDS</scp> in Ghana
Bibliographic record
Abstract
Intimate partner violence (IPV) is a significant problem globally. It is especially problematic in sub‐Saharan Africa, including Ghana. Some evidence suggests women living with HIV/AIDS (WLHIV) are more severely affected than others. A possible reason for their increased vulnerability is food insecurity. Food insecurity occurs when nutritionally adequate and safe foods are not available or inaccessible and is disproportionately high among WLHIV. The study examined the effects of food insecurity over the life course on IPV among WLHIV in Ghana. It used data from about 1,007 ever‐married Ghanaian WLHIV attending routine check‐ups in the Eastern Region of Ghana. Binary logit models examined the effects of food insecurity over the life course on physical, sexual, emotional, economic, and spiritual IPV. Both childhood and adulthood food insecurity were significantly associated with IPV. Women who experienced food insecurity in childhood and adulthood were significantly more likely to report all five types of IPV than those who did not. The largest association occurs when childhood food insecurity continues into adulthood. Our findings demonstrate the cumulative and intergenerational relationship between food insecurity and IPV among WLHIV in Ghana, thus calling for interventions to target children at risk.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".