Predictors and consequences of overweight and obesity in the household: A mixed methods study on rural Ghanaian women and men farmers
Bibliographic record
Abstract
Overweight/obesity (OW/OB) rates are increasing in Ghana. This study aims to identify the predictors of OW/OB in women, men, and at the household level (having at least one person as OW/OB in the household) in rural Ghana and examine local perceptions of the consequences of having an OW/OB person in the household. This was a cross-sectional mixed methods study. The quantitative data was a secondary analysis of the baseline data from the LinkINg Up (LU) project; a nutrition-sensitive agriculture intervention in eight rural communities in the Eastern Region of Ghana (ClinicalTrials.gov NCT03869853). The sample included 331 women and 205 men, 19-90 years old; there were 196 households that had both a participating woman and man (spouse, son, brother, or father). Logistic regression was used to assess variables associated with OW/OB in women (n=322), men (n=205), and households (n=196). Exposure variables included age, social support, mental health, self-efficacy, food security, the other family members’ OW/OB status, and others. Qualitative data included six focus group discussions (FGDs) (three with women and three with men, aged 22-69 years and recruited from the comparison arm of the LU project) were conducted in February-March 2022 in three of the eight project communities. A structured guide and a body figure instrument were used. The FGD recordings were translated and transcribed from Krobo to English. The analysis used an inductive thematic approach. Both women and men’s OW/OB were positively associated with age and wealth. Women’s OW/OB was negatively associated with age squared, and the score for mental health symptoms. Men’s OW/OB was negatively associated with being Krobo compared to other ethnicities. Households in the highest wealth tertile were 2.5-fold more likely to have at least one person who is OW/OB as compared to households in a lower wealth tertile. Participants expressed positive social consequences of having an OW/OB person for their families (for example respect). A person’s size was concerning only when it affected one’s ability to farm or make money, which would harm the household unit (for example lead to food insecurity, children dropping out of school). Having money was seen as a modifier for the negative effects. No negative consequences were perceived for OW people. The implications of the interruption of an OB person’s work on their family are worrisome and call for interventions that address poverty and food insecurity along with nutrition. Key words: Africa, farmer, household, social norms, perceptions, body image, overweight, obesity, predictors
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".