Predictors and consequences of overweight and obesity in the household: A mixed methods study on rural Ghanaian women and men farmers
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».