Food Insecure Women with Lower Education Report More Health Problems in a Global Sample of Individuals
Notice bibliographique
Résumé
Because of its intimate relationship with an insufficient dietary intake of nutrients essential for a healthy and active life, food insecurity (FI) is associated with a wide range of physical and mental health issues. Education is linked with better health through multiple pathways, including job prospects and income, risk for disease, intra‐generational effects, and behavioral and social factors. In most societies, especially in developing countries, women are more vulnerable than men to FI and to poor education levels. The present research uses the Food Insecurity Experience Scale (FIES) in the Gallup World Poll (GWP) to examine the impact of FI and education on self‐reported health status across nationally representative samples from 140 countries. This study aimed to ascertain the effects of FI, gender, and education on the likelihood that participants report health problems (HP). It also explored potential interactions in their effect on health. Data from the 2014 GWP were analyzed using IBM SPSS 21 with the Complex Samples module. One question regarding the presence of HP was selected as the outcome of interest. FI was assessed using the 8‐item FIES, considering food insecure (fi) individuals to be those who answered affirmatively to at least one questionnaire item. Multivariable logistic regression analysis was used to determine the effect of the interaction term of FI, education and gender on HP. The model was adjusted for age, income, water quality, and household size. Data was weighted by country population size. The sample included 136,667 individuals, of which 50.1% were female, 24.9% reported having a HP, and 45.3% were fi. Regarding education, 45.4% completed elementary education or less, 46.1% completed up to three years of tertiary education, and 8.5% completed four years beyond ‘high school’ and/or received a college degree. The interaction term (FI, education, and gender) was statistically significant (p < 0.001). When compared to food secure men with the highest education level, fi women with low education level presented the highest odds of reporting HP (OR=5.2). Results reveal increased vulnerability to HP among women, fi individuals, and those with lower education levels, providing evidence that health is determined not only by internal factors as genetics or external ones such as healthy lifestyle, but also by social factors. These social determinants are important to take into account in health‐related policy decisions and program design.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».