Maternal Education and Home Environment Quality Protect Infants in Low Asset Families From Poor Growth
Notice bibliographique
Résumé
OBJECTIVE Economic inequities are common in low and middle‐income countries, and are associated with poor growth among young children. To examine whether maternal education and home environment quality attenuate the association between economic inequities and children's growth. METHODS The sample included baseline data from 512 infants and 321 preschoolers in 26 villages in rural India (Project Grow Smart). Data collection included child growth (weight and length/height measured and converted to wt/age, ln/ht/age and BMI/age z‐scores, WAZ, LAZ/HAZ, BAZ) and hemoglobin (Hb); maternal education, wt/ht and Hb; and economic inequities measured by household assets (weighted score 0–8) and observations of home environment quality (HOME Inventory). Maternal education (completion of primary school or beyond) and home quality (top quartile) were combined into a 3‐level education/home protective factor (PF; high education and home quality, neither [0], either [1], both [2]). Data were analyzed using linear mixed models for infants and preschoolers separately, adjusted for gender and clustering within villages, and including asset by PF interactions. Interactions were interpreted at low/high assets (mean±SD). RESULTS Findings for infants/preschoolers: mean age 8.6/36.6 mo; underweight (WAZ<−2): 18.9%/45.9%; stunting (LAZ/HAZ<−2): 19.5%/40.6%; anemic (Hb<11.0 g/dL): 68.8%/48.6%. Findings for mothers of infants/preschoolers: mean age: 22.9/25.0 y; underweight (BMI<18.5): 37.8%/43.0%; anemic (Hb<12.0 g/dL): 43.7%/34.2%, primary school or beyond: 75.1%/55.2%. Among infants, relationships between assets and WAZ and LAZ are significantly attenuated by PF (p<0.01 for both) and relationships between assets and BAZ are marginally attenuated (p<0.10). Among infants, at low asset levels, PF accounts for 1.3 difference in WAZ (β= 0.65, p<0.01), a 1.38 difference in LAZ (β=0.69, p<0.01), and a 0.7 difference in BAZ (β=0.35, p<0.01). At high asset levels, the WAZ gap between children with/without PF narrows by 0.14 for 1 PF and 0.28 for both PFs (interaction β=0.14, p<0.01) (); the LAZ gap narrows by 0.13 for 1 PF and 0.26 for both PFs (interaction β=0.13, p<0.01); and the BAZ gap narrows by 0.08 for 1 PF and 0.16 for both PFs (interaction β=0.08, p<0.10). Among preschoolers, PF have a marginal independent association with BAZ (p<0.10), with no attenuation in relationships between assets and children's growth. CONCLUSIONS Maternal education and home environment quality may protect infants in low‐asset families from poor growth, illustrating the importance of the care giving context in offsetting the negative consequences of economic inequities on growth during infancy. Among preschoolers, maternal education and home environment quality do not provide growth protection from economic inequities in areas with high rates of stunting. To promote infant growth in low‐asset families, findings support early responsive care giving intervention. Support or Funding Information Micronutrient Initiative, The Mathile Institute for the Advancement of Human Nutrition
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».