The role of child nutrition counselling, gender dynamics, and intra-household feeding decision-making on child dietary diversity in semi-arid northern Ghana
Bibliographic record
Abstract
Every child has the right to proper nutrition. As such, child nutrition counselling is recommended to improve child dietary diversity and reduce malnutrition. However, there is limited empirical evidence on how child nutrition counselling, gender dynamics, and intra-household feeding decisions play a role and translate to child dietary diversity in rural contexts. Informed by theoretical conceptions from Health Belief Model (HBM) and Gender and Development (GAD) framework, we analyzed cross-sectional data from 517 smallholder households in Ghana's semi-arid Upper West Region to investigate the relationship between child nutrition counselling, gender dynamics, intra-household feeding decision-making, and their impact on child dietary diversity. Results from ordered logistic regression show that households that received child nutrition counselling reported higher child dietary diversity. Joint intra-household feeding decisions were associated with higher child dietary diversity. Households with good self-rated childcare, engaged in home gardening, and higher wealth, as well as those in the Waala ethnic group, were more likely to have high child dietary diversity. A decrease in household head age was linked to increased high child dietary diversity. On the other hand, female-headed households, Brifo ethnic groups, and those in the Wa East and Wa West districts were less likely to experience high child dietary diversity. Implementing interventions and policies prioritizing nutrition education in the Upper West Region and similar sub-Saharan African contexts is recommended. Strategies like scaling up child nutrition counselling, food demonstrations, mother-to-mother support nutrition outreach, and mobile nutrition clinics can empower women and improve children's well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".