Maternal Participation in a Nutrition-Sensitive Agriculture Intervention Matters for Child Diet and Growth Outcomes in Rural Ghana
Bibliographic record
Abstract
Little is known about how level of participation affects nutrition outcomes in rural interventions. This study examined the association between participation level in a nutrition-sensitive agriculture intervention (NSA) and children's diet and anthropometric outcomes. The Nutrition Links was a 2016–17 cluster RCT (clinicaltrials.gov NCT01985243) which enrolled caregivers with children < 18 mo in rural Ghana. Women in the intervention communities self-selected to receive poultry layers for egg production as part of a loan package, garden inputs, and nutrition education. Weekly meetings were used for education, for the staff to document egg production, and for the women to repay their loan and purchase feed. After endline, project participation was evaluated with a summative score that reflected 5 criteria: 1) egg productivity 2) timely and complete payment for feed and loan 3) meeting attendance 4) answering questions and making comments during the meetings and 5) attentiveness and helpfulness with the group. The field staff evaluated all criteria – from 1 (very poor) to 5 (excellent) – for each intervention woman who received poultry. Their participation was classified as high, medium, or low, based on tertiles. The participation of intervention women who never received poultry was ‘none’. Logistic and linear regressions tested the likelihood of consuming eggs and having a minimum diverse diet (MDD) and changes in anthropometric indices for the participation levels compared to those of the controls, who received standard-of-care services. In comparison to the control group, only a high participation level was significant for having a MDD (aOR = 3.1, 95% CI [1.1, 8.8]). Both medium and high participation levels were associated with an increased likelihood in egg consumption (aOR = 2.1, 95% CI [1.0, 4.1]; aOR = 2.7, 95% CI [1.3, 5.4]), and length‐for‐age (LAZ)/height‐for‐age (HAZ) z‐scores (β = 0.4, 95% CI [0.2, 0.6]; β = 0.4, 95% CI [0.2, 0.7]), and weight‐for‐age (WAZ) (β = 0.2, 95% CI [0.0, 0.4]; β = 0.2, 95% CI [0.0, 0.5]), respectively. These results show the potential effect of NSA interventions and highlight the importance of facilitating high participation of beneficiaries. Global Affairs Canada, Heifer International, World Vision, McGill University, International Development Research Centre.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".