Maternal Participation Level in a Nutrition-Sensitive Agriculture Intervention Matters for Child Diet and Growth Outcomes in Rural Ghana
Bibliographic record
Abstract
Little is known about how the level of program participation affects child nutrition in rural interventions. This study examined the association between participation level in a nutrition-sensitive agriculture intervention and children's diet and anthropometric outcomes in rural Ghana. Nutrition Links was a cluster randomized controlled trial (clinicaltrials.gov NCT01985243), which enrolled caregivers with children (aged less than 2 mo in 2014–2015 and less than 18 mo in 2016–2017). Of the 287 caregivers in 19 intervention communities who enrolled, 233 adopted the intervention and received layer poultry, garden inputs, and weekly child feeding education. The egg production and repayment of poultry were monitored, and feed was sold at the weekly meetings. After endline, the nutrition educators rated each woman who adopted the intervention on a scale [very poor (1) to excellent (5)] for: 1) meeting attendance, 2) egg productivity, 3) feed and poultry loan payment, 4) contributions during meetings, and 5) attentiveness towards group members. Participation level was classified as high, medium, and low by dividing the sum of these 5 items into tertiles; 54 women who did not adopt the intervention were classified as “no participation.” Generalized mixed linear models tested the difference in changes in children's diet and anthropometric indices between the participation levels and the control category – 213 caregiver-child dyads in 20 communities who received standard-of-care health and agricultural services. Compared with the control category, only high participation was associated with egg consumption [adjusted OR (aOR) = 3.03; 95% CI: 1.15, 7.94]. Both medium and high participation levels were associated with length‐for‐age z‐scores (LAZ)/height‐for‐age z‐scores (HAZ) [adjusted β-coefficients (aβ) = 0.44; 95% CI: 0.16, 0.72 and 0.40; 95% CI: 0.12, 0.67, respectively]. These results highlight the importance of promoting and monitoring the level of beneficiary participation to estimate the full potential of nutrition-sensitive agriculture interventions to improve nutritional outcomes.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".