Increases in caregivers' contributions to household food and non‐food expenditures did not affect child outcomes in the ENAM project
Bibliographic record
Abstract
Interventions that enhance women's incomes are likely to improve child nutrition outcomes. This association may be reduced if women increase their economic contribution primarily to household non‐food rather than food expenditures. The ENAM project is a nutrition education and enterprise development intervention aimed at improving caregiver incomes and children's animal source food (ASF) intakes. We assessed caregivers' contributions to household food and non‐food expenditures and their differential effects on children's ASF diversity and growth. 179 intervention and 287 control caregivers were interviewed about their household expenditures at 4 quarterly time points. Children's ASF intakes and anthropometry were also recorded. There were no between‐group differences at baseline. At the final time point, compared to control households, the intervention households had higher percentage increases in caregiver contribution to food (52.5% ± 40.4% vs 66.2% ± 33.1%; P<0.001) and non‐food (51.8% ± 34.1% vs. 63.9% ± 29.2%; p<0.05) expenditures. However, neither increased caregivers' contributions to food nor non‐food expenditures affected children's ASF diversity or anthropometry. An increase in the proportion of household expenditures coming from caregivers did not affect child nutritional outcomes. Funding: GL‐CRSP, funded in part by USAID, Grant # PCE‐G‐00‐98‐00036‐00.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".