An integrated economic and education intervention (the ENAM project) decreased household food insecurity in rural Ghana
Bibliographic record
Abstract
Interventions that enhance women's incomes and nutrition knowledge are likely to improve household food security through increased economic contribution to food expenditures and better nutrition practices. From May 2006 to September 2007, the ENAM project provided nutrition education and enterprise development services for 180 caregivers of young children in three ecological zones of Ghana, with the goal of improving household food security and child nutrition. A control sample of 287 households were also recruited. At baseline, almost three‐quarters of households expressed concern about their inability to meet their food needs and about half of adults and children were reported to consume less food than desired because of poverty. Locale, household size and type, and income were associated independently with food security in the past month (p<0.05). The logistic regression model demonstrated that, compared to control households, there was about a 50% decrease in the risk of food insecurity among intervention households at the final intervention time point (p<0.05). An integrated intervention that addresses barriers to availability, accessibility, and utilization of ASF can reduce household food insecurity. This is a collaborative research effort of ENAM researchers with support through the 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".