Unequal coverage of nutrition and health interventions for women and children in seven countries
Bibliographic record
Abstract
OBJECTIVE: To examine inequalities and opportunity gaps in co-coverage of health and nutrition interventions in seven countries. METHODS: = 7138). We estimated co-coverage for a set of eight health and eight nutrition interventions and assessed within-country inequalities in co-coverage by wealth and geography. We examined opportunity gaps by comparing coverage of nutrition interventions with coverage of their corresponding health delivery platforms. FINDINGS: Only 15% of 231 113 mother-child pairs received all eight health interventions (weighted percentage). The percentage of mother-child pairs who received no nutrition interventions was highest in Pakistan (25%). Wealth gaps (richest versus poorest) for co-coverage of health interventions were largest for Pakistan (slope index of inequality: 62 percentage points) and Afghanistan (38 percentage points). Wealth gaps for co-coverage of nutrition interventions were highest in India (32 percentage points) and Bangladesh (20 percentage points). Coverage of nutrition interventions was lower than for associated health interventions, with opportunity gaps ranging from 4 to 54 percentage points. CONCLUSION: Co-coverage of health and nutrition interventions is far from optimal and disproportionately affects poor households in south Asia. Policy and programming efforts should pay attention to closing coverage, equity and opportunity gaps, and improving nutrition delivery through health-care and other delivery platforms.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".