Factors associated with socio‐demographic characteristics and antenatal care and iron supplement use in Ethiopia, Kenya, and Senegal
Bibliographic record
Abstract
Antenatal care (ANC) offers remarkable opportunities to reach a large number of women with effective nutrition and health interventions, including iron (Fe) supplementation. However, all women do not equally seek nor benefit from ANC. We aimed to identify characteristics associated with ANC and Fe use among women in hard-to-reach areas in Afar, Ethiopia; Sedhiou and Kolda, Senegal; and Kakamega, Kenya. Women who gave birth within 1 year preceding the survey (n = 4,575) from 15 different sub-regions were randomly selected and surveyed. Multivariable logistic regression was used to identify associations of socio-demographic characteristics with ANC and Fe use. Factors that showed positive associations with ANC uptake included education, income, possession of a mobile phone, and the occupation of the mother or another household member. Beginning ANC in the first trimester associated positively with achievement of 4 or more ANC visits, and having any ANC visits related positively with Fe intake. Distance to the nearest health facility was negatively associated, and type of nearest facility and counselling and health education were positively associated with some outcomes. The results from these surveys demonstrate the need to ensure access of services across all population groups and can help identify ANC programming needs.
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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 | 0.000 | 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".