WATER ACCESS, WOMEN’S EMPOWERMENT, SANITATION AND CHILDREN’S ANTHROPOMETRIC STATUS: A STUDY OF ETHIOPIAN MOTHERS WITH CHILDREN UNDER FIVE
Bibliographic record
Abstract
Recent studies have shown that most African countries are overwhelmed with problems related to water access.Women are disproportionality responsible for fetching water for drinking and domestic use.Notably, fetching water has been shown to influence various aspects of women's lives.The main objective of this paper is to study the association between the amount of time mothers spend carrying water and factors such as socioeconomic characteristics, women's empowerment (i.e., decision making, group membership, attitudes toward gender equality), income-generating activities (i.e., access to land and saving money (as proxies)), maternal nutritional status (i.e., Mid Upper Arm Circumstances (MUAC), dietary diversity score), childcare practices (i.e., breastfeeding, taking children for weighting or treatment for malnutrition, and children's dietary diversity), and children's anthropometric indicators.Data from a survey carried out in Ethiopia in 2016 was used in this article.Different statistical analyses formed the basis of the work.Firstly, descriptive statistics were used to analyse the data.Secondly, bivariate analyses were used to examine the association between exposure and outcome variables.Finally, binary logistic regression analyses were conducted.Results showed significant negative associations between the time spent fetching and saving money, group membership, attitudes toward gender equality, MUAC, dietary diversity, childcare care practices, and children's anthropometric indicators.This paper supports the important link between water access and women and children's nutritional statuses.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".