Determinants of chronic energy deficiency among non-pregnant and non-lactating women of reproductive age in rural Kebeles of Dera District, North West Ethiopia, 2019: Unmatched case control study
Bibliographic record
Abstract
BACKGROUND: In Ethiopia about 25% of rural women are chronically malnourished. Non-pregnant and non-lactating women present an opportunity to implement strategies to correct maternal and child health status and to potentiate improved pregnancy outcomes in developing countries like Ethiopia. The determinant factors of chronic energy deficiency vary across settings and contexts; hence, it is important to identify local determinant factors in order to implement effective and efficient intervention strategies. OBJECTIVE: To assess the determinants of chronic energy deficiency of non-pregnant, non-lactating rural women within the reproductive age group (15-49 years), in rural kebeles of Dera district, North West Ethiopia, 2019. METHODS: A community based unmatched case control study was conducted. A total of 552 participants were involved and a multi-stage sampling technique was used to select the samples. Data was collected from January 15 to February 30, 2019 using face-to-face interviews and anthropometric assessments. EPI-info version 7 and SPSS™ version 23 were used for data entry and analysis, respectively. Bivariable and multivariable logistic regression models were used to analyze the association between dependent and independent variables. Association was considered statistically significant at 95% CI with p-value < 0.05 in multivariable logistic regression. RESULT: A total of 548 non-pregnant, non-lactating women with 137 cases and 411 controls were included in the study with a response rate of 99.3%. High family size (AOR = 1.88, 95% CI: 1.085, 3.275), low educational status (AOR = 3.389, 95% CI: 1.075, 10.683), inadequate meal frequency (AOR = 5.345, 95% CI: 2.266, 12.608), absence of home garden (AOR = 5.612, 95% CI: 3.177, 9.915) and absence of latrine facility (AOR = 6.365, 95% CI: 3.534, 11.462) were found positively associated with chronic energy deficiency. CONCLUSION AND RECOMMENDATION: Inadequate meal frequency, absence of home gardening, absence of latrine facility, high family size and educational status of illiterate were the determinants of chronic energy deficiency, thus indicating the imperative for a multi-sectoral approach with health, agriculture and education entities developing and delivering interventions.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".