Residential inequalities in health-related quality of life among women of reproductive age in four regions of Ethiopia: a decomposition analysis
Bibliographic record
Abstract
BACKGROUND: Ethiopian rural-urban disparities in key domains of health-related quality of life among women in reproductive age have been huge. However, sources of such inequalities were not studied well. Therefore, this study aimed to assess inequalities in health-related quality of life among women residing in urban and rural areas in four regions of Ethiopia. METHODS: This study used data extracted from the 2016 Ethiopian Demographic and Health Survey; collected at national level from January 18, 2016, to June 27, 2016. Stratified two stage cluster sampling method were used. The data collected from 2385 women in the age group 15-49 years who were living in four regions (Afar, Benishangul-Gumuz, Gambela, and Somali regions) of Ethiopia were used for this study. The outcome variable, Health-Related Quality of Life (HRQoL), was generated by Principal Component Analysis. Further, Multivariable Ordinary Least Square and Oaxaca decomposition threefold (interaction) were used in the analysis with a p-value less than 0.05 and 95% confidence interval to declare statistical significances. RESULTS: Women education, region, religion, wealth index, and husband/partner education were identified as predictors of Health-Related Quality of Life. Women residing in rural areas had far lower health-related quality of life than those living in urban areas. The wealth index and educational level of women were the largest contributor of the inequality in health-related quality of life. CONCLUSION: A substantial inequality in quality of life exist between women who reside in rural and urban areas in those four regions of Ethiopia. The socioeconomic factors more importantly wealth index and educational attainment explained the significant portion of the reported rural-urban disparities. Therefore, Policymakers and local administrators should pay more attention on interventions that promote education and narrowing gap in wealth in rural and urban settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".