Barriers and Facilitators to Accessing Health Care Services among Married Women in Ethiopia: a Multi-level Analysis of the Ethiopia Demographic and Health Survey
Bibliographic record
Abstract
Background and Objective: Access to health care services is a major challenge to women and children in many developing countries such as Ethiopia. In this study, we investigated the individual- and community-level factors associated with barriers to accessing health care services among married women in Ethiopia. Methods: Data from the 2016 Ethiopia demographic and health survey on 9,824 married women of reproductive age (15-49 years) were analyzed. Multilevel logistic regression models were used to assess individual- and community-level factors associated with barriers to access health care services. Regression analysis results revealed adjusted odds ratios at 95% confidence intervals. Results: Over two-thirds (71.8%) of married women in Ethiopia reported barriers to accessing health care services. Some of the individual-level factors that were associated with lower odds of reporting barriers to access health care services include: having secondary education (aOR=0.49, 95% CI: 0.32-0.77), being in the richest quintile (aOR=0.34, 95% CI: 0.22-0.54), and indicating wife-beating as unjustified (aOR=0.66, 95% CI:0.55-0.81). Among the community-level factors, high community-level literacy (aOR=0.56, 95% CI: 0.34-0.92) and moderate community socioeconomic status (aOR=0.62, 95% CI: 0.45-0.85) were significantly associated with lower odds of reporting barriers to access health care services. Conclusion and Implications for Translation: The findings revealed high barriers to access health care services, and both individual- and community-level factors were significant contributing predictors. Therefore, it is important to consider multidimensional strategies and interventions to facilitate access to health care services in Ethiopia. Copyright © Zegeye et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".