Relevance of the Health Extension Program to the current Health Needs and Evolving Demands of Rural Ethiopia: A Mixed-Method Analysis
Bibliographic record
Abstract
Background: The unmet need for family planning (FP) is a major impediment to achieving the sustainable development goal The COVID-19 pandemic and other contextual, individual, and hospital-related problems are major barriers that reduce FP service uptake. However, most of the studies are quantitative and give due focus to individual and community-level barriers. Therefore, this study tends to explore barriers to the utilization of FP in Ethiopia including health care and contextual barriers. Methods: A multiple explorative case study design was employed from October to December 2021 and a total of 41 Key-informant interviews, 32 in-depth interviews, and 13 focus group discussions were performed by using the purposive sampling technique. The data were analyzed with a thematic content analysis approach using NVivo software. Result: This study explored barriers to FP in four major teams; individual, community-related, health system, and contextual barriers. It reviled that the community's misconception, fear of side effects, lack of women's decision-making autonomy, existing socio-cultural norms, religious conditions, topography, covid 19 pandemic, and conflict were the major barriers to FP service utilization. Conclusion: Using the four teams mentioned above, this study identified different poor health professional skills, misconceptions, pandemics, functional, and structurally related barriers. As a result, it is recommended that health education for the community and training for health professionals are important. Collaboration between government and non-government organizations is also mandatory for strengthening mentorship and supervision systems and establishing resilient health care that can avoid future pandemics.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".