The Effect of Community Health Information System on Health Care Services Utilization in Rural Ethiopia
Bibliographic record
Abstract
Background: Non-communicable diseases (NCDs) pose a substantial global health challenge, resulting in an annual death toll of over 15 million individuals aged 30 to 69. Ethiopia, categorized as COVID-19 vulnerable, grapples with NCD treatment challenges. This study aims to assess disease service availability at primary health units in Ethiopia during the pandemic. Methods: A facility-based cross-sectional study was conducted from October to December 2021 across regions, encompassing 452 facilities: 92 health centers, 16 primary hospitals, 344 health posts, and 43 districts. Facility selection, based on consultation with regional health bureaus, included high, medium, and low performing establishments. The study employed the WHO tool for COVID-19 capacity assessment and evaluated services for various diseases using descriptive analysis. Results: Results reveal service disruptions in the past year: hospitals (55.6%), health centers (21.7%), districts (30.2%), and health posts (17.4%). Main reasons were equipment shortages (42%), lack of skilled personnel (24%), and insufficient infection prevention supplies (18.8%). While tuberculosis treatment was fully available in 23% of health posts and malaria services in 65.7%, some health centers lacked HIV/AIDS, cardiovascular, mental health, and cervical cancer services. Most communicable and non-communicable disease diagnoses and treatments were fully accessible at primary hospitals, except for cervical cancer (56.3%) and mental health (62.5%) services. Conclusion: Significant gaps exist in expected services at primary health units. Improving disease care accessibility necessitates strengthening the supply chain, resource management, capacity building, and monitoring systems.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".