The Quality of Logistics Management Information System and the Availability of Tracer Drugs at Health Posts in Rural Ethiopia: A Mixed-Method Study
Bibliographic record
Abstract
Background: Obstetric care has been at the center of both global and national agendas. More than 50% of pregnant mothers are still preferring to give birth at home with some even after having full antenatal care. However, a few literatures looked at contributing factors for this problem but they are not conclusive and do not consider different sociocultural context of Ethiopia and different health service related barriers. Hence, the aim of this study was to explore barriers to obstetric care service utilization in Ethiopia using the socio-ecological model. Methods: Explorative qualitative study was employed involving key-informant interviews, in-depth interviews, and focus group discussions between October and December 2021; Individual, community, health system, and contextual barriers were explored. Atlas ti. Version 9 was used for analysis. Result: Lack of awareness, unfavorable perception, lack of partner involvement, cultural barrier, shortage of supplies, poor infrastructure, provider-related factors, poor monitoring, and evaluation system, challenging topography, and conflict were the major barriers that hinder mothers from receiving obstetrics service in Ethiopia. Conclusion: Lack of awareness, unfavorable perception, conflict, problems with health system structure and process, and cultural and geographical conditions were major barriers in Ethiopia. Therefore, packages of intervention is important to avail essential equipment, strengthening follow up system, create awareness, and increase access to health facilities is very important for service improvement by the government and non-governmental organizations. Additionally, implementing conflict resolution mechanism is important for addressing better obstetric service.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".