Analysis of Spatial Distribution of Health Care Facilities and its Effects on Access to Primary Healthcare in Rural Communities in Kpandai District, Ghana
Bibliographic record
Abstract
Despite collaborative efforts by the government and the private sector in the provision of health facilities in Ghana, a substantial proportion of communities in rural Ghana still have poor access to primary health care. Employing a case study research design, this study presents an overview of the geographic distribution of healthcare facilities in Kpandai District and access to primary health care. Focus group discussions and interviews were conducted with randomly selected households, opinion leaders, healthcare and in-charges of health facilities to ascertain their perception of accessibility to health services. Geographic Information System (GIS) (ArcMap) was used to model the spatial distribution of health facilities. Spatially, 139 communities representing 50.5% of the communities are not accessible health facilities per World Health Organization and Ghana Health Service Survive distance threshold to health centers and Community-based Health Planning and Services (CHPS), and hospitals, respectively. Ideally, this implies that the majority of the population have to travel for more than 5 km to access health care services. This study found that the poor spatial distribution of health facilities has negative implications on access to primary health care in the district. Poor conditions of roads were a major barrier to the household’s accessibility to district hospitals. In addition to this, the availability, affordability, adequacy and acceptability which are major determinants of access to primary health care delivery were found to be fairly good. These findings have implications for the realization of the United Nations’ health-related Sustainable Development Goals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".