Abstract P191: Health Services Availability And Readiness For Management Of Hypertension And Diabetes In Primary Level Healthcare Facilities In Ghana
Bibliographic record
Abstract
Background: Hypertension (HTN) and diabetes (DM) are the leading cause of adult morbidity and mortality in Ghana and other African countries. We explored the service availability (SA) and readiness (SR) of health systems for managing HTN and DM in the Bono Region, Ghana. Methods: We conducted a multi-center cross-sectional study of four primary health facilities between June 2022 and July 2022. We modified the World Health Organization (WHO) Service Availability and Readiness Assessment tool to focus on management of HTN and DM. We computed composite scores for SR based on domains of functional equipment, diagnostic capacity, medications, and clinical guidelines & protocols; SA based on service-specific domains and stratified by facility ownership (Mission vs Government). At a cutoff value of 70%, services were considered "available" or facility "ready" to manage HTN and DM based on previous studies. Results: The median number of HCWs was 7 (IQR 2-7). The overall average SR and SA scores were 75.5% and 63% respectively (Fig A & B). SR scores were higher at mission hospitals (84.5%) than at government hospitals. SA scores were generally lower than SR Scores for both Mission and Government facilities. Half of the health facilities had guidelines and had CVD training in the last 2 years (n = 2; 50%), and all (n=4,100%) had received supervisory visits regarding CVD care in the last three months. Conclusion: There are gaps in diagnostic capacity, basic equipment, clinical guidelines, and a limited number of physicians accounting for low scores, particularly in government facilities. Improving resource allocation and implementing a team-based care policy may enhance HTN and DM care in Ghana .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".