HIV, TB and Malaria Service Readiness at the Primary Healthcare Centers (PHCs) in Ekiti State, Nigeria
Bibliographic record
Abstract
Introduction: access to services encompasses three components: availability, affordability, and acceptability. The physical presence of service delivery, which includes health infrastructure, core health staff, and aspects of service use, is referred to as service availability. This study was conducted to inform the health service availability and preparedness to deliver HIV, TB, and malaria prevention and control services in Ekiti State. Methods: this is a descriptive cross-sectional study conducted among all the Primary Health Centres (177) in Ekiti State Nigeria between August and October 2020. Data were collected with the use of the World Health Organization Service Availability and Readiness Assessment tool and were analyzed using STATA SE 12. Results: close to half (49%) of them had a condom in supply. More than 90% of them provided diagnosis and treatment of malaria. The HIV-specific service readiness index was approximately 40/0%. Only 26.6% of health facilities were ready to offer TB prevention and control services. Malaria specific service readiness index was 61.9%. There was a statistically significant difference in the HIV and TB-specific service readiness of facilities in the urban compared to rural locations. Health facilities located in the urban areas had higher mean readiness scores compared to those in the other residential areas (P=0.014). Conclusion: it is evident that HIV and TB-specific service readiness is very poor among PHCs in Ekiti State. Malaria Service Readiness was fair. Ekiti State government needs to expand investments in PHCs by strengthening the diagnostic services, commodities and medicine supply, adequate equipment and staff training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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 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".