Assessing the Impact of Pre-Third-Party Logistics (PRE-3PLS) and 3PLS on Viral Load Sample Turn-around Time in the Management of HIV/AIDS Patients in Ekiti State, Nigeria: A Retrospective Cross-Sectional Study
Bibliographic record
Abstract
Background: In 2014, The Joint United Nations Program on HIV/AIDS and its partners launched the 90–90–90 treatment targets, which aimed to diagnose 90% of all people living with Human Immunodeficiency Virus (PLHIV), 90% of PLHIV should be on antiretroviral therapy (ART) and 90% of those on ART should achieve viral suppression by 2020. Prompt HIV viral load testing and reporting is crucial to achieving the third 90% component of the target. This study aims to assess impact of Pre-Third-Party Logistics (Pre-3PLs) and Third-Party Logistics (3PLs) on viral load sample Turn-around Time (TAT) in the management of HIV/AIDS patients in Ekiti State, Nigeria Methods: A cross-sectional study was carried out in ten health facilities. Quantitative and qualitative data were collected and analyzed using the R statistical software. Results: High load facilities had a mean TAT of 83.68 and 30.76 days for pre-3PLs and 3PLs respectively with a mean difference of 52.92 days (95% CI:18.86-86.97, p<0.05). Low load facilities had a mean TAT of 104.4 and 26.9 days for pre-3PLs and 3PLs respectively with a mean difference of 77.38 days (95% CI: 64.50-90.26, p<0.05). Other than transportation, manpower, sample quality/integrity, reagent stockout and machine downtime, proximity, and human factors were also identified as factors associated with TAT. Conclusion: The TAT reduced significantly from pre 3PLs to the present era of 3PLs. The continuous use of 3PLs should be encouraged as this could further ensure that more PLHIV will have their viral load tested with their results received on time.
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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) | 0.001 | 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".