Spatiotemporal Trends in Human Immunodeficiency Virus Epidemics in Nigeria: A Systematic Review Protocol
Bibliographic record
Abstract
The scourge of the Human Immunodeficiency Virus infection is not abating fast as expected despite the many strategies put in place by various stakeholders. Geographical variations in its distribution have been implicated in its slow decline in sub-Saharan Africa, and Nigeria carries the highest burden in West Africa with subnational variations in 2019. This may negatively impact the implementation of interventions if not properly targeted. Geospatial technique is very important for spatial identification of hotspots and clusters which makes it easy to implement strategies, allocate scares resources, interventions and programs to achieve a success in the fight against the epidemics. It is on this note that the researcher is set to conduct this systematic review to assess trends in human immunodeficiency virus epidemics in Nigeria. The protocol will follow the preferred reporting items for systematic review and meta-analysis (PRISMA-P) 2020 guideline. The protocol was registered with the International Prospective Register for Systematic Reviews (PROSPERO) with registration number CRD42023488081. Articles would be retrieved electronically through database such as PubMed, Medline, Scopus, web of Science citation index, Google Scholar, and the open grey. Search period would be December 15th 2023 to June 15th, 2024 for related published articles. Two researchers will independently review articles for inclusion and if there are any disagreements, a third researcher would be called. Risk of bias would be assessed using New Castle Ottawa Scale and JBI critical appraisal checklist. Furthermore, statistical analysis will be conducted using the Review Manager Version 5.4. In conclusion, the Identification of clusters/hotspots helps the targeted allocation of scares resources, implementation of interventions and programs more efficiently and effectively. This strategy combined with the already existing ones, is a sure way to win the fight against the HIV epidemics.
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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 | 0.086 | 0.081 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.014 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.078 | 0.010 |
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".