Mapping the Spatial Variability of Sexually Transmitted Infections Across Fiji Health Regions
Bibliographic record
Abstract
Introduction: Sexually transmitted infections (STIs) are a significant public health problem in countries within the South Pacific, including Fiji. If untreated, curable STIs such as chlamydia, gonorrhea, and syphilis can cause infertility, adverse outcomes in pregnancy, and can increase the risk of contracting HIV in infected individuals. Methods: This research used cartographic software to map and analyze the spatial distribution of selected STIs across health regions in Fiji. Total rates of STIs, as well as the prevalence of gonorrhea and syphilis specifically, were examined for the years of 2007 and 2016 to determine how spatial distribution patterns have changed over this period, and how resources might currently be most effectively mobilized to address this public health issue. Results/Discussion: Our findings suggest that while some specific regions with high prevalence rates for 2007 and 2016 should be targeted for intervention in the short term, lack of data collecting and reporting raises concerns about the accuracy of rate estimations in non-urban areas. Conclusion: Analyzing the spatial distribution of the prevalence of STIs in a given population can better inform the development and implementation of intervention strategies at local scales, thus improving health outcomes for countries and their communities. Overall, consistent and transparent STI data collection and reporting procedures are necessary for effective long-term management and minimization of STI spread in Fiji.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".