Trends in Sexually Transmitted Infection Cases in HIV Populations in Indonesia: Need Firm Roadmaps and Actions
Bibliographic record
Abstract
Human immunodeficiency virus (HIV) infection and sexually transmitted infections (STIs) remain a public health problem and extend to social, economic, and cultural issues. HIV cases in the Southeast Asia region account for 10% of the total global HIV burden. In Indonesia, there are five provinces with the highest number of HIV cases as of December 2021, including DKI Jakarta (73,442), East Java (68,112), West Java (49,435), Central Java (42,012), and Papua (40,277). In Indonesia in 2021, the number of people living with HIV (ODHIV) reported was 36,902 and in 2022 up to the third quarter (July - September 2022) there were 34,213 people. New HIV infections in Indonesia continue to decline, in line with the global decline in new HIV infections. However, this decline has not been as large as expected.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".