Determinant factors correlated with discriminatory attitude towards people living with HIV in Indonesian population: demographic and health survey analysis
Bibliographic record
Abstract
Introduction: Discrimination of people living with HIV (PLHIV) and its’ various forms is a concern�ing issue. The purpose of this study was to examine HIV stigma and discriminatory behaviors among \nIndonesian women and men aged 15 to 49 years. \nMaterial and methods: This was a cross-sectional study that analyzed data from the 2017 Indonesia \nDemographic and Health Survey, focusing on the discriminatory attitude. A stratified two-stage cluster \ndesign was utilized. Data included demographics (age, education, wealth index, and place of residence), \nknowledge of HIV, and discriminatory attitude towards PLHIV. Logistic multiple regression modelling \nexamined the determinant factors towards PLHIV. A total of 49,204 women aged 15 to 49 years, and \nmales aged 15 to 54 years were included in this survey. \nResults: According to the findings, 85.1 percent of women and 85.7 percent of men presented discrimi�natory attitudes towards PLHIV. Older age (p = 0.00; 95% CI: 1.13-1.50%), richest group (p = 0.00; \n95% CI: 0.69-0.93%), inhabiting rural area (p = 0.00; 95% CI: 1.11-1.33%), and comprehensive knowl�edge about HIV (p = 0.00; 95% CI: 0.45-0.53%) were the determinant factors of discriminatory atti�tudes towards PLHIV in women, while secondary education (p = 0.04; 95% CI: 1.00-5.09%) and richer \ngroup (p = 0.003; 95% CI: 0.483-0.859%) were the determinant factors in men. \nConclusions: A large proportion of men and women in Indonesia continue to engage in discriminatory \nattitude towards PLHIV. Stigma and prejudice must be tackled in order to battle the HIV/AIDS pandemic \nin Indonesia. People should be informed about behavior modification strategies to prevent the disease \nfrom spreading.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".