Assessing HIV Risk in Workplaces for Prioritizing HIV Preventive Interventions in Karnataka State, India
Bibliographic record
Abstract
OBJECTIVE: To develop a model for prioritizing economic sectors for HIV preventive intervention programs in the workplace. METHODS: This study was undertaken in Karnataka state, India. A 3-stage survey process was undertaken. In the first stage, we reviewed secondary data available from various government departments, identified industries in the private sector with large workforces, and mapped their geographical distribution. In the second stage, an initial rapid risk assessment of industrial sectors was undertaken, using key-informant interviews conducted in relation to a number of enterprises, and in consultation with stakeholders. In the third stage, we used both quantitative (polling booth survey) and qualitative methods (key informant interviews, in-depth interviews, focus group discussions) to study high-risk sectors in-depth, and assessed the need and feasibility of HIV workplace intervention programs. RESULTS: The highest risk sectors were found to be mining, garment/textile, sugar, construction/infrastructure, and fishing industries. Workers in all sectors had at best partial knowledge about HIV/AIDS, coupled with common misconceptions about HIV transmission. There were intersector and intrasector variations in risk and vulnerability across different geographical locations and across different categories of workers. This has implications for the design and implementation of workplace intervention programs. CONCLUSIONS: There is tremendous scope for HIV preventive interventions in workplaces in India. Given the variation in HIV risk across economic sectors and limited available resources, there will be increased pressure to prioritize intervention efforts towards high-risk sectors. This study offers a model for rapidly assessing the risk level of economic sectors for HIV intervention programs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".