Concepts and strategies for scaling up focused prevention for sex workers in India
Bibliographic record
Abstract
OBJECTIVES: To describe the concepts, strategies and field results of a project to scale up prevention programmes and services for female sex workers (FSWs) in Karnataka, India. METHODS: A strategy was developed to scale up urban sex worker interventions in 18 districts in the southern Indian state of Karnataka. Macro-level coverage objectives were defined by mapping the urban locations where FSWs operate and estimating their population size. Prevention programmes were initiated in the urban locations that contained at least 90% of the estimated urban FSW population in each district. Within each location, a micro-planning process was used by FSW peer educators and outreach workers to design local outreach and service delivery plans. RESULTS: An estimated 48 973 FSWs were distributed across 1551 locations and 6232 spots. Outreach was conducted by 1043 peer educators. Services were provided through 170 drop-in centres, 93 programme-run clinics, 110 outreach clinics and 157 referral clinics. Within the first 3 years of the programme the cumulative number of individual FSWs contacted at least once was >78 000, with monthly contact established with 81% of the in situ population; >45 000 FSWs had visited a clinic and >10 000 visited monthly. Direct and indirect condom distribution by the programme amounted to more than 30 per contacted FSW, which is estimated to meet the condom requirement. CONCLUSIONS: A strategy that involves geographically defined coverage and micro-level outreach planning can rapidly and effectively provide outreach and services to large dispersed FSW populations.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".