Baseline survey of sexually transmitted infections in a cohort of female bar workers in Mbeya Region, Tanzania
Bibliographic record
Abstract
OBJECTIVES: To determine baseline prevalence of sexually transmitted infections (STI) and other reproductive tract infections (RTI) and their association with HIV as well as sociodemographic and behavioural characteristics in a newly recruited cohort of female bar workers in Mbeya Region, Tanzania. METHODS: 600 female bar workers were recruited from 17 different communities during September to November 2000 and underwent gynaecological examination, laboratory testing for HIV/STI, and interviews using structured questionnaires. RESULTS: HIV-1 seroprevalence was 68%. Prevalences of STI/RTI were high titre syphilis (TPPA/RPR >/=1/8), 9%; herpes simplex virus 2 antibodies, 87%; chlamydia, 12%; gonorrhoea, 22%; trichomoniasis, 24%; and bacterial vaginosis, 40%. HIV infection was associated with TPPA and HSV-2 seropositivity, bacterial vaginosis and clinically diagnosed genital ulcers, blisters, and warts. Reported high risk sexual behaviour during the past year (having multiple casual partners) was associated with prevalent STI. CONCLUSION: Female bar workers in Mbeya are at high risk of STI and HIV infection. Targeted STI/HIV prevention interventions for these women and their sexual partners need to be reinforced. Methods should be sought to improve healthcare seeking and to provide easily accessible and affordable STI care services.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".