Prevalence of HIV/Sexually transmitted infections among street based female sex workers in Nairobi
Bibliographic record
Abstract
Background: \nInterventions that control the prevalence of STDs among sex workers are central 111 \nHIV prevention programs worldwide. The interplay between classical STDs and HIV \nis further compounded by FSW's clients who act as a bridging group to the rest of the \npopulation, other socio-demographic factors and risk behaviors amongst FSW's \ncontributing to the unique pattern of the HIV/STI in different regions. \nMethods: \nA cross-sectional descriptive study was conducted among FSW's accessing care at \nSWOP clinic in Nairobi, Kenya over the period of I st September 2008 to 31st August \n2009 using the existing University of Manitoba/Nairobi research program database. \nDescriptive statistics on the prevalence of STls, univariate and multivariate logistic \nregression analysis were then used to determine significant correlates of HIV and \nSTI's among these FSWs \nResults: \nA total of 3011 FSWs were evaluated. Their median age was 31 years. Majority of the \nFSWs work within Nairobi environs. 53% of the FSWs had either been divorced or \nseparated and was also the group that had high STl/HIV infection rates. The FSWs \nwho had children had at least I child to support, with only menial jobs as a source of \nincome to provide for themselves and dependants. \nMajority of the FSW's had only attained upto primary level of education. Knowledge \non how to use male condoms was 29.7%. \nThere were varying differences in the rates of STI s amongst the FSWs with HIV \ninfection and those without HIV infection. Substance abuse (alcohol) came out as a \nsignificant covariable in influencing STI/HIV acquisition use, to their knowledge and attitudes on HIV/STI, risk taking behaviours and stigma still hindering health seeking behaviours of this vulnerable group. Further studies on this most at risk population may be useful in guiding on other likely biological factors that may be influencing this variations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".