How Canadian Service Providers Understand, and Incorporate, Biomedical Knowledge of HIV into their Prevention Work and their Own Sexual Practices
Bibliographic record
Abstract
Background: HIV prevalence in Kenya dropped from 7.4% in 2007 to 5.6% in 2012.Despite this decrease, the HIV prevalence in Nyanza region remains the same, 15.1%.This high prevalence may be driven by the highly migratory ''high risk'' fishing communities residing along the shores of Lake Victoria in Nyanza, with HIV prevalence at 25.6%.We examined the association between mobility patterns and perceived sexual practices of the fisher folk.Methods: Beach management units' registers were used to identify and determine the number of fisher folk in every fish landing beach on the shores of Lake Victoria in Kenya.Probability proportionate to size sampling was used to draw a random sample of 2638 across 308 beaches for participation in a crosssectional survey.Data was collected on sociodemographics, mobility patterns and reported sexual concurrency.Descriptive statistics and logistic regressions were used for data analysis.Results: In 2013 mobility was mainly to Homabay and Siaya counties which are high HIV prevalent areas, *36% and 31% respectively.Mobility was highest between April and December, 167 (33.3%) and 119 (23.7%) in Homa Bay and Siaya respectively.Approximately 36% suspected their partners had concurrent partners and 566 (46.1%) of the respondents reported that they were currently in a concurrent relationships.Mobility was significantly associated with reported sexual concurrency (OR 1.423 95% CI 1.09-1.85p = 0.009).After controlling for age, gender, education level, income, reported condom use, age of last born child, marital status, alcohol use and duration of relationship with the most recent sexual partner, mobility was significantly associated with concurrency (AOR 1.74 95% CI 1.01-2.98p = 0.046).Conclusions: Mobility and concurrent sexual practices among fisher folk may contribute the sustained high HIV prevalence in Nyanza and calls for targeted HIV prevention and care services.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".