Prevalence, risk behaviours, and HIV knowledge in an Indigenous community in Colombia
Bibliographic record
Abstract
There are 87 Indigenous ethnic groups in Colombia, representing 3.4% of the country’s population. Poverty, forced displacement, and social and health inequities place Indigenous communities at increased risk of HIV/AIDS. However, little is known about the prevalence of HIV in this population. The objectives of this study were to estimate the prevalence of HIV and other sexually transmitted infections in an Indigenous community in Colombia, and to assess community members’ knowledge about the disease and its risk factors. The study, conducted in 2010, was initiated at the request of the leadership of the community of Cristianía and involved community members in all stages of the project. HIV prevalence data were gathered through rapid testing of a random sample of 295 community members between the ages of 15 and 49 years. As well, researchers administered a survey related to sexual behaviours and knowledge about HIV. Findings revealed 3 cases of HIV, a prevalence of 1.02%, 95% CI [0.21, 2.94]. The 3 cases were women. The majority of individuals sampled had heard of HIV or AIDS, but their level of knowledge about the mechanisms of virus transmission varied substantially. The results of this study, the first to explore the prevalence of HIV among Indigenous people within a community in Colombia, suggest a need to investigate HIV prevalence within other Indigenous communities in Colombia.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".