Social capital and sexual behavior among Ugandan university students
Bibliographic record
Abstract
BACKGROUND: Uganda has reduced its prevalence of HIV/AIDS from 18 to 6.5% within a decade. An important factor behind this might have been the response from faith-based voluntary organizations, which developed social capital for achieving this. Three behaviors have been targeted: Abstinence, Being faithful, and Condom use (the ABC strategy). The aim of this study was to explore the association between social capital and the ABC behaviors, especially with reference to religious factors. METHODS: In 2005, 980 Ugandan university students responded to a self-administered questionnaire (response rate 80%). It assessed sociodemographic factors, social capital, importance of religion, sexual debut, number of lifetime sexual partners, and condom use. Logistic regression analysis was applied as the main analytical tool. RESULTS: Thirty-seven percent of the male and 49% of the female students had not had sexual intercourse. Of those with sexual experience, 46% of the males and 23% of the females had had three or more lifetime sexual partners, and 32% of those males and 38% of the females stated they did not always use condoms with a new partner. Low trust in others was associated with a higher risk for not always using condoms with a new partner among male students (OR 1.7, 95% CI 1.1-2.8), and with a lower risk for sexual debut among female students (OR 0.5, 95% CI 0.3-0.9). Non-dominant bridging trust among male students was associated with a higher risk for having had many sexual partners (OR1.8, 95% CI 1.2-2.9). However, low trust in others was associated with a greater likelihood of sexual debut in men, while the opposite was true in women, and a similar pattern was also seen regarding a high number of lifetime sexual partners in individuals who were raised in families where religion played a major role. CONCLUSIONS: In general, social capital was associated with less risky sexual behavior in our sample. However, gender and role of religion modified the effect so that we can not assume that risky sexual behavior is automatically reduced by increasing social capital in a highly religious society. The findings indicate the importance of understanding the interplay between social capital, religious influence, and gender issues in HIV/AIDS preventive strategies in Uganda.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".