I am not ``umqwayito'': A qualitative study of peer pressure and sexual risk behaviour among young adolescents in Cape Town, South Africa
Bibliographic record
Abstract
BACKGROUND: Young people in South Africa are susceptible to HIV infection. They are vulnerable to peer pressure to have sex, but little is known about how peer pressure operates. AIM: The aim of the study was to understand how negative peer pressure increases high risk sexual behaviour among young adolescents in Cape Town, South Africa. METHODS: Qualitative research methods were used. Eight focus groups were conducted with young people between the ages of 13 and 14 years. RESULTS: Peer pressure among both boys and girls undermines healthy social norms and HIV prevention messages to abstain, be faithful, use a condom and delay sexual debut. CONCLUSIONS: HIV prevention projects need to engage with peer pressure with the aim of changing harmful social norms into healthy norms. Increased communication with adults about sex is one way to decrease the impact of negative peer pressure. Peer education is a further mechanism by which trained peers can role model healthy social norms and challenge a peer culture that promotes high risk sexual behaviour. Successful HIV prevention interventions need to engage with the disconnect between educational messages and social messages and to exploit the gaps between awareness, decision making, norms, intentions and actions as spaces for positive interventions.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".