Predictors of multiple sexual partnerships among women and men in two urban townships in Bhutan
Notice bibliographique
Résumé
Introduction: Multiple sexual partnering is a known predictor for risk of STI and HIV transmission. This study explored the multiple sexual partnering and its predictors among people who visited public social venues (bars, restaurants, hotels, lodges, cafes, karaokes and discos) in Bhutan’s two largest townships of Thimphu and Phuntsholing. Methods: We interviewed 755 sexually active venue patrons from 102 randomly selected venues (56 in Thimphu, 46 in Phuntsholing) from a list of all venues identified as having sex workers or patrons seeking sexual partners. Both bivariate and multivariate analyses were carried out to characterize the predictors of multiple sexual partnering among 755 respondents who had previously had sex. Results: Of the 755 patrons, 46.09% had one sexual partner while the remaining 54.91% had multiple sex partners (greater than or equal to 2 sexual partners) in the 12 months preceding the study. Overall, 6.23% of respondents had received payment from someone at least once for sex; 34.61% of male respondents had paid someone at least once for sex. Nearly all patrons (97.72%) had heard about HIV/ AIDS. About one quarter (24.20%) felt that they were at risk of being infected with HIV, while 37.28% had taken an HIV test in the 12 months preceding the study. In multivariate analysis, males had higher odds of multiple sexual partners compared to females (OR =3.19, 95% CI 1.90-5.20). The odds of having multiple sexual partners was 2.24 (95% CI 1.30-3.90) times higher in those never married compared to those who were married/divorced or separated; multiple partnering increased with increasing age (OR = 1.07 per year, 95% CI 1.02-1.13). Between the townships of Phuentsholing and Thimphu, the odds of multiple sexual partnering did not vary. Conclusions: Venue patrons had a high prevalence of multiple sexual partnering and have the potential for creating sexual networks that could propagate wider transmission of infection, including to their monogamous partner. Targeting HIV prevention program to these groups of people in urban locations presents an opportunity to make a great impact in maintaining Bhutan’s current low HIV epidemic level.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».