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Enregistrement W2082574586 · doi:10.1363/3718111

Do Men and Women Report Their Sexual Partnerships Differently? Evidence from Kisumu, Kenya

2011· article· en· W2082574586 sur OpenAlexaff
Shelley Clark, Caroline W. Kabiru, Eliya M. Zulu

Notice bibliographique

RevueInternational Perspectives on Sexual and Reproductive Health · 2011
Typearticle
Langueen
DomaineHealth Professions
ThématiqueAdolescent Sexual and Reproductive Health
Établissements canadiensMcGill University
Organismes subventionnairesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Institutes of Health
Mots-clésCasualDemographyCondomReproductive healthPsychologySelection biasSexual behaviorGeneral partnershipSample (material)Marital statusMedicineClinical psychologyPopulationFamily medicineHuman immunodeficiency virus (HIV)Political scienceSociology

Résumé

récupéré en direct d'OpenAlex

At least two other explanations for such gender differences are plausible.First, differences may stem from sample selection bias, which may occur if, on average, men have more sexual partners outside the study area than do women, have partners younger than the minimum age of sample respondents (typically age 15 or 18) or have partners, such as commercial sex workers, who are not likely to be interviewed in standard surveys.These types of bias would tend to inflate aggregate gender differences in both the average number of partners and other reported sexual behaviors, such as condom use, frequency of sex and relationship duration.To limit the effects of sample selection bias, one study in Tanzania attempted to interview all men and women of reproductive age in four villages and restricted their sample of partners to those who resided in these villages and were the same age as the respondents (aged 15-64).5 Unfortunately, since only 84% of eligible women and 77% of eligible men participated in the survey, the investigators could not entirely eliminate the potential for sample selection effects.Nonetheless, they found that, on average, women reported significantly fewer nonmarital sexual partners than men, as well as longer sexual relationships, leading them to conclude that in general men "swagger" (i.e., exaggerate their sexual activities), while women are Do Men and Women Report Their Sexual Partnerships Differently? Evidence from Kisumu, KenyaCONTEXT: It is generally believed that men and women misreport their sexual behaviors, which undermines the ability of researchers, program designers and health care providers to assess whether these behaviors compromise individuals' sexual and reproductive health. METHODS:Data on 1,299 recent sexual partnerships were collected in a 2007 survey of 1,275 men and women aged 18-24 and living in Kisumu, Kenya.Chi-square and t tests were used to examine how sample selection bias and selective partnership reporting may result in gender differences in reported sexual behaviors.Correlation coefficients and kappa statistics were calculated in further analysis of a sample of 280 matched marital and nonmarital couples to assess agreement on reported behaviors.RESULTS: Even after adjustment for sample selection bias, men reported twice as many partnerships as women (0.5 vs. 0.2), as well as more casual partnerships.However, when selective reporting was controlled for, aggregate gender differences in sexual behaviors almost entirely disappeared.In the matched-couples sample, men and women exhibited moderate to substantial levels of agreement for most relationship characteristics and behaviors, including type of relationship, frequency of sex and condom use.Finally, men and women tended to agree about whether men had other nonmarital partners, but disagreed about women's nonmarital partners. CONCLUSIONS: Both sample selection bias and selective partnership reporting can influence the level of agreement between men's and women's reports of sexual behaviors. Although men report more casual partners than do women, accounts of sexual behavior within reported relationships are generally reliable.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,013
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,115
Score d'incertitude au seuil0,229

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,013
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,003
Études des sciences et des technologies0,0020,002
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,000

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.

Tête enseignante Opus0,285
Tête enseignante GPT0,441
Écart entre enseignants0,156 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations33
Publié2011
Routes d'admission1
Résumé présentoui

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