Socio-economic and demographic determinants of female genital mutilation in sub-Saharan Africa: analysis of data from demographic and health surveys
Notice bibliographique
Résumé
BACKGROUND: Owing to the severe repercussions associated with female genital mutilation (FGM) and its illicit status in many countries, the WHO, human rights organisations and governments of most sub-Saharan African countries have garnered concerted efforts to end the practice. This study examined the socioeconomic and demographic factors associated with FGM among women and their daughters in sub-Saharan Africa (SSA). METHODS: We used pooled data from current Demographic and Health Surveys (DHS) conducted between January 1, 2010 and December 31, 2018 in 12 countries in SSA. In this study, two different samples were considered. The first sample was made up of women aged 15-49 who responded to questions on whether they had undergone FGM. The second sample was made up of women aged 15-49 who had at least one daughter and responded to questions on whether their daughter(s) had undergone FGM. Both bivariate and multivariable analyses were performed using STATA version 13.0. RESULTS: The results showed that FGM among women and their daughters are significantly associated with household wealth index, with women in the richest wealth quintile (AOR, 0.51 CI 0.48-0.55) and their daughters (AOR, 0.64 CI 0.59-0.70) less likely to undergo FGM compared to those in the poorest wealth quintile. Across education, the odds of women and their daughters undergoing FGM decreased with increasing level of education as women with higher level of education had the lowest propensity of undergoing FGM (AOR, 0.62 CI 0.57-0.68) as well as their daughters (AOR, 0.32 CI 0.24-0.38). FGM among women and their daughters increased with age, with women aged 45-49 (AOR = 1.85, CI 1.73-1.99) and their daughters (AOR = 12.61, CI 10.86-14.64) more likely to undergo FGM. Whiles women in rural areas were less likely to undergo FGM (AOR = 0.81, CI 0.78-0.84), their daughters were more likely to undergo FGM (AOR = 1.09, CI 1.03-1.15). Married women (AOR = 1.67, CI 1.59-1.75) and their daughters (AOR = 8.24, CI 6.88-9.87) had the highest odds of undergoing FGM. CONCLUSION: Based on the findings, there is the need to implement multifaceted interventions such as advocacy and educational strategies like focus group discussions, peer teaching, mentor-mentee programmes at both national and community levels in countries in SSA where FGM is practiced. Other legislative instruments, women capacity-building (e.g., entrepreneurial training), media advocacy and community dialogue could help address the challenges associated with FGM. Future studies could consider the determinants of intention to discontinue or continue the practice using more accurate measures in countries identified with low to high FGM prevalence.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».