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Enregistrement W4399385036 · doi:10.1111/birt.12828

“I have to listen to them or they might harm me” and other narratives of why women endure obstetric violence in Bihar, India

2024· article· en· W4399385036 sur OpenAlexaff
Kaveri Mayra, Zoë Matthews, Jane Sandall, Sabu S. Padmadas

Notice bibliographique

RevueBirth · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueMaternal and Perinatal Health Interventions
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesBurdett Trust for NursingParkes Foundation
Mots-clésNarrativeChildbirthGender studiesQualitative researchHealth careHarmNursingHarassmentMedicinePsychologySociologySocial psychologyPolitical sciencePregnancySocial science

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Evidence suggests that obstetric violence has been prevalent globally and is finally getting some attention through research. This human rights violation takes several forms and is best understood through the narratives of embodied experiences of disrespect and abuse from women and other people who give birth, which is of utmost importance to make efforts in implementing respectful maternity care for a positive birthing experience. This study focused on the drivers of obstetric violence during labor and birth in Bihar, India. METHODS: Participatory qualitative visual arts-based method of data collection-body mapping-assisted interviews (adapted as birth mapping)-was conducted to understand women's perception of why they are denied respectful maternity care and what makes them vulnerable to obstetric violence during labor and childbirth. This study is embedded in feminist and critical theories that ensure women's narratives are at the center, which was further ensured by the feminist relational discourse analysis. Eight women participated from urban slums and rural villages in Bihar, for 2-4 interactions each, within a week. The data included transcripts, audio files, body maps, birthing stories, and body key, which were analyzed with the help of NVivo 12. FINDINGS: Women's narratives suggested drivers that determine how they will be treated during labor and birth, or any form of sexual, reproductive, and maternal healthcare seeking presented through the four themes: (1) "I am admitted under your care, so, I will have to do what you say"-Influence of power on care during childbirth; (2) "I was blindfolded … because there were men"-Influence of gender on care during childbirth; (3) "The more money we give the more convenience we get"-Influence of structure on care during childbirth; and (4) "How could I ask him, how it will come out?"-Influence of culture on care during childbirth. How women will be treated in the society and in the obstetric environment is determined by their identity at the intersections of age, class, caste, marital status, religion, education, and many other sociodemographic factors. The issues related to each of these are intertwined and cross-cutting, which made it difficult to draw clear categorizations because the four themes influenced and overlapped with each other. Son preference, for example, is a gender-based issue that is part of certain cultures in a patriarchal structure as a result of power-based imbalance, which makes the women vulnerable to disrespect and abuse when their baby is assigned female at birth. DISCUSSION: Sensitive unique feminist methods are important to explore and understand women's embodied experiences of trauma and are essential to understand their perspectives of what drives obstetric violence during childbirth. Sensitive methods of research are crucial for the health systems to learn from and embed women's wants, to address this structural challenge with urgency, and to ensure a positive experience of care.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,669
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

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

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 Opus0,046
Tête enseignante GPT0,341
Écart entre enseignants0,295 · 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 tête enseignante, pas un consensus.

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

Citations4
Publié2024
Routes d'admission1
Résumé présentoui

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