Participation of women in the health workforce in the fragile and conflict-affected countries: a scoping review
Notice bibliographique
Résumé
INTRODUCTION AND BACKGROUND: The full participation of women as healthcare providers is recognized globally as critical to favorable outcomes at all levels, including the healthcare system, to achieving universal health coverage and sustainable development goals (SDGs) by 2030. However, systemic challenges, gender biases, and inequities exist for women in the global healthcare workforce. Fragile and conflict-affected states/countries (FCASs) experience additional pressures that require specific attention to overcome challenges and disparities for sustainable development. FCASs account for 42% of global deaths due to communicable, maternal, perinatal, and nutritional conditions, requiring an appropriate health workforce. Consequently, there is a need to understand the impact of gender on workforce participation, particularly women in FCASs. METHODS: This scoping review examined the extent and nature of existing literature, as well as identified factors affecting women's participation in the health workforce in FCASs. Following Arksey and O'Malley's scoping review methodology framework, a systematic search was conducted of published literature in five health sciences databases and grey literature. Two reviewers independently screened the title and abstract, followed by a full-text review for shortlisted sources against set criteria. RESULTS: Of 4284, 34 sources were reviewed for full text, including 18 primary studies, five review papers, and 11 grey literature sources. In most FCASs, women predominate in the health workforce, concentrated in nursing and midwifery professions; medicine, and the decision-making and leadership positions, however, are occupied by men. The review identified several constraints for women, related to professional hierarchies, gendered socio-cultural norms, and security conditions. Several sources highlight the post-conflict period as a window of opportunity to break down gender biases and stereotypes, while others highlight drawbacks, including influences by consultants, donors, and non-governmental organizations. Consultants and donors focus narrowly on programs and interventions solely serving women's reproductive health rather than taking a comprehensive approach to gender mainstreaming in planning human resources during the healthcare system's restructuring. CONCLUSION: The review identified multiple challenges and constraints facing efforts to create gender equity in the health workforce of FCASs. However, without equal participation of women in the health workforce, it will be difficult for FCASs to make progress towards achieving the SDG on gender equality.
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 | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».