Tools for measuring gender equality and women’s empowerment (GEWE) indicators in humanitarian settings
Notice bibliographique
Résumé
BACKGROUND: Effective measurement of Gender Equality and Women's Empowerment (GEWE) is challenging in low and middle-income countries (LMICs), and even more so in humanitarian settings. Conflict, natural disasters, and epidemics may increase gender inequities, but also present an opportunity to address them. This scoping review describes and identifies gaps in the measurement tools, methods, and indicators used to measure GEWE in humanitarian settings, and presents a dashboard that can be used by researchers, organizations and governments to identify GEWE measurement tools. METHODS: Scientific articles published between January 2004 and November 2019 were identified using Embase, Medline, PsycInfo, CINAHL, Scopus, and PAIS index. Relevant non peer-reviewed literature was downloaded from the websites of humanitarian organizations. Publications on women and/or girls impacted by a humanitarian crisis in a LMIC, within 5 years of data collection, were included. Publications were double-screened in the title/abstract and full-text stages. We used a machine learning software during the title/abstract screening to increase the efficiency of the process. Measurement tools, sampling and data collection methods, gap areas (geographical, topical and contextual), and indicators were catalogued for easy access in an interactive Tableau dashboard. RESULTS: Our search yielded 27,197 publications and 2396 non peer-reviewed literature reports. One hundred and seventy publications were included in the final review. Extracted indicators were categorized into seven domains: economic, health, human development, leadership, psychological, security and justice, and sociocultural. The vast majority of studies were observational, and over 70% utilized a cross-sectional study design. Thirty-eight toolkits and questionnaires were identified in this review, of which 19 (50%) were designed specifically for humanitarian settings. Sociocultural was the largest domain in number of studies and indicators in this review, with gender-based violence indicators reported in 66% of studies. Indicators of economic, human development and leadership were uncommon in the peer-reviewed literature. DISCUSSION: While there has been some effort to measure GEWE in conflict-affected and other humanitarian settings, measurement has largely focused on violence and security issues. A more comprehensive framework for measuring GEWE in these settings is needed; objective measurement of women's empowerment and gender equality should be prioritized by organizations providing humanitarian aid.
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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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 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 | 0,000 |
| 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 ».