“If we have water, we have money”: A qualitative investigation of the role of water in women’s economic engagement in Guatemala, Honduras, Kenya, and Zimbabwe
Notice bibliographique
Résumé
Abstract Background Water is essential for life and development, and access to sufficient, safe, acceptable, physically accessible, and affordable water is a basic human right. Access to water can contribute to poverty reduction, as it can enhance agriculture and livestock production and enable engagement in other water-related economic activities. Globally, 2.2 billion people lack access to safe drinking water. Women are disproportionately affected by water scarcity, as they bear the greatest burden of water related challenges, particularly in low- and middle-income countries. Water collection can deplete women’s time and energy and jeopardize their health and wellbeing, limiting their ability to participate in economic activities. Therefore, we aimed to assess the role of water on women’s economic engagement in Guatemala, Honduras, Kenya, and Zimbabwe. Method We conducted 72 focus group discussions with women (n=38; participants=298) and men (n=34; participants=202), and 56 key informant interviews (women=33; men=23) from June to October 2023 in 25 rural communities. Qualitative tools included questions about barriers, facilitators and community perceptions of women’s economic engagement, and women’s water collection experiences. We used a modified grounded theory approach for data analysis, developed deductive and inductive codes, and used MAXQDA2020 to code and organize data. Results Women’s time was compromised by limited access to sufficient quantities of water, limiting their engagement in economic opportunities. Limited water reduced livelihood activities agriculture and livestock production that women engaged in, and constrained women’s economic resources for example payment for water or for costs associated with water access, needed for income-generating activities. Health and wellbeing, specifically, physical health issues like fatigue, exhaustion, physical injuries, and pain, from water collection work, left women depleted of energy needed for economic engagement activities. Gender norms shaped roles and responsibilities for men and women and ascribed water collection as the primary responsibility of women in all four countries. Environmental factors such as drought and seasonality diminished sufficient access to water, further reducing women’s time and energy, and negatively impacted livestock and agricultural productivity due to diminished pasture and reduced water supply. Conclusion Water is a prerequisite to economic engagement, especially in hard-to-reach low-resource settings. Access to water can enable livelihood opportunities, allow women to save or reallocate time, enable various economic resource options, and improve health and wellbeing, which can then facilitate women’s economic engagement. Insufficient access to water can demand arduous water collection tasks, which can impact health by causing energy depletion, increasing risk of injury, and causing mental strain. These negative health impacts, coupled with time and opportunity costs, can constrain women’s abilities to engage in other facets of life, including economic engagement which could provide well-being benefits to women and their families. Further research is needed to understand how health and wellbeing can influence economic engagement, as many studies focus on health outcomes rather than the reverse.
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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 | 0,012 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,016 | 0,014 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».