Bridging the health equity gap: examining the effects of water, sanitation and hygiene (WaSH) gender-based violence on health and wellbeing in Ghana
Bibliographic record
Abstract
Access to water, sanitation and hygiene (WaSH) is widely recognized as an important pathway to promoting human health and wellbeing. Despite the progress in access to water and sanitation in Low-and Middle-Income Countries (LMIC), there are significant inequalities and disparities across space and place. Inequalities in WaSH access heighten women’s vulnerability to violence when meeting their WaSH needs. This represents a significant barrier to achieving the Sustainable Development Goals (SDGs) by 2030. For instance, it will not be possible to achieve health and wellbeing for all (SDG 3) without safe water and sanitation for all (SDG 6), and neither is it possible without empowering women (SDG 5). However, WaSH practitioners and researchers are yet to adequately conceptualize the gendered vulnerability to violence in WaSH for informed policy. \nThis thesis draws on an integrated theoretical framework to explore the relationships between WaSH access and gendered dimensions of vulnerabilities to violence in LMICs. The research focused on three broad objectives: first, to develop an integrated theoretical framework for framing our understanding of WaSH gender-based violence in Low-and Middle-Income Countries (LMICs); second, to characterize the dimensions of WaSH gender-based violence (WaSH-GBV) in LMICs; and finally, to explore the WaSH-GBV experiences of Ghanaian immigrants in Canada over the life course. A qualitative research approach – involving conceptual review, scoping review, and in-depth interviews –was used in the research. \nThe conceptual review proposes an integrated theoretical framework for understanding intersectional vulnerabilities such as gender-based violence and water insecurity in LMICs. This framework emphasizes the role of place and scale in the conceptualization of WaSH-GBV in LMICs. It argues that WaSH-GBV is a relational outcome, and our understanding of it is tied to how individuals, communities and institutions envision place and how their interactions are maintained over time. Results from the scoping review reveal four interrelated dimensions of WaSH-GBV in LMICs, including structural, physical, psychosocial, and sexual violence. Structural violence is mutually constitutive with the other dimensions of violence. Further, the in-depth interviews (n=27; 16 women and 11 men) reveal that individual understanding and perceptions of WaSH-GBV are complex, socially constructed and context-dependent. Participants’ narratives indicate that while WaSH-GBV may occur in one place, the psychosocial impacts are not bound to place and time. \nThis research makes significant contributions to knowledge, policy, and practice. Theoretically, the research demonstrates the utility of incorporating feminist political ecology with political ecology of health to form an integrated theoretical framework – feminist political ecology of health (FPEH) for a broader understanding of the multidimensional nature of WaSH-GBV and how contextual factors produce and reinforce experiences of violence across space and place. The framework is a useful tool for exploring how structural factors at different scales interact to shape gendered WaSH vulnerabilities in LMICs. The framework provides a robust platform for understanding the relationship between health in place and how health outcomes across scales (i.e., micro, meso and macro) are gendered. While the framework focuses on WaSH inequalities and vulnerabilities in LMICS, it is also useful in the context of developed countries and other research areas concerned with exploring health inequalities among populations. Methodologically, the research contributes to the conceptualization of WaSH-GBV in LMICs. The research also highlights the strengths of employing multiple qualitative methods such as scoping reviews and in-depth interviews for a broader understanding of WaSH-GBV in cross-cultural settings. In terms of policy, understanding WaSH-GBV as a relational outcome will facilitate global efforts on achieving SDGs: 3 – ensure good health and wellbeing for all; 5 – achieve gender equality and empower all women and girls; and 6 – ensure water and sanitation for all. The research also highlights the need for policies that are tailored and integrate community perspectives and experiences for promoting collective social change and community-led mobilization efforts that promote gender equality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".