Concept mapping: Engaging stakeholders to identify factors that contribute to empowerment in the water and sanitation sector in West Africa
Bibliographic record
Abstract
Research has shown that inadequate access to water, sanitation, and hygiene (WASH) affects women and girls in several ways, including lowering their participation in the labour market and community activities and contributing to psychosocial stress and poor educational outcomes. There is growing awareness that addressing the gender inequalities related to WASH that many women and girls face on a daily basis must go beyond focusing on delivery of infrastructure and facilities alone and include attention to issues of empowerment. Yet there is limited exploration of how the concept of empowerment is defined and applied in the WASH sector and thus limited information on how it could be measured. This study used concept mapping to uncover the meaning and key dimensions of empowerment in WASH among 34 and 24 stakeholders in Asutifi North District, Ghana, and Banfora Commune, Burkina Faso, respectively. The study was part of initial steps toward choosing indicators for developing an Empowerment in WASH Index. In Ghana and Burkina Faso, 42 and 29 items were generated, respectively. These items were thought to empower men and women in WASH at the household and community levels. In both case studies, 7 clusters were generated and named by participants, and themes related to sharing of information, sociocultural norms, participation, and accessibility of WASH services were associated with empowerment. Some themes were unique to each case study site. Participants also showed a multidimensional and multilevel understanding of empowerment. Concept mapping created an effective balance between individual and group contributions and facilitated accessible, rapid, and contextually relevant data collection. The findings can be used to generate domains of empowerment in future quantitative research as well as inform the design of the Empowerment in WASH Index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".