Development and validation protocol for an instrument to measure household water insecurity across cultures and ecologies: the Household Water InSecurity Experiences (HWISE) Scale
Bibliographic record
Abstract
INTRODUCTION: A wide range of water-related problems contribute to the global burden of disease. Despite the many plausible consequences for health and well-being, there is no validated tool to measure individual- or household-level water insecurity equivalently across varying cultural and ecological settings. Accordingly, we are developing the Household Water Insecurity Experiences (HWISE) Scale to measure household-level water insecurity in multiple contexts. METHODS AND ANALYSIS: After domain specification and item development, items were assessed for both content and face validity. Retained items are being asked in surveys in 28 sites globally in which water-related problems have been reported (eg, shortages, excess water and issues with quality), with a target of at least 250 participants from each site. Scale development will draw on analytic methods from both classical test and item response theories and include item reduction and factor structure identification. Scale evaluation will entail assessments of reliability, and predictive, convergent, and discriminant validity, as well as the assessment of differentiation between known groups. ETHICS AND DISSEMINATION: Study activities received necessary ethical approvals from institutional review bodies relevant to each site. We anticipate that the final HWISE Scale will be completed by late 2018 and made available through open-access publication. Associated findings will be disseminated to public health professionals, scientists, practitioners and policymakers through peer-reviewed journals, scientific presentations and meetings with various stakeholders. Measures to quantify household food insecurity have transformed policy, research and humanitarian aid efforts globally, and we expect that an analogous measure for household water insecurity will be similarly impactful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".