Household Water Insecurity in the Western Amazon, Amazonas, Brazil: A Preliminary Approach
Bibliographic record
Abstract
The objective was to evaluate the quality of an instrument to measure the experience of household water insecurity (WI) and the factors associated with the prevalence of WI in an urban area in a municipality in the Western Brazilian Amazon. A cross-sectional, population-based study was conducted to investigate 983 urban households. The Household Water Insecurity Experiences (HWISE) scale was used to measure the psychometric properties of reliability and validity. An exploratory factor analysis was conducted, and the prevalence ratio (PR, 95% CI) was calculated, considering WI as the dependent variable and the other household variables as independent variables. WI affected 46.2% (95% CI: 43.0–49.4%) of the households, independently associated with: head of the family as parent/other and presence of a child in the household. The instrument exhibited unidimensionality in the factor analyses and was considered to be both reliable and valid, as indicated by a Cronbach’s α coefficient of 0.958. Household WI is a serious public health problem in the Amazon in correlation with both social vulnerability and a lack of public services. As a preliminary approach, the scale proved to be valid and reliable. However, considering the Amazonian context, misunderstandings about some issues by respondents were identified, and further validation studies are needed to improve the intelligibility of these questions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".