Household water security is a mediator of household food security in a nationally representative sample of Mexico
Bibliographic record
Abstract
OBJECTIVE: Explore the relationship between water insecurity (WI) and food security and their covariates in Mexican households. DESIGN: A cross-sectional study with nationally representative data from the National Health and Nutrition Survey-Continuous 2021 (in Spanish, ENSANUT-Continua 2021), collected data from 12 619 households. SETTING: WI was measured using the Household Water Insecurity Experiences (HWISE) Scale in Spanish and adapted to the Mexican context. Food security was measured using the Latin American and Caribbean Food Security Scale. A generalised path model was used to produce two simultaneous logistical regression equations - WI (HWISE ≥ 12) and moderate-to-severe food insecurity (FI) - to understand key covariates as well as the contribution of WI to FI. PARTICIPANTS: The head of the household, an adult of >18 years of age, consented to participate in the survey. RESULTS: Households experiencing WI were more likely to experience moderate-to-severe FI (OR = 2·35; 95 % CI: 2·02, 2·72). The odds of WI were lower in households with medium (OR = 0·74; 95 % CI: 0·61, 0·9) to high (OR = 0·45; 95 % CI: 0·37, 0·55) asset scores. WI also depended on the region of Mexico. FI is more prevalent in indigenous people (OR = 1·29; 95 % CI: 1·05, 1·59) and rural households (OR = 0·42; 95 % CI: 1·16, 1·73). Notably, wealth and household size did not contribute directly to FI but did so indirectly through the mediating factor of WI. CONCLUSIONS: Our study shows that there are structural factors that form part of the varied determinants of WI, which in turn is closely linked to FI.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Cross-sectional analysis of water insecurity as a mediator of household food security in Mexico.
It studies household water and food security in Mexico, not research itself.
Public health analysis of water and food security in Mexico, not research practice.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".