MétaCan
Menu
Back to cohort
Record W4382864824 · doi:10.21149/14424

Viabilidad de una escala de experiencias de inseguridad del agua en hogares mexicanos

2023· article· es· W4382864824 on OpenAlexaff
Teresa Shamah‐Levy, Verónica Mundo‐Rosas, Alicia Muñoz‐Espinosa, Ignacio Méndez‐Gómez‐Humarán, Rafael Pérez‐Escamilla, Hugo Melgar‐Quiñones, Edward A. Frongillo, Sera L. Young

Bibliographic record

VenueSalud Pública de México · 2023
Typearticle
Languagees
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesInternal consistencyGeographyPolitical sciencePsychologyArtPsychometrics

Abstract

fetched live from OpenAlex

OBJETIVO: Identificar la viabilidad de la Escala de Experiencias de Inseguridad del Agua en el Hogar (Household Water Insecurity Experiences Scale, HWISE, por sus siglas en inglés) como herramienta para evaluar las experiencias de hogares mexicanos en relación con la inseguridad en el acceso al agua. Material y métodos. La escala fue integrada en la Encuesta Nacional de Salud y Nutrición Continua 2021 (Ensanut Continua 2021) y se utilizaron tres criterios para evaluar su viabilidad: 1) Consistencia interna: Se aplicó la prueba Alfa de Cronbach para estimar la correlación entre los ítems de la escala. Se consideró un punto de corte de al menos 0.80 como criterio de confiabilidad; 2) Equivalencia de los ítems para distintos indicadores sociodemográficos; y 3) Variables asociadas con inseguridad del agua. RESULTADOS: La escala HWISE mostró: 1) Buena confiabilidad o consistencia interna (Alfa de Cronbach de 0.928); 2) comportamiento equivalente de los ítems en los contextos urbano y rural, en nueve regiones del país y por terciles de condiciones de bienestar; y 3) asociación significativa con variables predictoras de inseguridad del agua. CONCLUSIONES: La escala HWIS, adaptada para México, es apropiada para su uso en evaluar la condición de inseguridad del agua en hogares mexicanos.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.015
GPT teacher head0.306
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueSalud Pública de MéxicoSame topicChild Nutrition and Water AccessFrench-language works237,207