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Record W2884872473 · doi:10.3390/ijerph15081564

Nonadult Supervision of Children in Low- and Middle-Income Countries: Results from 61 National Population-Based Surveys

2018· article· en· W2884872473 on OpenAlexafffund
Mónica Ruiz‐Casares, José Ignacio Nazif‐Muñoz, René Iwo, Youssef Oulhote

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsLow and middle income countriesLow incomeEnvironmental healthPopulationGeographyDemographic economicsSocioeconomicsMedicineEconomic growthEconomicsDeveloping country

Abstract

fetched live from OpenAlex

Despite scarce empirical research in most countries, evidence has shown that young children are unsupervised or under the supervision of another young child while their adult caregivers attend work or engage in other activities outside the home. Lack of quality supervision has been linked to unintentional childhood injuries and other negative outcomes. Nationally representative, population-based data from rounds four and five of the Multiple Indicator Cluster Surveys (MICS) and four to eight of the Demographic and Health Surveys (DHS) from 61 low- and middle-income countries were used to estimate prevalence and socio-economic factors associated with leaving children under five years old home alone or under the care of another child younger than 10 years of age. Socio-economic factors included age and sex of the child, rurality, wealth, maternal education, and household composition. Large variations in the prevalence rates (0.1⁻35.3% for children home alone and 0.2⁻50.6% for children supervised by another child) and associated factors have been recorded within and across regions and countries. Understanding why and under what conditions children are home alone or under the supervision of another child is crucial to the development of suitable policies and interventions to protect young children, promote healthy growth, and support caregivers.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.441
Teacher spread0.354 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations42
Published2018
Admission routes2
Has abstractyes

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