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Record W2319521742 · doi:10.1080/17441692.2016.1166256

Growing healthy children and communities: Children’s insights in Lao People’s Democratic Republic

2016· article· en· W2319521742 on OpenAlexaff
Mónica Ruiz‐Casares

Bibliographic record

VenueGlobal Public Health · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalMontreal Children's HospitalMcGill University
Fundersnot available
KeywordsContext (archaeology)Public healthFeelingDemocracyCorporal punishmentInterpersonal communicationPsychologyRural areaSuicide preventionPoison controlMedicineEnvironmental healthSocial psychologyPolitical scienceNursingGeographyPolitics

Abstract

fetched live from OpenAlex

A diverse group of 103 children aged 7-11 years old living in family and residential care in rural and urban settings in two northern provinces in Lao People's Democratic Republic participated in group discussions using images and community mapping. Children's identified sources of risk and protection illustrate primary public health and protection concerns and resources. Young children worried about lack of hygiene, unintentional injuries, corporal punishment, and domestic violence. They also expressed concern about gambling and children sleeping in the streets, even if they had never seen any of the latter in their communities. In contrast, food and shelter; artistic, religious, and cultural practices; supportive interpersonal relationships; and schooling largely evoked feelings of safety and belonging. Images that prompted conflicting interpretations surfaced individual and contextual considerations that nuanced analysis. Researchers and decision-makers will benefit from using this developmentally appropriate, context-sensitive child-centred visual method to elicit young children's views of risk and protection. It may also serve as a tool for public health education. Involving young children in the initial selection of images would further enhance the efficiency of the method.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.187
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.324
Teacher spread0.287 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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