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Peer Reviewed: Adolescent Obesity and Social Networks

2009· article· en· W22708629 on OpenAlexfundno aff
Laura M. Koehly, Aunchalee Loscalzo

Bibliographic record

VenuePreventing Chronic Disease · 2009
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsOverweightMedicineObesityPsychological interventionIntervention (counseling)Context (archaeology)Social environmentEnvironmental healthPeer groupSocial network (sociolinguistics)DiseaseGerontologyDevelopmental psychologyPsychiatryPsychology

Abstract

fetched live from OpenAlex

The prevalence of overweight among children worldwide is growing at an alarming rate. Social relationships may contribute to the development of obesity through the interaction of biological, behavioral, and environmental factors. Although there is evidence that early environment influences the expression of obesity, very little research elucidates the social context of obesity among children or adolescents. Social network approaches can contribute to research on the role of social environments in overweight and obesity and strengthen interventions to prevent disease and promote health. By capitalizing on the structure of the network system, a targeted intervention that uses social relationships in families, schools, neighborhoods, and communities may be successful in encouraging healthful behaviors among children and their families.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.230
Threshold uncertainty score0.769

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.067
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2300.048

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 head0.055
GPT teacher head0.419
Teacher spread0.365 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2009
Admission routes1
Has abstractyes

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