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Record W1561887992

The influence of childhood obesity on the development of self-esteem.

2009· article· en· W1561887992 on OpenAlexaffabout
Fei Wang, T C Wild, Walter Kipp, Stefan Kuhle, Paul J. Veugelers

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOverweightSelf-esteemObesityConfoundingLongitudinal studyLogistic regressionMental healthChildhood obesityOddsMedicineDemographyPopulationOdds ratioBaseline (sea)PsychologyGerontologyEnvironmental healthClinical psychologyPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The consequences of overweight in childhood for physical health have received considerable attention, but relatively little research has examined the mental health consequences. This article examines longitudinal relationships between body weight and self-esteem in a nationally representative probability sample of Canadian children. DATA AND METHODS: The data are from cycles 1, 2 and 3 of the Canadian National Longitudinal Survey of Children and Youth. Logistic regression analysis using weighted data examined whether body weight at baseline predicted self-esteem two and four years later. RESULTS: When baseline self-esteem and other potential confounders were taken into account, children who were obese at baseline had almost twice the odds of reporting low self-esteem four years later, compared with children of normal body weight. Ancillary analyses indicated that baseline self-esteem was not associated with body weight status two or four years later. INTERPRETATION: The current childhood obesity epidemic may trigger an increase in the population prevalence of low self-esteem in the future. According to other research, low self-esteem predicts poor mental health. The curent childhood obesity epidemic may increase the prevalence of not only chronic diseases, but also poor mental health.

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)0.000
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 score0.395
Threshold uncertainty score0.232

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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

Citations127
Published2009
Admission routes2
Has abstractyes

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