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Record W1987081048 · doi:10.1017/s1368980014000810

Body size dissatisfaction among young Chinese children in Hong Kong: a cross-sectional study

2014· article· en· W1987081048 on OpenAlexaff
Gemma Knowles, Fiona Chun Man Ling, G. Neil Thomas, Peymané Adab, Alison M. McManus

Bibliographic record

VenuePublic Health Nutrition · 2014
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersUniversity of Hong KongUniversity of Birmingham
KeywordsOverweightMedicineCross-sectional studyDemographyObesityLogistic regressionBody mass indexPediatricsInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the potential predictors of body size dissatisfaction in Chinese children. DESIGN: The Child's Body Image Scale was used to assess body size perception and dissatisfaction. BMI was calculated from objectively measured height and weight. Predictors of body size dissatisfaction were examined by logistic regression analysis. SETTING: Hong Kong, China. SUBJECTS: Six hundred and twenty children (53 % boys, aged 6·1-12·9 years) from a state-run primary school. RESULTS: Female sex (adjusted OR (AOR)=1·91; 95 % CI 1·32, 2·76), age (AOR=2·62; 95 % CI 1·65, 4·16 for 8-10 years; AOR=2·16; 95 % CI 1·38, 3·38 for >10 years), overweight (AOR=6·23; 95 % CI 3·66, 10·60) and obesity (AOR=19·04; 95 % CI 5·64, 64·32) were positively associated with desire to be thinner. Size misperception was a strong predictor of body size dissatisfaction, irrespective of actual weight status (AOR=1·90; 95 % CI 1·02, 3·54 for overestimation; AOR=0·43; 95 % CI 0·27, 0·67 for underestimation). CONCLUSIONS: Body size dissatisfaction is prevalent among Chinese children as young as 6 years. Female sex, age, overweight, obesity and overestimation of size were associated with increased desire to be thinner. These findings emphasise the importance of preventing body image issues from an early age.

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 score0.009
Threshold uncertainty score0.994

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 studies0.0000.000
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 head0.023
GPT teacher head0.362
Teacher spread0.339 · 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

Citations26
Published2014
Admission routes1
Has abstractyes

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