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Record W2416666306 · doi:10.1093/pch/19.7.e113

Sex disparity and perception of obesity/overweight by parents and grandparents

2014· article· en· W2416666306 on OpenAlexaff
Jiarong Li, Jun Lei, Shi Wu Wen, Leshan Zhou

Bibliographic record

VenuePaediatrics & Child Health · 2014
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCentral South University
KeywordsGrandparentOverweightObesityDemographyMedicineBody mass indexLogistic regressionChildhood obesityNormal weightPediatricsGerontologyDevelopmental psychologyPsychologyEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the factors associated with the sex disparity showing a greater prevalence of obesity/overweight in boys compared with girls in Chinese school children. METHODS: Sampled students and their parents were asked to complete a questionnaire. Perceptions of weight status by the parents, grandparents and children themselves were collected. A logistic regression analysis was used. RESULTS: The sampled students included 327 obese/overweight students and 1078 students with normal body mass index (BMI). The crude OR of obesity/overweight for boys compared with girls was 1.57 (95% CI 1.22 to 2.01). The increased risk of childhood obesity/overweight for boys remained after adjustment for prenatal and infant factors, daily habits and family situation, but disappeared after adjustment for perception of weight status (OR 1.27 [95% CI 0.93 to 1.67]). There were differences in underestimation of children's weight status between boys and girls by their parents and grandparents (OR 1.33 [95% CI 1.08 to 1.64] and OR 1.42 [95% CI 1.15 to 1.75], respectively). CONCLUSIONS: Misconceptions about a child's weight status were prevalent among parents and grandparents, and boys' weight status was more frequently underestimated than girls. The disparity of underestimating weight according to sex may partially contribute to the difference in the prevalence of obesity/overweight between boys and girls among Chinese school children.

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 score0.008
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.262
Teacher spread0.254 · 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

Citations41
Published2014
Admission routes1
Has abstractyes

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