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Record W1976448806 · doi:10.1177/1757975913516652

Between-school variation and student characteristics associated with the accuracy of weight status perception among students: does the school a student attends impact his/her weight status perception?

2014· article· en· W1976448806 on OpenAlexaffabout
Andriana Barisic, Scott T. Leatherdale, Taryn Sendzik

Bibliographic record

VenueGlobal Health Promotion · 2014
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of WaterlooCancer Care Ontario
Fundersnot available
KeywordsSocioeconomic statusPerceptionLogistic regressionPsychologyDemographySocial statusMedicineGerontologyEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Research has identified that perceived weight status is a better predictor of weight control behavior than actual weight status. The purpose of this study was to examine whether the accuracy of weight status perception varies across schools, and to identify the student-level characteristics associated with inaccurate weight status perception among 25,060 grade 9 to 12 students attending 76 schools in Ontario, Canada. Although the majority of adolescents (60.4%) had accurate weight status perceptions, multi-level logistic regression analyses revealed significant between-school variability in the accuracy of weight status perceptions for both males and females. School location and school-level socioeconomic status were the school-level variables analyzed. We identified that males attending urban or suburban schools were more likely to overestimate their weight status compared with males attending rural schools. Important student-level characteristics included grade, weight status, sports participation and social influences. Additional research is required to better understand both the school- and student-level characteristics associated with the accuracy of weight status perceptions among adolescents.

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 score0.006
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.351
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

Citations0
Published2014
Admission routes2
Has abstractyes

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