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Record W2983982169 · doi:10.1080/07448481.2019.1679814

Sex and racial/ethnic differences in the prevalence of overweight and obesity among U.S. college students, 2011–2015

2019· article· en· W2983982169 on OpenAlexaff
Jaesin Sa, Beomyoung Cho, Jean‐Philippe Chaput, Joon Chung, Siyoung Choe, Julie A. Gazmararian, Jong Cheol Shin, Chung Gun Lee, Gabriel Navarrette, Tiffany Han

Bibliographic record

VenueJournal of American College Health · 2019
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsOverweightObesityDemographyMedicineEthnic groupLogistic regressionOddsOdds ratioCross-sectional studyGerontologyBody mass indexInternal medicineSociology

Abstract

fetched live from OpenAlex

Objective To investigate sex and racial/ethnic differences in overweight and obesity in college students. Participants: A nationally representative sample of 319,342 U.S. college students (mean age = 20.4 years; 67.7% female) from Fall 2011 to Spring 2015. Methods: A secondary data analysis of multi-year cross-sectional data was performed. Multiple logistic regression was used to examine factors (e.g. cumulative grade average, year in school, and living place) associated with overweight and obesity determined from BMI calculated by self-reported height and weight. Results: The prevalence of overweight and obesity was significantly higher for both sexes in Spring 2015 than in Fall 2011. Significant differences were found in overweight and obesity by sex and race/ethnicity. Higher adjusted odds ratios for overweight and obesity were observed for men, blacks, and Hispanics (compared to whites). Asians had the lowest adjusted odds of overweight and obesity. Conclusions: Intervention strategies for the prevention and management of overweight and obesity in U.S. college students should consider sex and racial/ethnic inequalities.

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)0.001
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 score0.002
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.017
GPT teacher head0.318
Teacher spread0.301 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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