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Record W3157332559 · doi:10.24171/j.phrp.2021.12.2.07

Sex differences in weight perception and weight gain among Black college students in the USA

2021· article· en· W3157332559 on OpenAlexaff
Jounghee Lee, Jaesin Sa, Jean‐Philippe Chaput, James Heimdal, Beatrice Nelson, Beomyoung Cho, Elizabeth Kwon

Bibliographic record

VenueOsong Public Health and Research Perspectives · 2021
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOverweightObesityWeight gainHistorically black colleges and universitiesMedicineGerontologyDemographyWeight managementIntervention (counseling)Body weightAfrican americanInternal medicineNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: The aims of this study were to examine the prevalence of overweight/obesity and to explore sex differences in body weight perceptions and correlates of weight gain among Black students at 2 historically Black colleges and universities (HBCUs) in the USA. METHODS: Participants completed a paper-based survey, and their height and weight were measured (67% completion rate). RESULTS: The overweight and obesity rates were 33.8% and 26.9%, respectively. More females than males accurately assessed their weight (p < 0.05). Body weight underestimation was associated with male sex, excellent/very good perceived overall health, and not being informed by a doctor of having overweight or obesity (p < 0.01). Higher odds of ≥5% weight gain were related to female sex, living on campus, and not being informed by a doctor of having overweight or obesity (p < 0.05). CONCLUSION: Given the high overweight and obesity rates among Black students, HBCUs in the USA should develop intervention strategies for the prevention and management of overweight and obesity. College health educators at HBCUs need to provide regular check-ups or health screenings that help male students perceive their weight accurately and prevent weight underestimation. It is important for HBCUs to monitor and address weight gain among Black students as early as possible.

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)0.015
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.198
GPT teacher head0.524
Teacher spread0.326 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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