MétaCan
Menu
Back to cohort
Record W2371109525 · doi:10.1177/0165025416647799

Overweight and isolated

2016· article· en· W2371109525 on OpenAlexafffund
Megan E. Ames, Bonnie J. Leadbeater

Bibliographic record

VenueInternational Journal of Behavioral Development · 2016
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsOverweightPsychologyDevelopmental psychologyInterpersonal communicationLongitudinal studyYoung adultInterpersonal relationshipClinical psychologyObesitySocial psychologyMedicine

Abstract

fetched live from OpenAlex

This longitudinal study investigates whether there are particularly salient ages when being overweight is related to problems in interpersonal relationships (i.e., physical, relational, and verbal victimization, lack of friend social support, dating status, and romantic relationship worries). Participants were from a large, six-wave longitudinal study ( N = 662, 48% males, M age at T1 = 15.5 years, SD = 1.9 years). We use time-varying effect models to estimate how the associations between weight status and interpersonal problems differ from ages 12 to 28. Gender differences are also investigated. Findings show that youth who are overweight are more likely to experience verbal victimization, feel less supported by their peers, and are less likely to date than youth who are not overweight from mid-adolescence into early young adulthood. Further, females who are overweight are more likely to be physically victimized at ages 15 to 22 than females who are not overweight. The results provide a better understanding of age-related changes in interpersonal problems among youth who are overweight from adolescence into young adulthood.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.079
GPT teacher head0.475
Teacher spread0.396 · 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 source (direct Gemma or distilled Codex), 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
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Behavioral DevelopmentSame topicObesity and Health PracticesFrench-language works237,207