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Record W3088545697 · doi:10.1177/1948550620959236

A Social-Belonging Intervention Benefits Higher Weight Students’ Weight Stability and Academic Achievement

2020· article· en· W3088545697 on OpenAlexafffund
Christine Logel, Joel M. Le Forestier, Eben B. Witherspoon, Omid Fotuhi

Bibliographic record

VenueSocial Psychological and Personality Science · 2020
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of TorontoUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of WaterlooRaikes Foundation
KeywordsNormativePsychologyPsychological interventionIntervention (counseling)Developmental psychologyClinical psychologyAcademic achievementSocial psychologyGerontologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Psychological interventions can narrow college achievement gaps between students from nonstigmatized and stigmatized groups. However, no intervention we know of has investigated effects for one highly stigmatized group: people of higher bodyweights. We analyzed data from a prematriculation social-belonging intervention trial at 22 colleges, which conveyed that adversity in the college transition is normative, temporary, and nondiagnostic of lack of belonging. Nine months postintervention, higher weight participants in a standard belonging treatment had higher first-year grade point averages (GPAs) than controls and maintained more stable weights, an indicator of physical well-being. Effects of a belonging treatment customized to specific colleges were directionally similar but nonsignificant. Exploratory analyses revealed that effects did not differ by race and that weight effects were driven by women. Together, results show that higher weight students contend with belonging concerns that contribute to a weight gap in GPA, but belonging interventions can raise GPA and promote healthy weight stability.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.281
GPT teacher head0.532
Teacher spread0.251 · 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

Citations12
Published2020
Admission routes2
Has abstractyes

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