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Record W2945495529

Exploring the association between body-related emotions and university students' mental health and physical activity behaviour

2018· article· en· W2945495529 on OpenAlexaff
Amy Nesbitt, Eva Pila, Andrée L. Castonguay, Catherine M. Sabiston

Bibliographic record

VenueJournal of Exercise, Movement, and Sport (SCAPPS refereed abstracts repository) · 2018
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMontreal Neurological Institute and HospitalUniversity of SaskatchewanCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsPrideShamePsychologyMental healthAssociation (psychology)Affect (linguistics)Clinical psychologyDevelopmental psychologySocial psychologyPsychiatryPsychotherapist
DOInot available

Abstract

fetched live from OpenAlex

Body image emotions are important correlates of health behaviour engagement and well-being, and are especially salient among emerging adults. Given that emotions tied to the body's appearance are distinct from those linked to the body's function, it is important to examine how these domain-specific emotions are associated with health outcomes. This multi-study investigation examined the unique associations between appearance- and fitness-related emotions of pride, shame, and guilt, and indices of mental health and physical activity (PA) behaviour among university students. Study 1 compared the strength of the associations between appearance- and fitness-related emotions to mental health and PA behaviour. University students (N=387) completed measures of appearance- and fitness-related emotions, self-esteem, depression, affect, and PA behaviour. Fitness-related pride, shame, and guilt were significantly stronger correlates of PA behaviour (r=-.50-.53; Z=5.38-6.80, p

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)0.001
metaresearch head score (Gemma)0.005
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.295
Teacher spread0.266 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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Same venueJournal of Exercise, Movement, and Sport (SCAPPS refereed abstracts repository)Same topicEating Disorders and BehaviorsFrench-language works237,207