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Record W4386906332 · doi:10.1016/j.bodyim.2023.101628

Food insecurity is associated with muscle dysmorphia symptomatology among a sample of Canadian adolescents and young adults

2023· article· en· W4386906332 on OpenAlexafffundabout
Kyle T. Ganson, Nelson Pang, Alexander Testa, Dylan B. Jackson, Jason M. Nagata

Bibliographic record

VenueBody Image · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of Toronto
FundersConnaught Fund
KeywordsFood insecurityPsychologyYoung adultIntervention (counseling)Clinical psychologyMultilevel modelPsychiatryFood securityDevelopmental psychology

Abstract

fetched live from OpenAlex

Prior research has documented the association between food insecurity and eating disorders, disordered eating behaviors, and body dissatisfaction. No known research has investigated whether food insecurity is associated with muscle dysmorphia symptomatology, which was the aim of this study. Data from 912 adolescents and young adults in Canada were analyzed. Linear regression analyses were used to determine the association between experiencing past year food insecurity and current muscle dysmorphia symptomatology. Among the sample, 15.7% reported experiencing any food insecurity. In regression analyses, food insecurity was significantly associated with greater overall muscle dysmorphia symptomatology and symptoms of Functional Impairment and Appearance Intolerance. Nearly one in five (18.5%) participants who reported food insecurity were at clinical risk for muscle-dysmorphia. Findings add to the growing literature on the adverse correlates of food insecurity and underscore the need for more research and intervention efforts to address the relationship between food insecurity and muscle dysmorphia symptomatology.

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)0.001
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 score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.346
Teacher spread0.288 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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