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Record W4415013809 · doi:10.1016/j.eatbeh.2025.102041

Examining associations among food insecurity, muscularity-oriented disordered eating, and internalizing symptoms in undergraduate students

2025· article· en· W4415013809 on OpenAlexaffabout
Aditya Thakur, Lisa Y Zhu, Lindsay P. Bodell

Bibliographic record

VenueEating Behaviors · 2025
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsWestern University
Fundersnot available
KeywordsDisordered eatingAssociation (psychology)Mental healthPsychological interventionAnxietyDepression (economics)Food insecurityPsychological distress

Abstract

fetched live from OpenAlex

Food insecurity (FI) is a serious public health concern linked to negative physical and mental outcomes, including eating disorder symptoms. However, little is known about its relationship with muscularity-oriented disordered eating (MODE), which involves eating behaviours aimed at increasing muscle mass and decreasing body fat. This study examined the association between FI and MODE, as well as the potential indirect effects of depression and anxiety. A sample of 394 undergraduate students (72.34 % women) from a Canadian university completed online self-report measures of FI, MODE, depression, and anxiety. Linear regression analyses indicated that FI was significantly positively associated with MODE, even when controlling for gender. Indirect effects analyses further revealed that this association was partially explained by depression and anxiety in women, suggesting that psychological distress may play a key role in linking FI to MODE. Importantly, our findings provide evidence that MODE is not limited to individuals with greater financial resources to access high-protein diets and supplements. Food insecurity does not preclude engagement in MODE, emphasizing the need for theoretical models and interventions that consider FI as a potential risk factor. Future research should employ longitudinal designs to clarify the temporal relationship between FI, MODE, and mental health factors, as well as explore these associations in more diverse populations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.336
Teacher spread0.314 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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