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Record W4410160891 · doi:10.1186/s40337-025-01240-y

Empirical validation of a developmental model for binge-eating disorder in adolescents: a structural equation modeling approach

2025· article· en· W4410160891 on OpenAlexaffabout
Camille Clermont, Christopher Rodrigue, Catherine Bégin

Bibliographic record

VenueJournal of Eating Disorders · 2025
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsStructural equation modelingPsychologyBinge-eating disorderBinge eatingClinical psychologyDevelopmental psychologyEating disordersBulimia nervosaStatisticsMathematics

Abstract

fetched live from OpenAlex

Loss of control (LOC) eating is characterized by a reported sense of being unable to control food intake, regardless of the amount of food consumed. It is the hallmark feature of binge-eating episodes, which involve consuming an unusually large amount of food within a discrete time frame, accompanied by a sense of LOC over eating. Some studies investigating the progression of LOC-eating symptoms in children and adolescents suggest that LOC-eating may be a precursor to binge-eating disorder (BED) in adults. To explain the progression from LOC-eating in childhood and adolescence to BED in adulthood, Tanofsky-Kraff and her colleagues developed a theoretical model highlighting three main constructs: negative affectivity, reward sensitivity, and executive functioning. However, a thorough empirical validation of this model has not yet been performed. The current study aims to empirically test Tanofsky-Kraff and her colleagues’ model via structural equation modeling (SEM) and explore potential gender and age differences within this framework. We surveyed 969 adolescents aged 12 to 18 years from the Quebec City area who completed self-report questionnaires. Our findings revealed that both negative affectivity and reward sensitivity are significantly associated with binge-eating symptomatology, whereas self-reported executive functioning is not significantly associated with binge-eating symptomatology. These results support several key components of the proposed model and provide insights into the interactions between the variables when tested simultaneously. Additionally, our study underscores the importance of considering individual factors such as age and gender in understanding binge-eating symptomatology. Some young people feel they can’t control how much they eat, even if they aren’t eating large amounts. This is called loss of control (LOC) eating. Some research suggests that LOC eating in children and adolescents could lead to binge-eating disorder (BED) later in life. To better understand why this might happen, experts have identified three key factors: struggling with negative emotions, being more sensitive to food rewards, and having difficulties with executive functioning. However, this idea hasn’t been fully tested yet. We surveyed 969 teenagers, aged 12 to 18, from the Quebec City area to explore how these three factors might be linked to binge-eating symptomatology. Our results suggest that adolescents who struggle with negative emotions or feel strongly rewarded by food might be at higher risk to report binge-eating symptoms. These findings underscore the importance of teaching young people how to manage their emotions from an early age. The also highlight the differential role of these factors depending on age and gender, which is important for developing more personalized prevention and treatment strategies.

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)0.015
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
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 head0.062
GPT teacher head0.366
Teacher spread0.303 · 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 designSimulation or modeling
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

Citations2
Published2025
Admission routes2
Has abstractyes

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