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Record W4417460074 · doi:10.1080/10640266.2025.2602456

Associations between eating disorders and difficulties with emotion regulation in a sample of adolescent boys and young adult men

2025· article· en· W4417460074 on OpenAlexaffabout
Kyle T. Ganson, Jason M. Lavender, Rachel F. Rodgers, Alexander Testa, Jason M. Nagata

Bibliographic record

VenueEating Disorders · 2025
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEating disordersYoung adultAnorexia nervosaAssociation (psychology)CLARITYAnorexiaDisordered eating

Abstract

fetched live from OpenAlex

This study aimed to examine the association between eating disorders and emotion regulation difficulties in a sample of adolescent boys and young adult men in Canada and the United States (2024; N = 925). Multiple linear regression analyses were used to explore whether boys and men with any probable eating disorder (i.e. anorexia nervosa/atypical anorexia nervosa, bulimia nervosa, binge-eating disorder) had higher scores on the Difficulties in Emotion Regulation-18 (DERS-18) measure, while adjusting for relevant sociodemographic confounders. Participants with any probable eating disorder, compared to those without, had significantly higher total DERS-18 scores (B = 9.03, 95% CI 6.66, 11.39), and higher scores on the clarity (B = 1.14, 95% CI 0.59, 1.69), goals (B = 1.63, 95% CI 0.97, 2.28), impulse (B = 1.43, 95% CI 0.96, 1.90), nonacceptance (B = 2.48, 95% CI 1.79, 3.18), and strategies (B = 2.30, 95% CI 1.71, 2.90) subscales. These findings largely align with and expand prior research on eating disorders and emotion regulation that has predominantly focused on females. Treatment methods that address adaptive emotion regulation abilities of boys and men with eating disorders may have utility, with a particular focus on increasing acceptance of emotions and developing strategies for emotion regulation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.025
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.274
Teacher spread0.264 · 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.

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
Published2025
Admission routes2
Has abstractyes

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