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Record W4403615329 · doi:10.1080/10640266.2024.2411476

Examining the roles of reward sensitivity and difficulties in emotion regulation in relation to low-restraint binge eating

2024· article· en· W4403615329 on OpenAlexafffund
Laura Lapadat, Ege Biçaker, Sarah E. Racine

Bibliographic record

VenueEating Disorders · 2024
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsBinge eatingPsychologyRelation (database)Sensitivity (control systems)Clinical psychologyDevelopmental psychologyCognitive psychologyEating disorders

Abstract

fetched live from OpenAlex

Leading treatments for binge eating target dietary restraint, but up to 35% of the people with binge eating report low restraint. This study examined the roles of reward sensitivity and emotion dysregulation in relation to low-restraint binge eating. Women with binge eating (low-restraint: n = 22; high-restraint: n = 69) and controls (n = 49) completed self-report measures of generalized reward sensitivity and emotion dysregulation and a picture-viewing task assessing craving and pleasure for high-calorie food. As expected, food-related craving and emotion dysregulation were greater in the clinical than in the control group, but no differences emerged between high- and low-restraint binge eating groups. However, correlational analyses found that, within the clinical group, the number of restraint days related to greater anticipatory sensitivity for generalized rewards and lower pleasure ratings of food. Results suggest that emotion dysregulation characterizes both high- and low-restraint binge eating. As self-reported food liking was linked with lower restraint, greater enjoyment of palatable foods may uniquely contribute to low-restraint binge eating. Increasing emphasis on emotion regulation and food-related reward sensitivity may enhance treatment outcomes for individuals with low-restraint binge eating.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.283
Teacher spread0.263 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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