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Record W2040875808 · doi:10.1177/0145445510393478

Differences in the Nature of Body Image Disturbances Between Female Obese Individuals With Versus Without a Comorbid Binge Eating Disorder: An Exploratory Study Including Static and Dynamic Aspects of Body Image

2011· article· en· W2040875808 on OpenAlexaff
Tanja Legenbauer, Silja Vocks, Sabrina Betz, María José Báguena Puigcerver, Andrea Benecke, Nikolaus F. Troje, Heinz Rüddel

Bibliographic record

VenueBehavior Modification · 2011
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyBinge-eating disorderCognitionBody shapeBinge eatingObesityBody mass indexPerceptionClinical psychologyEating disordersPsychological interventionDevelopmental psychologyBulimia nervosaPsychiatryPhysical medicine and rehabilitationMedicineInternal medicineNeuroscience

Abstract

fetched live from OpenAlex

Various components of body image were measured to assess body image disturbances in patients with obesity. To overcome limitations of previous studies, a photo distortion technique and a biological motion distortion device were included to assess static and dynamic aspects of body image. Questionnaires assessed cognitive-affective aspects, bodily attitudes, and eating behavior. Patients with obesity and a binge eating disorder (OBE, n = 15) were compared with patients with obesity only (ONB; n = 15), to determine the nature of any differences in body image disturbances. Both groups had high levels of body image disturbances with cognitive-affective deficits. Binge eating disorder (BED) participants also had perceptual difficulties (static only). Both groups reported high importance of weight and shape for self-esteem. There were some significant differences between the groups suggesting that a comorbid BED causes further aggravation. Body image interventions in obesity treatment may be warranted.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.089
GPT teacher head0.369
Teacher spread0.280 · 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

Citations51
Published2011
Admission routes1
Has abstractyes

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