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Eating Disorders: Anorexia Nervosa, Bulimia Nervosa, and Binge Eating Disorder

2012· other· en· W1601356117 on OpenAlexaff
Howard Steiger, Kenneth R. Bruce, Mimi Israël

Bibliographic record

VenueHandbook of Psychology, Second Edition · 2012
Typeother
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteDouglas College
Fundersnot available
KeywordsBulimia nervosaDietingEating disordersAnorexia nervosaPsychologyBinge eatingBinge-eating disorderClinical psychologyEtiologyPsychiatryDisordered eatingAnxietyImpulsivityPsychotherapistMedicineObesityWeight loss

Abstract

fetched live from OpenAlex

This chapter addresses anorexia nervosa, bulimia nervosa, and the various eating syndromes that are classified as eating disorders not otherwise specified, providing an up-to-date discussion on phenomenology (including eating-specific and comorbid features), epidemiology, and etiology. In the eating disorders, interactive effects involving environmental risks (e.g., cultural inducements toward excessive dieting or overachievement) and constitutional susceptibilities in vulnerable individuals (e.g., heritable propensities toward appetitive dysregulation, affective instability, or excessive anxiety) are highly evident, perhaps more so than in many other mental-health problems. As a result, the eating disorders encourage us to think in terms of multiple determinants and maintaining factors. A main organizing concept throughout this chapter is a multidimensional etiological concept that integrates findings from biological, psychological, family-developmental, and cross-cultural studies and, more importantly, from studies on constitution x environment interactions. The chapter also provides a heuristic for differentiating factors that constitute disorder-specific risks from those that contribute (and that may explain comorbid traits, like marked impulsivity or affective instability) without having specific causal roles.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0530.000

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 head0.021
GPT teacher head0.311
Teacher spread0.291 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2012
Admission routes1
Has abstractyes

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