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Record W1966161532 · doi:10.1002/erv.418

Silencing the self and suppressed anger: relationship to eating disorder symptoms in adolescent females

2001· article· en· W1966161532 on OpenAlexaff
Shannon L. Zaitsoff, Josie Geller, Suja Srikameswaran

Bibliographic record

VenueEuropean Eating Disorders Review · 2001
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsPsychologyAngerEating disordersClinical psychologyFeelingDevelopmental psychology

Abstract

fetched live from OpenAlex

Abstract Objective: This study examined the extent to which inhibited expression of negative feelings and an interpersonal style that focuses on others' needs and expectations are related to eating disorder symptoms in adolescent females. Method: Female high school adolescents (N = 235) completed the Anger Expression Scale, the Silencing the Self Scale, and measures of eating disorder symptoms, self‐esteem, and psychological adjustment. Results: Adolescents with higher eating disorder symptom scores had significantly higher levels of anger inhibition and silencing the self scores. In regression analyses, the Silencing the Self and Anger Expression Scales contributed statistically significant unique variance to cognitive and behavioural eating disorder symptoms scores after controlling for shape‐ and weight‐based self‐esteem. A similar, though weaker, pattern of results was found after controlling for global self‐esteem. Discussion: These results partially replicate relationships found between inhibited self‐expression and eating disorder symptoms in adult clinical samples. Implications for the development of eating disorder symptoms are addressed. Copyright © 2002 John Wiley & Sons, Ltd and Eating Disorders Association.

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)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.042
GPT teacher head0.322
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

Citations91
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Eating Disorders ReviewSame topicEating Disorders and BehaviorsFrench-language works237,207