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Record W2055428052 · doi:10.1002/erv.996

French adaptation of the eating disorder recovery self‐efficacy questionnaire (EDRSQ): Psychometric properties and conceptual overview

2010· article· en· W2055428052 on OpenAlexaff
Stéphanie Couture, Serge Lecours, Geneviève Beaulieu‐Pelletier, Frédérick L. Philippe, Irène Strychar

Bibliographic record

VenueEuropean Eating Disorders Review · 2010
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsCentre Hospitalier de l’Université de MontréalMcGill UniversityUniversité de Montréal
Fundersnot available
KeywordsPsychologyEating disordersConfirmatory factor analysisClinical psychologyNormativeConstruct validityFactorial analysisReliability (semiconductor)PsychometricsStructural equation modeling

Abstract

fetched live from OpenAlex

High prevalence of Eating Disorders (EDs) and poor treatment outcome rates have urged research in the assessment of EDs. Self-efficacy is a key motivational factor in the recovery from EDs. A self-report measure, the Eating Disorder Recovery Self-Efficacy Questionnaire (EDRSQ), was recently developed to assess confidence in adopting healthy eating behaviours and in maintaining a realistic body image. The objectives of this study were to (a) translate the EDRSQ to French (EDRSQ-F), (b) assess the psychometric properties of this French version, and (c) establish normative data for a non-clinical sample. Participants were 203 undergraduate women. They completed the EDRSQ-F and measures of ED symptoms, depression and self-esteem. A confirmatory factor analysis (CFA) revealed a bi-factorial structure. Both scales demonstrated evidence of reliability and theoretically consistent evidence of construct validity. Findings support the validity of the EDRSQ-F and suggest it is a useful instrument for the assessment of EDs.

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)0.004
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.046
GPT teacher head0.295
Teacher spread0.249 · 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

Citations8
Published2010
Admission routes1
Has abstractyes

Explore more

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