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Record W2118939860 · doi:10.1002/erv.813

The patient's account of relapse and recovery in anorexia nervosa: a qualitative study

2007· article· en· W2118939860 on OpenAlexaff
Anita Federici, Allan S. Kaplan

Bibliographic record

VenueEuropean Eating Disorders Review · 2007
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsToronto General HospitalUniversity of TorontoYork University
Fundersnot available
KeywordsAnorexia nervosaEating disordersPsychologyQualitative researchPerspective (graphical)Clinical psychologyAnorexiaMental illnessPsychotherapistPsychiatryMedicineMental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to explore the subjective accounts of weight-recovered female patients, who met DSMIV criteria for anorexia nervosa (AN), regarding their views of their illness following weight restoration. METHOD: Qualitative semi-structured interviews were administered to 15 participants to ascertain their perspective of the factors that either contributed to their maintaining a healthy weight, or the factors involved in their having relapsed over the follow-up period. RESULTS: Qualitative analyses revealed six core categories: internal motivation to change, recovery as a work in progress, the perceived value of the treatment experience, developing supportive relationships, awareness and tolerance of negative emotion and self-validation. DISCUSSION: This study provides valuable information about the way in which AN patients experience their illness and highlight the factors that help or hinder recovery. These findings may help enhance relapse prevention programs and potentially enhance our ability to identify and target those individuals at the greatest risk of relapsing.

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)0.010
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0010.002
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 head0.026
GPT teacher head0.357
Teacher spread0.331 · 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 designQualitative
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

Citations237
Published2007
Admission routes1
Has abstractyes

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