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Record W2313521385 · doi:10.1097/nmd.0000000000000480

The Effects of Stigma on Recovery Attitudes in People With Anorexia Nervosa in Intensive Treatment

2016· article· en· W2313521385 on OpenAlexafffund
Gina Dimitropoulos, Leslie McCallum, Marlena Colasanto, Victoria Freeman, Tahany M. Gadalla

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2016
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoUniversity of CalgaryUniversity Health Network
FundersUniversity Health NetworkUniversity of Calgary
KeywordsAnorexia nervosaStigma (botany)PsychologyAnorexiaPsychotherapistClinical psychologyPsychiatryMedicineEating disordersInternal medicine

Abstract

fetched live from OpenAlex

Self-stigma in individuals with anorexia nervosa (AN) may affect engagement in intensive treatment. The objective of this study was to test a Model of Self-Stigma to identify the influence of public stigma, internalized stigma, self-esteem, and self-efficacy on recovery attitudes in individuals in inpatient treatment for AN. Using a cross-sectional design, 36 female participants with AN completed questionnaires during the first week of intensive inpatient treatment. Better attitude towards recovery was positively correlated with higher self-esteem and self-efficacy and negatively correlated with greater internalized stigma and perceptions of others devaluing families of individuals with AN. Together, these factors accounted for 63% of the variance in recovery attitudes. Findings demonstrate the adverse effects perceived stigma towards families, self-stigma, and self-esteem have on recovery attitudes in individuals with AN. Clinical interventions are needed to challenge internalized stigma and bolster self-esteem to enhance individuals' recovery efforts.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.009
GPT teacher head0.277
Teacher spread0.268 · 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

Citations20
Published2016
Admission routes2
Has abstractyes

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