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Record W3082663155 · doi:10.1097/nmd.0000000000001160

Does Age Impact the Clinical Presentation of Adult Women Seeking Specialty Eating Disorder Treatment?

2020· article· en· W3082663155 on OpenAlexaff
Christine Henriksen, Corey S. Mackenzie, Patricia Fergusson, Danielle R. B̀ouchard

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2020
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of New BrunswickUniversity of Manitoba
Fundersnot available
KeywordsPsychopathologySpecialtyPresentation (obstetrics)AnxietyQuality of life (healthcare)Eating disordersMaturity (psychological)Clinical psychologyPsychiatryMedicinePsychologyGerontologyDevelopmental psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Recent evidence suggests that eating disorders (EDs) are becoming increasingly common in older women. Previous research examining differences between younger and older women with EDs has been mixed, making it unclear whether older women with EDs represent a distinct group. We sought to determine whether there are age differences in the clinical presentation of women seeking specialty treatment for an ED. We examined the linear relationship between age and clinical constructs among adult women (N = 436) diagnosed with a Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, ED. Across analyses, there was no impact of age on most measures of ED symptoms, comorbid psychopathology, self-esteem, quality of life, and motivation to change. However, older age was associated with fewer interoceptive awareness difficulties, maturity fears, anxiety symptoms, and body image concerns. These findings suggest that the clinical presentation of older ED cases is largely similar, although somewhat less severe than in younger women. The implications of this research for future research and treatment are discussed.

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)0.001
metaresearch head score (Gemma)0.010
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.376
Teacher spread0.342 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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