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Record W4410895680 · doi:10.3390/bs15060749

Overcoming Implementation Barriers of Concurrent Treatment for Eating Disorders and Posttraumatic Stress Disorder: Two Novel and Feasible Approaches

2025· article· en· W4410895680 on OpenAlexaff
Kathryn Trottier, Sara Bartel, Aaron Keshen

Bibliographic record

VenueBehavioral Sciences · 2025
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsDalhousie UniversityNova Scotia Health AuthorityToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsPsychoeducationEating disordersIntervention (counseling)Psychological interventionPosttraumatic stressPsychotherapistPsychologyCognitionClinical psychologyExposure therapyPsychiatryCognitive behavioral therapyMedicineAnxiety

Abstract

fetched live from OpenAlex

Eating disorders (EDs) and posttraumatic stress disorder (PTSD) frequently co-occur and share a functional relationship. Evidence suggests benefits of integrated and/or concurrent treatment; however, implementation is hindered by clinician training burden and the challenges of delivering two treatments simultaneously. This paper explores two novel and feasible approaches to addressing ED-PTSD. The first is a clinician-guided cognitive behavioural workbook intervention delivered concurrently with ED treatment. It involves psychoeducation, addresses dissociation, and encourages approach (versus avoidance) practices. The second involves combining Written Exposure Therapy (WET) with ED treatment at both outpatient and day hospital levels of care. Both interventions have a low training burden and are feasible in routine clinical practice, making concurrent approaches available to those who need them.

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)0.011
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0030.003
Open science0.0030.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.131
GPT teacher head0.439
Teacher spread0.307 · 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 designNon-randomized trial
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

Citations0
Published2025
Admission routes1
Has abstractyes

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