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Record W4400598985 · doi:10.1002/eat.24263

Outcomes of a Virtual Day Treatment Program for Adults With Eating Disorders—Comparison With In‐Person Day Treatment

2024· article· en· W4400598985 on OpenAlexaff
Lea Thaler, Linda Booij, Annie St‐Hilaire, Chloé Paquin Hodge, Nesrine Mesli, Hope Burko, Viveca Lee, Stephanie Oliverio, Mimi Israël, Howard Steiger

Bibliographic record

VenueInternational Journal of Eating Disorders · 2024
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsEating disordersModality (human–computer interaction)Treatment modalityMedicinePsychologyClinical psychologyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Previous studies have indicated that virtual treatments for eating disorders (EDs) are roughly as effective as are in-person treatments; the present nonrandomized study aimed to expand on the current body of evidence by comparing outcomes from a virtual day treatment program with those of an in-person program in an adult ED sample. METHOD: Participants were 109 patients who completed at least 60% of day treatment sessions (n = 55 in-person and n = 54 virtual). Outcome measures included ED and comorbid symptoms, and motivation. RESULTS: Linear mixed models showed that global EDE-Q scores decreased during treatment (AIC = 376.396, F = 10.94, p = 0.002), irrespective of treatment modality (p = 0.186). BMI significantly increased over time (AIC = 389.029, F = 27.97, p < 0.001), with no effect of treatment modality (p = 0.779). DISCUSSION: Our findings suggest that the virtual delivery of day treatments produces comparable outcomes to those obtained using in-person formats, and that virtual formats may represent a pragmatic treatment option, especially in situations in which access to in-person care is limited.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.359
Teacher spread0.338 · 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 teacher head, not a consensus.

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

Citations6
Published2024
Admission routes1
Has abstractyes

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