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Record W1898645104 · doi:10.1002/eat.22246

Mandating weekly weight gain in a day treatment program for eating disorders

2014· article· en· W1898645104 on OpenAlexaff
Lea Thaler, Samantha Wilson, Jennifer S. Coelho, Juliana Mazanek Antunes, Mimi Israël, Howard Steiger

Bibliographic record

VenueInternational Journal of Eating Disorders · 2014
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsBC Children's HospitalUniversité de MontréalMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsWeight gainProtocol (science)Eating disordersBody mass indexWeight lossPsychologyMedicineBody weightPsychiatryInternal medicineObesity

Abstract

fetched live from OpenAlex

OBJECTIVE: We examined differences in treatment outcome associated with a "self-directed"- versus "external-incentive"-based day treatment protocol for individuals with eating disorders (EDs) with below normal body mass index. The self-directed protocol recommended a weekly 500 g weight gain, but allowed participants freedom to gain weight at their own rate; the "external-incentive" protocol required a minimum weight gain of 500 g/week as a requirement for staying in the program. METHOD: Weight outcome was available for 49 individuals who were treated with the recommended weight gain protocol, and 40 individuals who were treated with the required weight gain protocol. RESULTS: Post-treatment weight was significantly higher in patients treated in the required weight gain protocol compared to those treated in the recommended weight gain protocol. All patients, regardless of which program they completed, showed improvements in the measures of eating-disorder and comorbid-psychiatric symptoms. DISCUSSION: This study demonstrated the benefits of an alteration in protocol that required no additional financial or human resources. Further studies are needed to validate the effectiveness of various day treatment protocol parameters.

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)0.001
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 score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.348
Teacher spread0.333 · 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
Published2014
Admission routes1
Has abstractyes

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