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Record W2052211597 · doi:10.1002/eat.10009

Day hospitalization programs for eating disorders: A systematic review of the literature

2002· review· en· W2052211597 on OpenAlexaff
Stephan Zipfel, Deborah L. Reas, Chris Thornton, Marion P. Olmsted, Donald A. Williamson, M. Gerlinghoff, Wolfgang Herzog, Peter Beumont

Bibliographic record

VenueInternational Journal of Eating Disorders · 2002
Typereview
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsToronto General HospitalUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMultidisciplinary approachEating disordersInclusion (mineral)GermanMedicineMEDLINEPsychologyGerontologyFamily medicinePsychiatryPolitical scienceGeography

Abstract

fetched live from OpenAlex

OBJECTIVE: Partial day hospitalization programs for the treatment of eating disorders are increasingly being developed worldwide. METHOD: First, a systematic database search of day hospitalization programs for eating disorders, published in either English or German, was conducted. Programs that provided sufficient information on their program structure were summarized and compared across various dimensions. Second, the responsible program directors were contacted to provide additional information regarding outcome data, current trends, challenges, and future directions of their programs. Third, outcome data from day programs presented at international conferences were included to expand the base of the review. DISCUSSION: Although the programs from different countries and health care environments varied in terms of their purpose and operated within very different health care systems, many similarities were found to exist, including the use of a multidisciplinary staff and reliance on group treatment as the primary means of therapy. Marked differences were noted in terms of inclusion criteria and intensity of care.

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)0.004
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0060.010
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.027
GPT teacher head0.362
Teacher spread0.335 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations156
Published2002
Admission routes1
Has abstractyes

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