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Record W7123309429 · doi:10.17605/osf.io/y37kc

Clinician Perspectives and Experiences about Training in Family-Based Treatment for Eating Disorders

2025· other· W7123309429 on OpenAlexaffabout
Jennifer Coelho, Daria Hammond, Josie Geller, Julia Kaufmann, Patricia Obee, Tayla Bain, Kim Williams

Bibliographic record

VenueOpen Science Framework · 2025
Typeother
Language
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEating disordersTraining (meteorology)Mental healthPrimary careMEDLINEPublic health

Abstract

fetched live from OpenAlex

Eating disorders are a serious and deadly illness amongst young people. Canadian clinical care guidelines strongly recommend family-based treatment (FBT) for pediatric eating disorders; however, FBT is not widely available in eating disorders programs across the province. The Eating Disorders Program at BC Children's Hospital has developed a Provincial Eating Disorders Training Hub that provides a 2-day training workshop and consultation in FBT at no cost to mental health clinicians in BC and Yukon who support youth with pediatric eating disorders. The goal of this study is to supplement the existing REB-approved survey portion of this project (separately registered at osf.io/8ax4y). Clinicians who have taken part in the survey component of the study will be invited to participate in a follow-up interview, in which we will ask about their perspectives and experiences with the Training Hub. Our primary goals of the interview will be to understand reach, effectiveness, adoption, implementation, and maintenance/sustainability of the Training Hub, their experiences with the Training Hub, and and the contextual factors associated with the outcomes of the Training Hub.

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)0.020
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.046
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0100.006
Scholarly communication0.0070.004
Open science0.0020.006
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.063
GPT teacher head0.420
Teacher spread0.357 · 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 designQualitative
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 routes2
Has abstractyes

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