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Record W4414844255 · doi:10.1080/10640266.2025.2565470

Early intervention for caregivers of youth with restrictive eating disorders (CARE Skills Group): feasibility, outcomes and opportunities for spread and scale

2025· article· en· W4414844255 on OpenAlexafffundabout
Jennifer S. Coelho, Nicole Obeid, Andrea S. Wallace, Pei‐Yoong Lam, Wendy Spettigue, Madeline J. Gertler, Niana Lavallée, Justina Melkis, Leanna Isserlin, Noah M. P. Spector, Elizabeth Quon, Catherine Bouchard, Tayla Bain, Kim Williams, Mark L. Norris

Bibliographic record

VenueEating Disorders · 2025
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of OttawaBC Children's HospitalChildren's Hospital of Eastern OntarioUniversity of British Columbia
FundersCanadian Institutes of Health ResearchProvincial Health Services AuthorityMichael Smith Health Research BC
KeywordsIntervention (counseling)Eating disordersScale (ratio)Healthy eatingSocial skillsPsychological intervention

Abstract

fetched live from OpenAlex

Early intervention is key to improving prognosis for youth with eating disorders (EDs). Caregiver groups may be an effective way to intervene early in the treatment of youth with EDs, in conjunction with speciality medical care. A 12-session online caregiver skills group (CARE Skills Group) was designed and offered to caregivers of youth with recent onset, newly diagnosed restrictive EDs at two different Canadian sites. The CARE Skills group integrated family-based treatment (FBT) principles and was led by experienced ED clinicians. The group was feasible, with some preliminary evidence that youth whose caregivers participated in the CARE Skills Group benefited in terms of weight restoration. The CARE Skills Group model represents a brief, and replicable early intervention model that has potential utility for implementation in community-based ED settings.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.318
Teacher spread0.291 · 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 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

Citations0
Published2025
Admission routes3
Has abstractyes

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