MétaCan
Menu
← Back to cohort
Record W98651386

The canadian eating disorder program survey - exploring intensive treatment programs for youth with eating disorders.

2013· article· en· W98651386 on OpenAlexaffabout
Mark L. Norris, Melanie Strike, Leora Pinhas, Rebecca Gomez, April Elliott, Patricia Ferguson, Joanne Gusella

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsStaffingModalitiesEating disordersTreatment modalityDiversity (politics)PsychologyBaseline (sea)MedicineNursingPsychiatryPolitical scienceSociology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore and describe philosophies and characteristics of intensive eating disorder (ED) treatment programs based in tertiary care institutions across Canada. METHOD: A ninety-item survey examining ED services for adolescents was developed, piloted, and completed by 11 programs across Canada. Information pertaining to program characteristics and components, governance, staffing, referrals, assessments, therapeutic modalities in place, nutritional practices, and treatment protocols were collected. RESULTS: The results highlight the diversity of programming available but also the lack of a unified approach to intensive eating disorder treatment in youth. CONCLUSIONS: This report provides important baseline data that offers a framework that programs can use to come together to establish assessment and treatment protocols as well as a process for outcome evaluation. Continued collaboration will be essential moving forward to ensure Canadian youth, regardless of geographic location, receive the necessary treatment required to attain and sustain recovery.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.110
GPT teacher head0.290
Teacher spread0.180 · 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

Citations8
Published2013
Admission routes2
Has abstractyes

Explore more

Same venuePubMed→Same topicEating Disorders and Behaviors→French-language works237,207→