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Record W3085980178 · doi:10.1177/0008417420953229

Perceived Occupational Performance in Youth with Eating Disorders: Treatment-Related Changes

2020· article· en· W3085980178 on OpenAlexfundvenueaboutno aff
Jennifer S. Coelho, Avarna Fernandes, Janet Suen, Adi Keidar, Jadine Cairns

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2020
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsUnderweightOccupational therapyEating disordersAutonomyContext (archaeology)Clinical psychologyPsychologyMedicinePsychiatryBody mass indexOverweight

Abstract

fetched live from OpenAlex

BACKGROUND.: This study examined changes in performance and satisfaction with self-identified occupational performance goals during a specialized day treatment admission in children and adolescents with eating disorders. Weight-related outcomes for underweight youth were also examined. METHODS.: A total of 63 youth participated in the study, with admission and discharge data on ratings of self-identified occupational performance goals (measured with the Canadian Occupational Performance Measure) available for 42 participants. FINDINGS.: Significant improvements were found in ratings of satisfaction and performance with self-identified goals over the course of treatment. The program was also effective in supporting weight restoration for underweight youth, with a large effect size observed. IMPLICATIONS.: A symptom-focused day treatment program for paediatric eating disorders led to improvements in perceived occupational performance. Collaborating with youth to develop self-identified goals in the context of eating disorders treatment can foster autonomy and potentially improve treatment engagement.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.114
GPT teacher head0.344
Teacher spread0.231 · 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

Citations6
Published2020
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Occupational TherapySame topicEating Disorders and BehaviorsFrench-language works237,207