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Record W2888734452 · doi:10.5014/ajot.2018.025924

Occupational Performance Issues of Adults Seeking Bariatric Surgery for Obesity

2018· article· en· W2888734452 on OpenAlexaffabout
Karen S. Barclay, Susan Forwell

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychosocialMental healthMedicineAnxietyDepression (economics)Occupational therapyPopulationObesityClinical psychologyPhysical therapyPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: We sought to describe the occupational performance issues of a sample of bariatric surgery candidates and to explore the relationships among occupational performance, satisfaction with performance, demographic characteristics, and mental health factors. METHOD: We reviewed the health records of 241 bariatric surgery candidates and analyzed their scores on the Canadian Occupational Performance Measure (COPM) and standardized mental health questionnaires. RESULTS: Exercise and eating behavior were the most common occupational performance issues. Cognitive and affective issues were reported more frequently than physical issues. Occupational performance and satisfaction correlated negatively with anxiety and depression and positively with self-esteem. Self-esteem contributed 27% of the variance in occupational performance. CONCLUSION: COPM scores revealed a wide range of occupational performance issues and significant associations with mental health factors, supporting a psychosocial approach to occupational therapy with this population. Routine mental health screening can help ensure that mental health factors are adequately addressed.

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)0.000
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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.148
GPT teacher head0.485
Teacher spread0.337 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

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