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Record W2000114703 · doi:10.1177/0008417414561857

Assessment practices of Canadian occupational therapists working with adults with mental disorders

2015· article· en· W2000114703 on OpenAlexfundvenueaboutno aff
Suzanne Rouleau, Karyne Dion, Nicol Korner‐Bitensky

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2015
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
FundersMental Health CommissionMcGill University
KeywordsOccupational therapyStandardized testBest practiceSchizophrenia (object-oriented programming)MedicinePsychologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Little is known about assessment practices of occupational therapists working with adults with mental disorders. PURPOSE: This study investigates the assessment practices of occupational therapists working with clients experiencing symptoms of schizophrenia or major depressive disorder. METHOD: We conducted a national survey of assessment practices using case vignettes of hypothetical clients. FINDINGS: From 343 vignettes completed by 286 respondents, 68.4% included the use of one or more standardized measures during treatment. Measures were rarely repeated. Results showed that the Canadian Occupational Performance Measure was the most frequently used, suggesting a focus on assessing global functioning, while the Assessment of Motor and Process Skills was listed as the most desired assessment tool. Implementing nonstandardized assessments was common. IMPLICATIONS: Despite wide variations in occupational therapists' assessment practices, the use of standardized assessments is prevalent. The low rate of repeated measures (0% to 25.9%) suggests a need to better monitor changes and treatment outcomes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.347
GPT teacher head0.509
Teacher spread0.161 · 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 teacher head, not a consensus.

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

Citations13
Published2015
Admission routes3
Has abstractyes

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