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The Effectiveness of Occupational Performance Outcome Measures within Mental Health Practice

2011· article· en· W2332829600 on OpenAlexaboutno aff
Kate Fuller

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2011
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsGoal Attainment ScalingOccupational therapyMental healthScale (ratio)PsychologyOutcome (game theory)MedicineReliability (semiconductor)Evidence-based practiceIntervention (counseling)NursingClinical psychologyPsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

Introduction: The routine use of outcome measures is essential to the maintained delivery of quality care and the continued commissioning of mental health occupational therapy services. Occupational therapists are required to demonstrate that intervention is successful in an evidence-based, valid and reliable way. Therefore, this critical review aims to address the issue of choosing an appropriate occupational performance outcome measure for use within mental health services. Method: Evidence was critically appraised for the effectiveness of the Assessment of Communication and Interaction Skills (ACIS), Occupational Therapy Task Observation Scale (OTTOS), Canadian Occupational Performance Measure (COPM) and Goal Attainment Scaling (GAS), all recommended for use by occupational therapists within mental health practice. Findings and discussion: The review identifies that there are a limited number of clinically based studies evidencing the validity and reliability of occupational performance outcome measures. It also identifies a paucity of literature concerning service user experience of outcome measures, bringing into question how client centred and meaningful these tools are.

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)0.018
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.303
GPT teacher head0.514
Teacher spread0.210 · 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

Citations17
Published2011
Admission routes1
Has abstractyes

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