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Record W2150961872 · doi:10.3109/09638237.2011.638000

Evaluating outcomes of therapies offered by occupational therapists in adult mental health

2012· article· en· W2150961872 on OpenAlexaboutno aff
Rajkumar S P Samsonraj, Michael Loughran, Jenny Secker

Bibliographic record

VenueJournal of Mental Health · 2012
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyGlobal Assessment of FunctioningPsychological interventionMental healthPhysical therapyClinical psychologyPsychologyMedicinePsychiatryCognition

Abstract

fetched live from OpenAlex

BACKGROUND: Attitudes towards the use of outcome measures by professionals working in mental health have been shown to be variable. Occupational therapists appear to have difficulty specifying goals and measuring the outcomes of interventions. AIMS: To measure the outcomes of therapies offered by occupational therapists and to assess concurrent validity of the Van du Toit Model of Creative Ability (VdT MoCA) assessment. METHOD: The Global Assessment of Functioning (GAF), VdT MoCA assessment and Canadian Occupational Performance Measure (COPM) were used. Changes in mean scores on the measures were assessed using appropriate tests. Correlations between measures were assessed using Spearman's non-parametric test. RESULTS: Mean post-therapy scores were significantly higher than pre-therapy scores on all three measures. VdT MoCA assessment scores pre- and post-therapy were highly correlated with GAF scores. The COPM outcome scores were uncorrelated with VdT MoCA assessment and GAF scores. CONCLUSIONS: The results offer a promising indication that occupational therapy interventions may increase functioning and thus aid clients' recovery. The VdT MoCA assessment is promising as a measure of improvement in functioning. Further research is needed to confirm these results and to further explore issues around occupational therapists' use of outcome measures.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.052
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.221
GPT teacher head0.594
Teacher spread0.373 · 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.

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
Published2012
Admission routes1
Has abstractyes

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