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Record W1977965137 · doi:10.1177/030802260506800506

The Canadian Occupational Performance Measure: Does it Address the Cultural Occupations of Ethnic Minorities?

2005· article· en· W1977965137 on OpenAlexaboutno aff
Sarah A. Fisher

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2005
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
FundersUniversity of OxfordElizabeth Casson TrustOxford Brookes University
KeywordsEthnic groupOccupational therapySample (material)PsychologyTelephone interviewCulturally sensitiveCultural diversityFocus groupClinical psychologyMedicineSocial psychologySociologySocial sciencePsychiatryAnthropology

Abstract

fetched live from OpenAlex

This study is the first to examine the cultural sensitivity of the Canadian Occupational Performance Measure (COPM), as reported by occupational therapists who have used it with people from an ethnic minority background. The research was conducted as part of a Master's degree, held in Boston, USA, and funded by the Elizabeth Casson Trust, UK. Twenty-five practising occupational therapists from North America answered an emailed survey and, from this sample, three were interviewed by telephone. The findings showed that the practitioners perceived the COPM as able to address the cultural occupations of ethnic minorities; however, they felt that including examples of cultural occupations and children's occupations and modifying the rating scales would improve the tool. Two of the three therapists interviewed worked with children and, because of this, there was a focus on children's occupations that was only revealed in the second part of the research. The nature of the tool, its ability to focus on occupation and the therapist's interview skills were identified as the reasons that the COPM is effective in addressing the cultural occupations of ethnic minorities. Additional research is needed using a larger sample to validate these findings.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.479
Teacher spread0.258 · 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

Citations5
Published2005
Admission routes1
Has abstractyes

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