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Record W1977516638 · doi:10.1177/000841740006700108

Development of a Tool to Measure Clinical Competence in Occupational Therapy: A Pilot Study?

2000· article· en· W1977516638 on OpenAlexafffundvenueabout
Penny Salvatori, Sue Baptiste, Maureen Ward

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2000
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsMcMaster University
FundersCanadian Occupational Therapy FoundationMcMaster University
KeywordsCompetence (human resources)Occupational therapySupervisorAuditChartPsychologyApplied psychologyMedicineMedical educationNursingSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Clinical competence is generally defined as a combination of knowledge, skill and professional behaviour. It is typically assessed using written tests, direct observation, chart audit, client satisfaction surveys and supervisor ratings. This paper describes the development and evaluation of a chart-stimulated recall (CSR) measure that combines the methods of chart audit and clinician interview to assess the clinical competence of practicing occupational therapists. The CSR tool was developed using the Canadian Guidelines for Client-Centred Practice and taps global domains of competence: use of theory, assessment, program planning, intervention, discharge planning, follow-up, program evaluation, clinical reasoning and professional behaviours. This pilot study involved two independent raters/interviewers who assessed twelve occupational therapy clinicians on two occasions using a random sample of client cases/records on each occasion Results indicate that the CSR tool is not only reliable and valid, but also sufficiently generic to be used in a variety of practice settings as a global measure of on-the-job performance.

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)0.037
metaresearch head score (Gemma)0.051
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.599
GPT teacher head0.563
Teacher spread0.036 · 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

Citations34
Published2000
Admission routes4
Has abstractyes

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