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Record W2184620519 · doi:10.3138/ptc.2014-29e

Development of the Canadian Physiotherapy Assessment of Clinical Performance: A New Tool to Assess Physiotherapy Students' Performance in Clinical Education

2015· article· en· W2184620519 on OpenAlexaffvenueabout
Brenda Mori, Dina Brooks, Kathleen E. Norman, Jodi Herold, Dorcas Beaton

Bibliographic record

VenuePhysiotherapy Canada · 2015
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsInstitute for Work & HealthUniversity of TorontoQueen's University
Fundersnot available
KeywordsDelphi methodInterviewDelphiMedical educationRating scaleValuation (finance)Cognitive interviewPsychologyPhysical therapyMedicineCognitionComputer sciencePolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

PURPOSE: To develop the first draft of a Canadian tool to assess physiotherapy (PT) students' performance in clinical education (CE). Phase 1: to gain consensus on the items within the new tool, the number and placement of the comment boxes, and the rating scale; Phase 2: to explore the face and content validity of the draft tool. METHODS: Phase 1 used the Delphi method; Phase 2 used cognitive interviewing methods with recent graduates and clinical instructors (CIs) and detailed interviews with clinical education and measurement experts. RESULTS: Consensus was reached on the first draft of the new tool by round 3 of the Delphi process, which was completed by 21 participants. Interviews were completed with 13 CIs, 6 recent graduates, and 7 experts. Recent graduates and CIs were able to interpret the tool accurately, felt they could apply it to a recent CE experience, and provided suggestions to improve the draft. Experts provided salient advice. CONCLUSIONS: The first draft of a new tool to assess PT students in CE, the Canadian Physiotherapy Assessment of Clinical Performance (ACP), was developed and will undergo further development and testing, including national consultation with stakeholders. Data from Phase 2 will contribute to developing an online education module for CIs and students.

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)0.004
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 score0.167
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.221
GPT teacher head0.603
Teacher spread0.383 · 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

Citations26
Published2015
Admission routes3
Has abstractyes

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