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Record W2083957987 · doi:10.3138/ptc.2013-26

Assessing Physical Therapy Students' Performance during Clinical Practice

2014· article· en· W2083957987 on OpenAlexaffvenueabout
Sue Murphy, Megan Dalton, Diana Dawes

Bibliographic record

VenuePhysiotherapy Canada · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGrading (engineering)Clinical PracticeMedical educationPhysical therapyMedicinePsychology

Abstract

fetched live from OpenAlex

PURPOSE: To assess the feasibility and acceptability of using the Assessment of Physiotherapy Practice (APP) instrument to assess physiotherapy students' clinical competencies. METHODS: A convenience sample of clinical educators (CEs) and students from the University of British Columbia (UBC) in Vancouver, Canada, completed the instrument currently in use, the Physical Therapist Clinical Performance Instrument (PT-CPI), and the APP. A cross-sectional survey of CEs and physiotherapy students was conducted from 2011 to 2012; the survey included questions worded to elicit opinions about the two instruments when used in the clinical environment with students at different stages of training. Questions addressed various aspects of the instruments, including ease of use, provision of feedback, and completion time. RESULTS: Data were analyzed from 63 CEs from a variety of settings; sufficient data were recorded on 71 student PT-CPI and APP forms. A grading comparison between the PT-CPI and the APP demonstrated equivalence of entry-to-practice standard. Mean completion time was 80 (SD 53) minutes for the PT-CPI and 23 (13) minutes for the APP; mean time difference was 57 (95% CI, 39-75). Students would prefer (82%) that the APP be used to provide feedback and assess their performance on clinical placements. CONCLUSIONS: It is feasible and acceptable to use the APP to assess physiotherapy students' clinical competencies at the University of British Columbia.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.097
GPT teacher head0.571
Teacher spread0.474 · 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

Citations22
Published2014
Admission routes3
Has abstractyes

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