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Record W2157300947 · doi:10.3138/ptc.2015-35e

Canadian Physiotherapy Assessment of Clinical Performance: Face and Content Validity

2015· article· en· W2157300947 on OpenAlexaffvenueabout
Brenda Mori, Kathleen E. Norman, Dina Brooks, Jodi Herold, Dorcas Beaton

Bibliographic record

VenuePhysiotherapy Canada · 2015
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of TorontoQueen's UniversityInstitute for Work & Health
Fundersnot available
KeywordsRating scaleContent validityPsychologyScale (ratio)Face validityDemographicsPhysical therapyMedical educationPsychometricsMedicineClinical psychologyGeographyCartographySociology

Abstract

fetched live from OpenAlex

PURPOSE: To investigate face and content validity of a draft measure to be used across Canada to assess physiotherapy students' performance in clinical education, through broad consultation with physiotherapy clinical instructors (CIs) across Canada. METHODS: An online survey was used to collect input on the draft measure. In addition to demographics, the questionnaire included questions on the preferred rating scale, the items within the measure that should have their own rating scale, and general impressions. RESULTS: Among the 259 CIs who completed the survey, a discrete rating scale with six anchors and 10 boxes or a continuous-line rating scale with six anchors was preferred. Respondents favoured using one rating scale for each key competency in the Expert role but considered a single rating scale sufficient for assessing the Scholarly Practitioner role. CIs agreed that the proposed measure would allow them to assess a student who was performing poorly or very well. The name Canadian Physiotherapy Assessment of Clinical Performance (ACP) received the most votes in the questionnaire. CONCLUSIONS: CIs' collective preferences on the design, organization, and naming of the measure they will use in evaluating students are reflected in the second draft of the ACP.

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)0.001
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 score0.130
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.426
GPT teacher head0.572
Teacher spread0.146 · 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

Citations16
Published2015
Admission routes3
Has abstractyes

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