MétaCan
Menu
Back to cohort
Record W2811010158 · doi:10.1080/09593985.2018.1491080

Teaching professionalism: some features in Canadian physiotherapy programs

2018· article· en· W2811010158 on OpenAlexaffabout
Sue Murphy, Laura Whitehouse, Betsabeh Parsa

Bibliographic record

VenuePhysiotherapy Theory and Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhysical therapyMedicinePhysical medicine and rehabilitationMedical educationPsychology

Abstract

fetched live from OpenAlex

The role of "professionalism" is core to many health professions including Physical Therapy (PT), and the development of competence in professionalism is a key requirement of entry-to-practice PT programs. However, the actual curriculum, teaching methodologies, and evaluation methods currently used to develop professional competence during PT training programs in Canada are unclear. This study explored current teaching practices, evaluation and curricular content related to professionalism in Canadian entry-to-practice PT programs. Results showed that teaching practices related to professionalism were not necessarily congruent with methods promulgated by educational theory and relied heavily on lecture, while more appropriate strategies such as simulation and role play were under-utilized. The numbers of different teaching methods utilized for specific aspects of professionalism were variable. Emphasis on different curricular areas related to professionalism also varied: communication was given the most emphasis while change management was under-represented. It is posited that teaching methods related to professionalism could be improved and curricular content and emphasis should also be reconsidered.

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)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.577

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0070.002
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.425
Teacher spread0.407 · 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 designQualitative
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

Citations13
Published2018
Admission routes2
Has abstractyes

Explore more

Same venuePhysiotherapy Theory and PracticeSame topicInnovations in Medical EducationFrench-language works237,207