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Record W2987251156 · doi:10.3138/ptc-2019-0013

Teaching and Assessing Advocacy in Canadian Physiotherapy Programmes

2019· article· en· W2987251156 on OpenAlexaffvenueabout
Jennifer Bessette, Mélissa Généreux, Aliki Thomas, Chantal Camden

Bibliographic record

VenuePhysiotherapy Canada · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversité de SherbrookeMcGill UniversitySanté Mentale au Québec
Fundersnot available
KeywordsCurriculumCLARITYMedical educationThematic analysisPedagogyPsychologyMedicineNursingSociologyQualitative research

Abstract

fetched live from OpenAlex

Purpose: Advocacy is an essential component of physiotherapy (PT) practice. As a result, universities are expected to teach and assess advocacy-related competencies in their curriculum. The purpose of this study was to explore current educational practices for teaching and assessing advocacy in Canadian PT programmes, barriers to teaching and assessment, and solutions for enhancing educational practices. Method: We used a convergent parallel mixed-methods design. Teachers and coordinators from Canadian PT programmes completed an online survey, and clinical supervisors participated in telephone interviews. We performed descriptive statistics and thematic analyses. Results: Advocacy-related competencies were widely covered in the academic curriculum of the 13 PT programmes represented by our participants, but not all competencies were assessed equally. Barriers to teaching and assessment of advocacy included the lack of role clarity, relevant teaching and assessment strategies, time, and opportunity to practice the role in the curriculum. Students’ personal experience and motivation also had an impact. Conclusion: Essential steps toward enhancing educational practices are to clarify the definition of advocacy, guide PT educators in explicitly and concretely teaching and assessing advocacy, develop a staged approach to covering advocacy throughout the curriculum, and normalize advocacy as a PT domain.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.997

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.465
Teacher spread0.434 · 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

Citations15
Published2019
Admission routes3
Has abstractyes

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