MétaCan
Menu
Back to cohort
Record W6925068629 · doi:10.17605/osf.io/r76wm

Entry-To-Practice Competency Expectations for Health Justice in Canadian Physiotherapy Curricula: A Scoping Review

2022· other· en· W6925068629 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationEconomic JusticeCurriculumContext (archaeology)Inclusion (mineral)Health equityHealth professionalsHealth education

Abstract

fetched live from OpenAlex

In Canada, physiotherapists are expected to possess and demonstrate a number of essential competencies upon entry-to-practice. Despite the inclusion of concepts related to health justice, inclusivity, and cultural awareness into the Physiotherapy Accreditation Standards for Canadian physiotherapy programs, health justice is an emerging topic among Canadian physiotherapy programs and current curricula may be lacking explicit health justice frameworks. Due to the importance of health justice in the context of Canadian physiotherapy education and practice, further research is required to examine how health justice has been integrated into entry-level curricula to date and to identify opportunities for improvement. Thus, the primary objective of this paper is to examine existing Canadian entry-level competencies for physiotherapy related to health justice. The secondary objective is to examine the themes of current recommendations and written resources related to health justice for entry-level physiotherapists in countries other than Canada that have already taken steps to incorporate principles of health justice into their health program curricula. The final objective is to evaluate how existing entry-level competencies related to health justice in Canadian physiotherapy practice compare to those of other countries.

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)0.041
metaresearch head score (Gemma)0.145
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score0.666

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.145
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0190.025
Science and technology studies0.0040.004
Scholarly communication0.0090.004
Open science0.0050.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.035
GPT teacher head0.476
Teacher spread0.441 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueOpen Science FrameworkFrench-language works237,207