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Record W2918629906 · doi:10.3138/ptc.2018-14.e

Build Insight, Change Thinking, Inform Action: Considerations for Increasing the Number of Indigenous Students in Canadian Physical Therapy Programmes

2019· article· en· W2918629906 on OpenAlexaffvenueabout
Jason Cox, Vandna Kapil, Aindrea N McHugh, Jaya Sam, Katie Gasparelli, Stephanie Nixon

Bibliographic record

VenuePhysiotherapy Canada · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsIndigenousAction (physics)Medical educationMedicine

Abstract

fetched live from OpenAlex

Purpose: We explored the perspectives of experts on increasing the recruitment of Indigenous students into Canadian physical therapy (PT) programmes. Methods: For this qualitative interpretivist study, we conducted in-depth, semi-structured interviews with individuals with expertise in encouraging Indigenous students to pursue higher education, recruiting them into PT programmes, or both. Data were organized using NVivo and analyzed using the DEPICT method, which included inductive and deductive coding to develop broader themes. Results: Analyzing the participants’ perspectives revealed three themes, which could be layered sequentially, so that each informed the next: (1) building insight by increasing awareness of structural forces and barriers; (2) changing thinking, using a paradigm shift, from the dominant Eurocentric orientation to a view that respects the sovereignty and self-determination of Indigenous peoples; and (3) informing action by recommending practical strategies to facilitate the recruitment of Indigenous students into Canadian PT programmes. Conclusions: This is the first study to provide evidence of the structural considerations, barriers to, and facilitators of increasing the recruitment of Indigenous students into Canadian PT programmes.

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)0.000
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 score0.160
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.028
GPT teacher head0.373
Teacher spread0.345 · 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

Citations6
Published2019
Admission routes3
Has abstractyes

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