Educators’ Perspectives on the Teaching and Learning of Type 2 Diabetes Content in Physiotherapy Programmes across Canada
Bibliographic record
Abstract
Purpose: This qualitative descriptive study researched educators' perspectives of type 2 diabetes (T2D) Teaching and learning, in physiotherapy (PT) programmes across Canada. Methods: Faculty members and clinical instructors from the 15 PT programmes in Canada were contacted. Online surveys collected data on the educators' professional background and perspectives on T2D in the PT curriculum. One-on-one telephone interviews were conducted and thematic analysis was used to generate themes and codes from the interview transcripts. Results: Ten educators from 10 universities completed the survey. Seven of the 10 educators also participated in a telephone interview. Survey responses revealed that T2D content is taught predominantly through case studies and lectures. Of the 10 respondents, six reported that the curriculum does not devote adequate time to T2D content, and nine reported they "strongly agree" or "agree" that T2D is an essential component of the PT curriculum. The interviews revealed that T2D content varies across PT programmes. The educators agreed that T2D is a common condition seen in practice, there is a role for PT intervention, and T2D content is limited by classroom time. Conclusions: Educators noted challenges integrating more T2D content in the curriculum and said that PT clinical contributions for people living with T2D are underutilized. Additional evidence-informed rationale is needed to explore optimal integration of T2D content in PT programmes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".