Physiotherapists’ Perspectives on Type 2 Diabetes Management and as a Primary Condition for Referral to Physiotherapy Services: A Qualitative Descriptive Study
Bibliographic record
Abstract
Purpose: We explored the current and potential role of physiotherapists in the management of people with type 2 diabetes (T2D) and T2D as a primary condition for physiotherapy referral. Methods: We conducted a qualitative descriptive study. Participants were physiotherapists practicing in community and outpatient settings across Canada. One-on-one telephone interviews were completed to explore provision of physiotherapy care for people with T2D, including current practices and readiness of physiotherapists to provide direct care. We employed thematic analysis for generation of themes from interviews. Results: We interviewed 21 participants from eight provinces and territories. Three themes were generated from the data: current approach to T2D management; challenges for physiotherapy integration; and merits of physiotherapy and needed evolution. Participants described that physiotherapists are not part of the healthcare team for T2D management. There is a gap in medical management of T2D that physiotherapy would fill, that is, education and prescription for exercise participation. Conclusions: Our findings support a gap in the management of T2D in Canadian healthcare, particularly in reference to physiotherapy. Further, our findings support the need for greater inclusion of physiotherapists for lifestyle counseling with an emphasis on physical activity and exercise for patients at risk of and with T2D to maximize health and improve/maintain function. Studies focusing on accessibility and funding of physiotherapy services are needed to validate these findings.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".