Healthcare Professionals’ Insights on the Integration of Kinesiologists into Ontario’s Health System
Bibliographic record
Abstract
ABSTRACT Introduction/Purpose Kinesiologists are well suited to work collaboratively or independently within the health system to improve patient/client care and well-being. This cross-sectional survey explored perceptions of the integration of registered kinesiologists (RKins) into the health system in Ontario. Methods RKins ( n = 202) and other health professionals (OHP; n = 337), including physicians, physiotherapists, nurse practitioners, etc., participated in an online survey. Results RKins reported working in diverse practice environments, and more than half reported receiving patients/clients through referrals. Of the OHP, 37.7% had ongoing professional interactions with RKins and 86.7% reported high satisfaction with these interactions; 32.6% of OHP reported referring patients/clients to RKins, primarily for exercise prescription (86.0%), treatment of clinical conditions (48.8%), and patient education (46.5%). Perceived barriers to referral included lack of awareness of the RKins’ scope of practice (81.0%), inadequate funding for services (67.1%), and low confidence in the clinical competency of RKins (61.8%). Conclusions RKins are experts in exercise-based interventions to prevent, treat, and manage many chronic lifestyle-related diseases. Initiatives to increase awareness of the RKins’ scope of practice, clinical competency, and standards of practice and to increase funding for RKin services are important next steps.
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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 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".