Using a Socioecological Approach to Explore the Integration of Exercise Physiologists into Primary Healthcare Teams
Bibliographic record
Abstract
Objective: This paper explores the experiences of Clinical Exercise Physiologists (CEPs) and physicians participating in a pilot exercise referral program in Atlantic Canada. Additionally, the study aims to identify the barriers and facilitators that could impact broader integration of exercise professionals into primary healthcare teams. Design: Semi-structured individual interviews were conducted with CEPs and physicians involved in the exercise referral program to explore their experiences with the exercise referral program. The Socio-Ecological Model was used to draft a list of interview topics for discussion. Interviews were audio-recorded, transcribed verbatim, and analyzed using Lichtman’s three Cs approach to qualitative analysis. Setting: Two urban family medicine clinics associated with an Atlantic Canadian university. Participants: Four CEPs and five family medicine physicians who participated in the exercise referral program. Results: Four main themes emerged from data generation: (1) the importance of CEP-led advocacy for exercise referral in healthcare, (2) gaps in training and regulation of CEPs, (3) the unclear role for exercise professionals within healthcare, and (4) policy and organizational changes required to improve exercise referral. Conclusion: Based on our results, to improve exercise counselling services, efforts should be made to improve the ability of CEPs to advocate for their role on healthcare teams, address issues related to CEP training and regulation in Canada, create a more clearly defined role for exercise professionals within healthcare, and improve exercise referral billing and coverage. To our knowledge, this is the first study to qualitatively investigate the integration of CEPs into primary healthcare teams in Canada and could help guide efforts to expand multidisciplinary healthcare moving forward.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".