Referrals from Health Care Professionals to Community-Based Exercise Programmes Targeting People with Balance and Mobility Limitations: An Interviewer-Administered Survey
Bibliographic record
Abstract
Purpose: To describe programme representatives’ perceptions of the: (1) type and work setting of health care professionals who refer to community-based exercise programmes with health care-community partnerships (CBEP-HCPs) by community size; (2) nature, frequency, and utility of strategies used to promote referral from health care professionals to CBEP-HCPs; and (3) facilitators and barriers to CBEP-HCP promotion. Method: We invited individuals involved with the Together in Movement and Exercise (TIME™) programme in 48 centres to participate in a cross-sectional survey. TIME™ is a group, task-oriented CBEP-HCP taught by fitness instructors; health care partners promote referrals. Data were summarized using frequencies and percentages. Content analysis was used for open-ended questions. Results: Twenty-three representatives of 27 TIME™ programmes (56% response rate) participated. Out of 26 health care partners identified, 69% were physical therapists. We report the most common findings: programmes received referrals from physical therapists (16, 70%); programmes gave health care partners promotional materials (e.g., flyers) to facilitate referrals ( n = 17, 63%); strong relationships with health care partners facilitated promotion ( n = 18, 78%); and representatives perceived their lack of credibility challenged promotion ( n = 3, 23%). Conclusions: Physical therapists were the most common referral source. Health Care partners were instrumental in programme promotion. Future research is needed to leverage referrals from physical therapists in settings other than hospitals and to better understand the role of health care partners in CBEP-HCPs.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".