Exploring the role of healthcare partners in referrals to a community-based exercise program with a healthcare-community partnership designed for people with balance and mobility limitations
Bibliographic record
Abstract
Purpose To explore how healthcare partners in community-based exercise programs for people with balance and mobility limitations perceive and enact referral in the context of their role.Materials and methods We conducted a descriptive, qualitative study involving semi-structured interviews and reflexive thematic analysis.Results Twelve healthcare partners from the Together In Movement and Exercise (TIMETM) program completed interviews. Seven (58%) participants were clinicians and 5 (42%) held non-clinical roles. The most common professional background of participants was physical therapy (n = 9, 75%). Clinicians made direct referrals while non-clinical participants facilitated referral by promoting the program. The main theme was healthcare partners perceive their role in referrals as secondary to their role as educators and trainers. Subthemes were: (1) healthcare partners fulfill educator and trainer roles when conducting formal training of instructors, educating instructors during program visits, and fielding questions; (2) almost all healthcare partners facilitate referral by sharing program information formally and informally; and (3) healthcare partners in clinical practice make direct referrals depending on the clientele.Conclusions Healthcare partners perceive their roles as educators and trainers as taking precedence over their role in referrals. Findings can be used to guide selection and training of healthcare partners, design of clinical education programs, and research on competencies.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".