A scoping review of exercise referral schemes involving qualified exercise professionals in primary health care
Bibliographic record
Abstract
Qualified exercise professionals (QEPs) have the training, knowledge, and scope of practice to effectively provide physical activity counselling, prescribe exercise, and deliver exercise programming to patients with or without chronic diseases. Healthcare providers identify an interest in referring patients to QEPs; however, the impact of exercise referral schemes (ERS) involving QEPs on patients’ physical health is unclear. A scoping review regarding the available evidence of ERS involving healthcare provider referrals to QEPs was performed. A literature search was conducted in 6 databases (initially: n = 6011 articles), yielding n = 23 articles examining QEP delivered physical activity counselling (n = 7), QEP supervised exercise training (n = 4), or some combination (n = 12). Although studies were heterogeneous in methods, procedures, and populations, ERSs increased patients’ subjective physical activity levels. Few studies incorporated objective physical activity measures (n = 5/23), and almost half measured aerobic fitness (n = 11/23). ERS involving a QEP that includes activity counselling and/or exercise programming/training report favourable impacts on patients’ subjectively measured physical activity and objectively measured aerobic fitness. Based on the existing literature on the topic, this scoping review provides recommendations for designing and evaluating ERS with QEPs that include: objective measures, long-term follow-up, QEP qualifications, and the cost-effectiveness of ERS. Novelty: ERS involving QEPs report increased patients’ perceived physical activity level and may improve patients’ cardiorespiratory fitness. Promoting the collaboration of QEPs with other healthcare providers can enhance patients’ physical fitness and health. This scoping review provides recommendations for the design and evaluation of ERS involving QEPs.
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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 | 0.023 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".