Evidence-based risk recommendations for best practices in the training of qualified exercise professionals working with clinical populations<sup>1</sup>This paper is one of a selection of papers published in this Special Issue, entitled Evidence-based risk assessment and recommendations for physical activity clearance, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
This systematic review examines critically "best practices" in the training of qualified exercise professionals. Particular attention is given to the core competencies and educational requirements needed for working with clinical populations. Relevant information was obtained by a systematic search of 6 electronic databases, cross-referencing, and through the authors' knowledge of the area. The level and grade of the available evidence was established. A total of 52 articles relating to best practices and (or) core competencies in clinical exercise physiology met our eligibility criteria. Overall, current literature supports the need for qualified exercise professionals to possess advanced certification and education in the exercise sciences, particularly when dealing with "at-risk" populations. Current literature also substantiates the safety and effectiveness of exercise physiologist supervised stress testing and training in clinical populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".