Clinical Exercise Physiologists in Cardiac Rehabilitation and Clinical Exercise Testing
Bibliographic record
Abstract
ABSTRACT Background The unique training of clinical exercise physiologists (CEPs) positions them to be an integral part of multidisciplinary teams in phase 2 cardiac rehabilitation (CR). However, the roles and responsibilities of CEPs vary widely between institutions. In addition, job tasks of CEPs at some institutions might not fully leverage their knowledge and skills. The purpose of this study was to describe the roles and responsibilities of CEPs working in CR and noninvasive clinical exercise testing at select institutions in the United States. Methods This was a descriptive study of the job tasks performed by CEPs in CR and noninvasive clinical exercise testing at select institutions. Job tasks that are common to CR and noninvasive clinical exercise testing were identified by a working group of the Clinical Exercise Physiology Association. Results The 6 CR programs in this report are predominately staffed by CEPs with no other health care professional present during exercise classes. In 5 of these programs CEPs perform all tasks required of phase 2 CR, from patient screening to program discharge. At 3 of the 4 programs that also performed noninvasive exercise testing, CEPs performed all the necessary tasks with no other health care professional present in the room during testing. Conclusion CEPs play an integral role in the conduct of phase 2 CR and noninvasive cardiology exercise testing. Granting privileges to CEPs that allow them to work at the top of their knowledge and skills will allow other health care professionals to better use their skills in other high demand areas.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".