Enhancing the effectiveness of clearance for physical activity participation: background and overall process<sup>1</sup>This paper is one of a selection of papers published in the 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
Recent feedback from physical activity (PA) participants, fitness professionals, and physicians has indicated that there are limitations to the utility and effectiveness of the existing PAR-Q and PARmed-X screening tools for PA participation. The aim of this study was to have authorities in exercise and chronic disease management to work with an expert panel to increase the effectiveness of clearance for PA participation using an evidence-based consensus approach and the well-established Appraisal of Guidelines for Research and Evaluation (AGREE) Instrument. Systematic reviews were conducted to develop a new PA clearance protocol involving risk stratification and a decision-tree process. Evidence-based support was sought for enabling qualified exercise professionals to have a direct role in the PA participation clearance process. The PAR-Q+ was developed to use formalized probes to clarify problematic responses and to explore issues arising from currently diagnosed chronic disease or condition. The original PARmed-X tool is replaced with an interactive computer program (ePARmed-X+) to clear prospective PA participants for either unrestricted or supervised PA or to direct them to obtain medical clearance. Evidence-based validation was also provided for the direct role of highly qualified university-educated exercise professionals in the PA clearance process. The risks associated with exercise during pregnancy were also evaluated. The systematic review and consensus process, conforming to the AGREE Instrument, has provided a sound evidence base for enhanced effectiveness of the clearance process for PA participation of both asymptomatic populations and persons with chronic diseases or conditions.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".