Validation of the PAR-Q+ and ePARmed-X+
Bibliographic record
Abstract
The Physical Activity Readiness Questionnaire for Everyone (PAR-Q+) and the electronic Physical Activity Readiness Medical Examination (ePARmed-X+) were created recently to reduce the barriers to physical activity participation for individuals with and without established chronic disease. Our primary purpose was to provide preliminary evidence on the effectiveness of these new forms for pre-participation screening and risk stratification. In particular, we sought to examine the new PAR-Q+ (and ePARmed-X+) risk stratification strategy in comparison to the previous PAR-Q. The new PAR-Q+ and ePARmed-X+ risk stratification and pre-participation strategy reduced significantly the number individuals that were sent for medical referral in comparison to the PAR-Q (i.e., 0.8% vs. 15%, respectively). The reliability of the PAR-Q+ over a three month period was high (r = 0.99). Moreover, the new strategy demonstrated high sensitivity (0.90 (95% CI = 0.77-0.96)) and specificity (1 (95% CI = 0.99-1)) for determining those with and without hypertension, respectively. In conclusion, our preliminary evaluation of the new PAR-Q+ and ePARmed-X+ risk stratification and pre-participation strategy in comparison to the PAR-Q reveals that the new process reduces greatly the barriers to physical activity participation, with a high reliability, sensitivity, and specificity of measurement.
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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 | 0.029 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".