Relevance of the Get Active Questionnaire (GAQ) in a Tropical Environment
Bibliographic record
Abstract
Introduction: The Get Active Questionnaire (GAQ), developed by the Canadian Society for Ex-ercise Professionals (CSEP) was recently recommended for pre-participation screening for the general population in Singapore before increasing exercise levels. This review examines the evi-dence behind the GAQ and relevance to our tropical environment. Methods: Searches were done via Pubmed, MEDLINE and the Cochrane Central Register of Controlled Trials. Resources referenced by the CSEP were hand-searched. The CSEP was also contacted for further information. Evidence behind each GAQ question was compared to inter-national literature and guidelines, where applicable. Results: Out of 273 studies, 49 were suitable for analysis. Two GAQ studies commissioned by the CSEP showed a high negative predictive value but high false negative rate. Of the nine GAQ questions, those on dizziness, joint pains and chronic diseases appear justified. Those on heart disease/stroke, hypertension, breathlessness and concussion require modification. That on syncope can be amalgamated into the dizziness question. The remaining question may be deleted. No long-term studies were available to validate use of the GAQ. Heat disorders were not considered in the GAQ. Conclusions: Modification of the GAQ, including inclusion of environmental factors, may make it more suitable for the general population and should be considered.
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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 | 0.027 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".