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Record W2323110681 · doi:10.1123/jpah.2013-0473

Qualified Fitness and Exercise as Professionals and Exercise Prescription: Evolution of the PAR-Q and Canadian Aerobic Fitness Test

2014· article· en· W2323110681 on OpenAlexaffabout
Roy J. Shephard

Bibliographic record

VenueJournal of Physical Activity and Health · 2014
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsExercise prescriptionAerobic exerciseMedical prescriptionTest (biology)MedicineSophisticationPopulationPhysical fitnessPhysical therapyReferralProtocol (science)Family medicineAlternative medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Traditional approaches to exercise prescription have included a preliminary medical screening followed by exercise tests of varying sophistication. To maximize population involvement, qualified fitness and exercise professionals (QFEPs) have used a self-administered screening questionnaire (the Physical Activity Readiness Questionnaire, PAR-Q) and a simple measure of aerobic performance (the Canadian Aerobic Fitness Test, CAFT). However, problems have arisen in applying the original protocol to those with chronic disease. Recent developments have addressed these issues. METHODS: Evolution of the PAR-Q and CAFT protocol is reviewed from their origins in 1974 to the current electronic decision tree model of exercise screening and prescription. RESULTS: About a fifth of apparently healthy adults responded positively to the original PAR-Q instrument, thus requiring an often unwarranted referral to a physician. Minor changes of wording did not overcome this problem. However, a consensus process has now developed an electronic decision tree for stratification of exercise risk not only for healthy individuals, but also for those with various types of chronic disease. CONCLUSIONS: The new approach to clearance greatly reduces physician referrals and extends the role of QFEPs. The availability of effective screening and simple fitness testing should contribute to the goal of maximizing physical activity in the entire population.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.339
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations49
Published2014
Admission routes2
Has abstractyes

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