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Record W2067449541 · doi:10.14288/hfjc.v10i1.234

The 2017 Physical Activity Readiness Questionnaire for Everyone (PAR-Q+) and electronic Physical Activity Readiness Medical Examination (ePARmed-X+)

2017· article· en· W2067449541 on OpenAlexaffabout
Darren E. R. Warburton, Veronica Jamnik, Shannon S. D. Bredin, Roy J. Shephard, Norman Gledhill

Bibliographic record

VenueOpen Collections · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhysical activityFamily medicinePeer reviewConsensus conferenceMedical educationPsychologyMedicineLibrary sciencePolitical sciencePhysical therapyLawComputer science

Abstract

fetched live from OpenAlex

This article contains the current CONSENSUS PANEL APPROVED AND OFFICIAL version of the Physical Activity Readiness Questionnaire for Everyone (PAR-Q+). The new PAR-Q+ and ePARmed-X+ were introduced officially at the 3rd International Congress on Physical Activity and Public Health in Toronto, Ontario, Canada (May 5-8, 2010) by Drs. Darren Warburton, Norman Gledhill, Veronica Jamnik, and Shannon Bredin. The first peer reviewed article containing the PAR-Q+ was published in the Health & Fitness Journal of Canada and subsequent updates have been made through this journal. Owing to the evidence-based nature of the PAR-Q+ and ePARmed-X+ both forms require routine update as the evidence expands. These ongoing revisions are made by the PAR-Q+ Collaboration and evaluated by an international consensus committee. This article contains the 2017 PAR-Q+ that includes significant changes from our original version. This version replaces all previous versions. This is the current evidence-based and consensus panel approved version of the PAR-Q+.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.048
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.003
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0170.008

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 head0.019
GPT teacher head0.342
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations39
Published2017
Admission routes2
Has abstractyes

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