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Record W3034220563 · doi:10.14288/hfjc.v12i4.291

Cross-cultural translation, adaptation, and reliability of the Spanish version of the Physical Activity Readiness Questionnaire for Everyone (PAR-Q+)

2019· article· en· W3034220563 on OpenAlexaff
Juliano Schwartz, Sebastià Mas-Alòs, Mônica Yuri Takito, Jesus Martinez‐Garcia, María Esther Álvarez Cueto, Martín S. Rubio Mibelli, Jennifer Nagtegaal, Joyce Lubert, Diogo Rodrígues Bezerra, Shannon S. D. Bredin, Darren E. R. Warburton

Bibliographic record

VenueOpen Collections · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCronbach's alphaReliability (semiconductor)Adaptation (eye)General partnershipConsistency (knowledge bases)PsychologyInternal consistencyApplied psychologyMedical educationSocial psychologyMedicineClinical psychologyPsychometricsComputer sciencePolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Researchers from different countries worked in partnership with the PAR-Q+ Collaboration to translate and adapt the questionnaire into Spanish. Initially two independent translations were created, then a common initial version was produced. After two independent back translations of this first document, the team of experts established the official Spanish version. A total of 177 volunteers answered this version twice, one to two weeks apart. This translated version had a very good consistency (Cronbach’s a = 0.995; p < 0.01) and for 98% of the questions an agreement almost perfect between the two times the questionnaire was answered (

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)0.030
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.030
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.059
GPT teacher head0.416
Teacher spread0.357 · 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 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

Citations7
Published2019
Admission routes1
Has abstractyes

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