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Record W2945678011 · doi:10.24095/hpcdp.39.5.04

At-a-glance – Conceptualizing a framework for the surveillance of physical activity, sedentary behaviour and sleep in Canada

2019· article· en· W2945678011 on OpenAlexaffvenueabout
Gregory Butler, Karen Roberts, Erin Kropac, Deepa P. Rao, Brenda Branchard, Stéphanie A. Prince, Wendy Thompson, Gayatri Jayaraman

Bibliographic record

VenueHealth Promotion and Chronic Disease Prevention in Canada · 2019
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsHealth CanadaUniversity of OttawaPublic Health Agency of Canada
Fundersnot available
KeywordsPhysical activityScope (computer science)Conceptual frameworkAgency (philosophy)Conceptual modelSedentary behaviorSleep (system call)Work (physics)PsychologyGerontologySociologyMedicineComputer sciencePhysical medicine and rehabilitationEngineering

Abstract

fetched live from OpenAlex

The Public Health Agency of Canada (PHAC) has modernized its approach to physical activity surveillance by broadening its scope to include sedentary behaviour and sleep. The first step was to develop a conceptual framework which covers the full spectrum of physical movement from moderate-to-vigorous intensity physical activity (MVPA) and light intensity physical activity (LPA) to sedentary behaviour and sleep. The framework accounts for the environments in which these behaviours take place (home, work/school, transportation, and community), and applies a socioecological approach to incorporate individual factors and broader built, social, and societal environmental indicators. A visual model of the conceptual framework was created to aid dissemination.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.775
Threshold uncertainty score0.899

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.010
Science and technology studies0.0130.016
Scholarly communication0.0130.003
Open science0.0040.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.347
Teacher spread0.312 · 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 designTheoretical or conceptual
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

Citations10
Published2019
Admission routes3
Has abstractyes

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