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Record W2183513552 · doi:10.1123/jpah.8.8.1044

Evolution and Devolution of National Physical Activity Policy in Canada

2011· review· en· W2183513552 on OpenAlexaffabout
Cora L. Craig

Bibliographic record

VenueJournal of Physical Activity and Health · 2011
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsCanadian Fitness and Lifestyle Research Institute
FundersCenters for Disease Control and PreventionDepartment of Tourism and Culture
KeywordsPer capitaDevolution (biology)JurisdictionGovernment (linguistics)Physical activityAuditPolitical scienceHealth policyPublic administrationPublic economicsBusinessHealth careMedicineEnvironmental healthEconomicsAccounting

Abstract

fetched live from OpenAlex

BACKGROUND: Low levels of physical activity (PA) and fitness have long been a government concern in Canada; however, more than half of adults are inactive. This article examines factors influencing policy development and implementation using Canadian PA policy as a case study. METHODS: Current and historical PA policy documents were amassed from a literature review, audit of government and non government websites and from requests to government officials in each jurisdiction directly responsible for PA. These were analyzed to determine policy content, results, barriers, and success factors. RESULTS: The national focus for PA policy in Canada has devolved to a multilevel system that meets most established criteria for successful strategies. Earlier PA targets have been met; however, the prevalence of PA decreased from 2005 to 2007. Annual per capita savings in health care associated with achieving the earlier target is estimated at $6.15 per capita, yet a fraction of that is directed to promoting PA. CONCLUSION: Evidenced-based strategies that address multiple policy agendas using sector-specific approaches are needed. Sustained high-level commitment is required; advocacy grounded in metrics and science is needed to increase the profile of the issue and increase the commitments to PA policies in Canada and internationally.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.130
GPT teacher head0.425
Teacher spread0.295 · 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.

Study designOther design
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

Citations25
Published2011
Admission routes2
Has abstractyes

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