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Record W1895979718

Development and implementation of the Daily Physical Activity policy in Ontario, Canada: A retrospective analysis

2014· article· en· W1895979718 on OpenAlexaffabout
Kenneth R. Allison, Nour Schoueri‐Mychasiw, Jennifer Robertson, Erin Hobin, John J. M. Dwyer, Heather Manson

Bibliographic record

VenueRevue phénEPS / PHEnex Journal · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of GuelphPublic Health Ontario
Fundersnot available
KeywordsFlexibility (engineering)Context (archaeology)PoliticsPhysical activityPolicy analysisPolicy developmentPolitical sciencePublic relationsPublic administrationProcess managementMedicineBusinessManagementPhysical medicine and rehabilitationEconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: The Daily Physical Activity policy (DPA) is considered to be an important initiative but current perspectives suggest that it may be unevenly implemented in Ontario. The current study focused on a retrospective analysis of the initial development and implementation of DPA.METHODS: Semi-structured interviews were conducted with 10 central players involved in the development and implementation of DPA and transcripts were analysed thematically.RESULTS: Findings consisted of 11 final themes focusing on the influences on policy development and implementation, the roles and relationships involved, the barriers to implementation, and the current status of DPA.CONCLUSION: Key findings emerged in this study, such as the tension between flexibility and structure and the politics of incremental policy. This analysis contributes new insights into these issues and provides evidence both unique to the Ontario context and relevant to studies of physical activity policy and program implementation in other jurisdictions.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.288
Teacher spread0.267 · 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

Citations11
Published2014
Admission routes2
Has abstractyes

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