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Record W2166890746 · doi:10.1080/10810730.2011.626504

Canada's Physical Activity Guide: Examining Print-Based Material for Motivating Physical Activity in the Workplace

2011· article· en· W2166890746 on OpenAlexaffabout
Ronald C. Plotnikoff, Ivan Todosijczuk, Steven T. Johnson, Nandini Karunamuni

Bibliographic record

VenueJournal of Health Communication · 2011
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsAthabasca UniversityUniversity of Alberta
Fundersnot available
KeywordsPhysical activityReading (process)Sample (material)GerontologyIntervention (counseling)PsychologyDescriptive statisticsMedicinePhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

The authors conducted a secondary analysis on 202 adults from the Physical Activity Workplace Study. The aim of this analysis was to examine demographic characteristics associated with reading Canada's Physical Activity Guide (CPAG), being motivated by the guide, and whether participants in the Physical Activity Workplace Study who read the CPAG increased their physical activity levels over 1 year. Results revealed that less than 50% of participants read the full version of CPAG, and less than 10% were motivated by it. The CPAG also appears to be more appealing to and effective for women than for men. Although the CPAG had some influence in increasing mild physical activity levels in a workplace sample, there was also a decrease in physical activity levels among some members of the group. Overall, the effectiveness of CPAG was not substantial, and the findings of this analysis could help guide future targeted intervention materials and programs.

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)0.002
metaresearch head score (Gemma)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.688

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.151
GPT teacher head0.387
Teacher spread0.236 · 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

Citations6
Published2011
Admission routes2
Has abstractyes

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