MétaCan
Menu
Back to cohort
Record W2895497822 · doi:10.1186/s12889-018-5897-4

The relationship between physical literacy scores and adherence to Canadian physical activity and sedentary behaviour guidelines

2018· article· en· W2895497822 on OpenAlexafffundabout
Kevin Belanger, Joel D. Barnes, Patricia E. Longmuir, Kristal D. Anderson, Brenda Bruner, Jennifer L. Copeland, Melanie Gregg, Nathan Hall, Angela M. Kolen, Kirstin N. Lane, Barbi Law, Dany J. MacDonald, Luc J. Martin, Travis J. Saunders, Dwayne P. Sheehan, Michelle Stone, Sarah J. Woodruff, Mark S. Tremblay

Bibliographic record

VenueBMC Public Health · 2018
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of WindsorMount Royal UniversityQueen's UniversityUniversity of Prince Edward IslandDalhousie UniversityUniversity of WinnipegSt. Francis Xavier UniversityUniversity of LethbridgeNipissing UniversityCamosun CollegeChildren's Hospital of Eastern Ontario
FundersMitacsRoyal Bank of CanadaPublic Health AgencyPublic Health Agency of Canada
KeywordsHealth literacyBiostatisticsMedicineGuidelineCompetence (human resources)Logistic regressionSittingPhysical educationGerontologyPhysical fitnessPublic healthPhysical therapyPsychologyMedical educationSocial psychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Physical literacy is an emerging construct in children's health promotion, and may impact their lifelong physical activity habits. However, recent data reveal that only a small portion of Canadian children are regularly physically active and/or meet sedentary behaviour guidelines. To our knowledge, no study has investigated the association between physical literacy and movement behaviour guidelines. Therefore, the purpose of this study was to examine the relationship between physical literacy scores in Canadian children who meet or do not meet physical activity and sedentary behaviour guidelines. METHODS: Children (n = 2956; 56.6% girls) aged 8-12 years from 10 Canadian cities had their physical literacy levels measured using the Canadian Assessment of Physical Literacy, which consists of four domains (Physical Competence; Daily Behaviour; Knowledge and Understanding; and Motivation and Confidence) that are aggregated to provide a composite physical literacy score. Physical activity levels were measured by pedometers, and sedentary behaviour was assessed through self-report questionnaire. Analyses were conducted separately for each guideline, comparing participants meeting versus those not meeting the guidelines. Comparisons were performed using MANOVA and logistic regression to control for age, gender, and seasonality. RESULTS: Participants meeting physical activity guidelines or sedentary behaviour guidelines had higher physical literacy domain scores for Physical Competence and for Motivation and Confidence compared to those not meeting either guideline (both p < 0.0001). Participants had increased odds of meeting physical activity guidelines and sedentary behaviour guidelines if they met the minimum recommended level of the Physical Competence and Motivation and Confidence domains. Significant age (OR 0.9; 95% CI: 0.8, 0.9), gender (OR 0.4; 95% CI: 0.3, 0.5) and seasonality effects (OR 1.6; 95% CI: 1.2, 2.2 spring and OR 1.7; 95% CI: 1.2, 2.5 summer, reference winter) were seen for physical activity guidelines, and age (OR 0.8; 95% CI: 0.7, 0.8) and gender effects (OR 1.7; 95% CI: 1.4, 2.0) for sedentary behaviour guidelines. Knowledge and Understanding of physical activity principles was not related to guideline adherence in either model. CONCLUSIONS: These cross-sectional findings demonstrate important associations between physical literacy and guideline adherence for physical activity and sedentary behaviour. Future research should explore the causality of these associations.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.120
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.147
GPT teacher head0.426
Teacher spread0.279 · 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

Citations172
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueBMC Public HealthSame topicChildren's Physical and Motor DevelopmentFrench-language works237,207