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Record W6986424317

Physical literacy and physical activity in swedish preschool children – a cross-sectional study

2023· article· en· W6986424317 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical activityPromotion (chess)LiteracyHealth promotionEarly childhoodPublic healthCognition
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION:There are substantial evidence for the numerous positive health benefits of physical activity (PA) [1]. At the same time, PA-levels in European and Swedish children are insufficient [2]. Inadequate PA-levels, and associated noncommunicable diseases, are regarded as one of the most significant public health challenges confronting us. Hence, it is important to understand how we can promote ways for children to reach adequate PA-levels. Physical Literacy (PL), a theory with potential benefits for PA-behaviors and health, has garnered increasing attention over the last few years. Encompassing physical, affective, and cognitive dimensions PL is often described as an individual’s capacity, confidence, and motivation to partake- and engage in PA [3]. The early childhood years are suggested for PL promotion since this period is regarded as crucial for PA-behaviors, future health, and the opportunity to reach most children via school settings. However, the assessment and status of PL in young children, specifically in Sweden, is at best scarce. Consequently, research is required to assess PL and its connection to PA-levels in young Swedish children. METHODS:The data for this study will be derived from hip-worn accelerometers (GT3X+, Actigraph) worn for 7 days to assess PA-levels, as well as a modified version of the Canadian Preschool Physical Literacy Assessment to assess PL. The study will include 412 preschool children, aged 3-6, from 20 preschools. The data will be analyzed and presented via descriptive statistics, and multi-level linear regression models will be used to determine associations between total- and intensity stratified PA-levels and PL. RESULTS:Tentatively, the results of this study are expected to provide: 1) a picture of PL and PA in Swedish preschool children and the connection therein; 2) much-needed data for the fields of PL and early childhood research; 3) Indications on effectiveness of PL for promoting PA; and 4) guidance for future research in PL. CONCLUSION:With individual, and public health advancements in mind, there is ample reason to enhance our understanding of the relationship between PL and PA-levels of Swedish preschool children, as well as, adding data to the PL-field. The present study has the potential to contribute to these objectives. References:1. Warburton, D. E., & Bredin, S. S. (2017). Health benefits of physical activity: a systematic review of current systematic reviews. Current opinion in cardiology, 32(5), 541-556.2. Steene-Johannessen J, Hansen BH, Dalene KE, Kolle E, Northstone K, Møller NC, et al. Variations in accelerometry measured physical activity and sedentary time across Europe – harmonized analyses of 47,497 children and adolescents. Int J Behav Nutr Phys Act. 2020;17(1):38.3. Edwards, L. C., Bryant, A. S., Keegan, R. J., Morgan, K., & Jones, A. M. (2017). Definitions, foundations and associations of physical literacy: a systematic review. Sports medicine, 47(1), 113-126.

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)0.002
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 score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.319
Teacher spread0.308 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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