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Record W4283078631 · doi:10.1080/14927713.2022.2085156

The feasibility and impact of a painted designs intervention on school children’s physical activity

2022· article· en· W4283078631 on OpenAlexafffundvenue
Janet B. Wong, Kyle McCallum, Levi Frehlich, William Bridel, Meghan H. McDonough, Gavin R. McCormack, Kris Fox, Laura Brunton, Leah Yardley, Carolyn A. Emery, Brent Hagel

Bibliographic record

VenueLeisure/Loisir · 2022
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsAlberta Children's HospitalWestern UniversityUniversity of Calgary
FundersUniversity of Calgary
KeywordsPhysical activityIntervention (counseling)Psychological interventionPsychologyMathematics educationPhysical activity levelPhysical therapyMedicine

Abstract

fetched live from OpenAlex

Interventions such as painted designs on school tarmacs may increase children’s physical activity during school hours. This mixed-methods study examined the influence of a painted designs (e.g., traditional games, random circles) intervention on the physical activity experiences of elementary school children. Systematic observations and accelerometer data were collected to evaluate the type and quantity of student physical activity. Interviews were used to explore teacher and student experiences. Observed physical activity was not significantly different between intervention and control schools (t(43) = 0.22, p = 0.83), and children at the intervention schools undertook less physical activity (steps, moderate, vigorous, and combined moderate-to-vigorous activity) as compared with the control school (t = 2.71–4.35, p < 0.05). Teachers and students commented that the painted designs were confusing but held potential for inclusiveness, physical activity, and learning. Additional resources and instruction may assist in better use of painted designs for physical activity and academic learning.

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 score0.036
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.336
Teacher spread0.298 · 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

Citations2
Published2022
Admission routes3
Has abstractyes

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