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Record W2987230975 · doi:10.3390/ijerph16224400

Exploring the Feasibility and Effectiveness of a Childcare PhysicaL ActivitY (PLAY) Policy: Rationale and Protocol for a Pilot, Cluster-Randomized Controlled Trial

2019· article· en· W2987230975 on OpenAlexafffundabout
Patricia Tucker, Molly Driediger, Leigh M. Vanderloo, Shauna M. Burke, Jennifer D. Irwin, Andrew M. Johnson, Jacob Shelley, Brian W. Timmons

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsMcMaster UniversityHospital for Sick ChildrenInstitute for Clinical Evaluative SciencesSickKids FoundationWestern University
FundersCanadian Institutes of Health ResearchWestern University
KeywordsRandomized controlled trialProtocol (science)Cluster (spacecraft)Cluster randomised controlled trialPhysical activityMedicinePsychologyComputer sciencePhysical therapyComputer networkAlternative medicine

Abstract

fetched live from OpenAlex

Background: Young children are prone to low levels of physical activity in childcare. This environment, inclusive of equipment, policies, and staff, has been identified as influencing young children’s activity behaviours. To date, no study has examined the feasibility and effectiveness of such policies in Canadian childcare centres, while the provision of physical activity policies in other countries has shown some promise for improving the activity levels of young children. As such, the primary objective of the Childcare PhysicaL ActivitY (PLAY) Policy study is to examine the feasibility of an evidence-based, stakeholder-informed, written physical activity and sedentary time policy for centre-based childcare (i.e., at the institutional level). The secondary objectives are to examine the impact of policy implementation on the physical activity levels and sedentary time of young children, subsequent environmental changes in childcare centres, and childcare providers’ self-efficacy to implement a physical activity policy. This study will examine both policy implementation and individual (behavioural) outcomes. Methods/Design: The Childcare PLAY Policy study, a pilot, cluster-randomized controlled trial, involves the random allocation of childcare centres to either the experimental (n = 4) or control (n = 4) group. Childcare centres in the experimental group will adopt a written physical activity policy for eight weeks (at which time they will be asked to stop enforcing the policy). Physical activity levels and sedentary time in childcare will be assessed via ActiGraph™ accelerometers with measurements at baseline (i.e., week 0), mid-intervention (i.e., week 4), immediately post-intervention (i.e., week 9), and at six-month follow-up. Policy implementation and feasibility will be assessed using surveys and interviews with childcare staff. The Environment and Policy Assessment and Observation Self-Report tool will capture potential changes to the childcare setting. Finally, childcare providers’ self-efficacy will be captured via a study-specific questionnaire. A nested evaluation of the impact of policy implementation on young children’s physical activity levels will be completed. A linear mixed effects models will be used to assess intervention effects on the primary and secondary outcomes. Descriptive statistics and thematic analysis will be employed to assess the feasibility of policy implementation. Discussion: The Childcare PLAY Policy study aims to address the low levels of physical activity and high sedentary time observed in childcare centres by providing direction to childcare staff via a written set of evidence-informed standards to encourage young children’s activity and reduce sedentary time. The findings of this work will highlight specific aspects of the policy that worked and will inform modifications that may be needed to enhance scalability. Policy-based approaches to increasing physical activity affordances in childcare may inform future regulations and programming within this environment.

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)0.005
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.158
GPT teacher head0.448
Teacher spread0.289 · 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 designRandomized trial
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
Published2019
Admission routes3
Has abstractyes

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