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Record W2147182525 · doi:10.1123/jpah.2011-0325

How to Motivate Childcare Workers to Engage Preschoolers in Physical Activity

2014· article· en· W2147182525 on OpenAlexaffabout
Camille Gagné, Isabelle Harnois

Bibliographic record

VenueJournal of Physical Activity and Health · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsTheory of planned behaviorPsychologyPhysical activityNorm (philosophy)Developmental psychologyIntervention (counseling)Social norms approachSocial psychologyVariance (accounting)Descriptive statisticsApplied psychologyControl (management)MedicinePerception

Abstract

fetched live from OpenAlex

BACKGROUND: Data available indicate that numerous childcare workers are not strongly motivated to engage children aged 3-5 in physical activity. Using the theory of planned behavior as the main theoretical framework, this study has 2 objectives: to identify the determinants of the intention of childcare workers to engage preschoolers in physical activity and to identify the variables that could be used to develop an intervention to motivate childcare workers to support preschoolers' physical activity. METHODS: 174 childcare workers from 60 childcare centers selected at random in 2 regions of Quebec completed a self-administered questionnaire assessing the constructs of the theory of planned behavior as well as past behavior, descriptive norm and moral norm. RESULTS: Moral norm, perceived behavioral control and subjective norm explained 85% of the variance in intention to engage the children in physical activity. CONCLUSIONS: To motivate childcare workers, it is necessary that they perceive that directors, children's parents and coworkers approve of their involvement in children's physical activity. In addition, their ability to overcome perceived barriers (lack of time, loaded schedule, inclement weather) should be developed. Access to a large outdoor yard might also help motivate childcare workers.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.002
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 head0.110
GPT teacher head0.476
Teacher spread0.365 · 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

Citations24
Published2014
Admission routes2
Has abstractyes

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