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Record W4313816360 · doi:10.3390/ijerph20021049

Pre-Service Teachers’ Perceptions of and Experiences with Classroom Physical Activity

2023· article· en· W4313816360 on OpenAlexafffundabout
Hannah Bigelow, Barbara Fenesi

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2023
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsFidelityPerceptionService (business)Medical educationPhysical educationPsychologyMental healthPhysical activityWork (physics)MedicineEngineeringPhysical therapyBusiness

Abstract

fetched live from OpenAlex

Physical inactivity is one of the most modifiable factors linked to childhood obesity. Several Canadian provinces adopted daily physical activity (DPA) policies to promote physical activity during the school day. In Ontario, only 23% of in-service teachers meet DPA mandates. Promoting DPA implementation must occur at the pre-service level to foster self-efficacy and create long-term teaching habits. This study surveyed 155 pre-service teachers from an Ontario university to determine key perceptions and practices that should be targeted during their educational training to improve DPA fidelity. Findings revealed that over 96% of pre-service teachers viewed physical activity as beneficial for their own and their students mental and physical health, and as much as 33% received no education or training related to DPA. Pre-service teachers valued DPA more if they had opportunities to learn about and observe DPA during school placements. Pre-service teachers were more confident implementing DPA if they were more physically active, viewed themselves as more athletic, and had more positive physical education experiences. This work brings to the forefront important factors that could contribute to DPA implementation among in-service teachers and highlights target areas at the pre-service level for improved fidelity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.204

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 integrity0.0000.000
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 head0.071
GPT teacher head0.414
Teacher spread0.342 · 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

Citations8
Published2023
Admission routes3
Has abstractyes

Explore more

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