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Record W2329316602 · doi:10.1080/07053436.2016.1151217

Elementary school generalist teachers’ perceived competence to deliver Ontario’s daily physical activity program

2016· article· en· W2329316602 on OpenAlexvenueaboutno aff
Todd C. Gilmore, Holly Donohoe

Bibliographic record

VenueLoisir et Société / Society and Leisure · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)OperationalizationGeneralist and specialist speciesPsychologyMedical educationSchool teachersGovernment (linguistics)Regression analysisMathematics educationMedicineMathematicsSocial psychologyStatistics

Abstract

fetched live from OpenAlex

The objective of this study was to examine the perceived competency of elementary school generalist teachers to deliver the government-mandated Daily Physical Activity (DPA) program. An e-survey was circulated to 20 elementary schools in a southern Ontario school board district. One hundred and thirty-six generalist teachers completed the survey from an initial pool of approximately 261 (response rate = 69%). Multiple linear regression was used to establish model fit and the prediction of participants’ perceived competence (dependent variable) to deliver DPA based on their motivation, school environment, and skills (independent variables). The most significant finding of this research is that nearly half of teachers surveyed (46%) reported that this mandatory program is not active in their schools. Additional analysis suggests that a majority of elementary teachers in this district lack specific DPA training and the motivation to teach the DPA program. The results not only suggest that the DPA program is not uniformly operationalized across the school board district, they also suggest that teacher competency may be a precondition for the DPA or other physical activity programs in elementary schools.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.077
GPT teacher head0.450
Teacher spread0.374 · 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.

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

Citations12
Published2016
Admission routes2
Has abstractyes

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