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Record W2941576415

Enhancing university practicum students' roles in implementing the Ontario Daily Physical Activity (DPA) policy

2017· article· en· W2941576415 on OpenAlexaffabout
Angela M. Coppola, David J. Hancock, Veronica Allan, Matthew Vierimaa, Jean Côté

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsQueen's University
Fundersnot available
KeywordsPracticumGeneral partnershipContext (archaeology)Participatory action researchMedical educationCapacity buildingCitizen journalismPsychologyPedagogyMedicineSociologyComputer sciencePolitical science
DOInot available

Abstract

fetched live from OpenAlex

In Canada, the Ontario Daily Physical Activity (DPA) policy promotes comprehensive school physical activity programs (CSPAP) by mandating 20 minutes of daily physical activity in schools. As community and teacher involvement is a key component of the CSPAP framework, developing partnerships to implement CSPAPs is worth exploring to facilitate meaningful and relevant engagement of partners. One understudied role is that of university Physical Education practicum students who intern in the CSPAP context. Thus, the purpose of this study was to explore university practicum students' perceptions of DPA engagement and identify strategies to enhance their roles in implementing DPA. Using abductive reasoning to create meaningful and practical findings for CSPAP partners, we analysed the experiences of nine practicum students before and after DPA implementation using the CSPAP framework. We contextualized the findings using the CSPAP and community-based participatory research literature. Three themes provided insight into how to prepare practicum students for and enhance their roles in DPA: (1) building relationships to enhance DPA and facilitate school partners' engagement, (2) maximizing use of resources, and (3) co-learning implementation knowledge and skills. The main contributions of this study include the application of co-learning and mutual capacity-building strategies to the DPA and CSPAP context, and reflective questions to facilitate building relationships, maximizing use of resources, and co-learning between partners. Methodologically, this study is an example of creating practical DPA partnership findings using the CSPAP framework and provides support for further use of abductive reasoning methodologies to explore DPA and CSPAP programs and partnerships.Acknowledgments: This research was partially supported by an Insight Grant from the Social Sciences and Humanities Research Council of Canada (SSHRC Grant # 435-2014-0038)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0090.006
Scholarly communication0.0080.003
Open science0.0030.011
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.031
GPT teacher head0.415
Teacher spread0.384 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2017
Admission routes2
Has abstractyes

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