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Record W4410005733 · doi:10.1080/13573322.2025.2495818

Understanding the development of physical education professionals’ policy capacity

2025· article· en· W4410005733 on OpenAlexaff
Jenna R. Lorusso, Ann MacPhail, Melody Viczko

Bibliographic record

VenueSport Education and Society · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsWestern University
Fundersnot available
KeywordsPhysical educationPedagogySociologyPsychology

Abstract

fetched live from OpenAlex

The United Nations Educational, Scientific, and Cultural Organization (UNESCO) and others have emphasized the need for all physical education (PE) professionals to prioritize attention to, and action on, PE policy to improve quality provision. Yet, policy neglect remains arguably normative in PE due, in part, to a lack of preparation for policy in higher education and professional learning programmes. Furthermore, research on what such preparation should entail has not been conducted. Therefore, in this research we asked: What are the key facilitators, barriers, and content in the development of PE professionals’ policy capacity (i.e. policy interest, understanding, and involvement/action/initiative)? A two-round interview and survey Delphi investigation with 16 international PE professionals with experience in policy research and/or practical initiatives was conducted. Participants were asked about key barriers, facilitators, and content in the development of their own and others’ policy capacity. Reflexive thematic analysis revealed that while participants acknowledged the barriers of lacking time, professional learning, confidence, and accessible language, the barrier they identified as most consequential was misunderstanding the nature of policy as only fixed documents developed in a top-down and linear process. In contrast, participants identified their shifts to understanding policy as more than static texts and as happening in complex processes to be a key facilitator, with reflexive interactions and relationships with others being critical to facilitating that perspective shift. Other facilitators identified included the issuing of moral imperatives to ‘use’ policy to generate change, policy learning in higher education, and the interrogation of policy. When considered alongside the existing literature, these findings highlight that: (a) dispelling unrealistic traditional policy myths and fostering complex policy perspectives is central to policy capacity development; (b) particular configurations of policy-focused learning communities are a key way to do that; and (c) one must determine their personal policy purpose to engage in such work.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.259
GPT teacher head0.525
Teacher spread0.266 · 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 designTheoretical or conceptual
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

Citations3
Published2025
Admission routes1
Has abstractyes

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