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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 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)0.043
metaresearch head score (Gemma)0.052
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 score0.043
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.052
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0090.033
Scholarly communication0.0170.016
Open science0.0020.016
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0040.000

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 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

Citations3
Published2025
Admission routes1
Has abstractyes

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