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Record W4416889945 · doi:10.1016/j.aehs.2025.11.003

Co-developing a disability-informed competency framework for qualified exercise professionals at entry-to-practice: Reflections from a multi-phase partnership

2025· article· en· W4416889945 on OpenAlexafffundabout
Alexandra J. Walters, Jennifer R. Tomasone, Amy E. Latimer‐Cheung

Bibliographic record

VenueAdvanced Exercise and Health Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGeneral partnershipProcess (computing)Interpersonal communicationFace (sociological concept)Interpersonal relationshipKey (lock)

Abstract

fetched live from OpenAlex

Competency-based education (CBE) offers a promising approach to improve qualified exercise professionals’ (QEPs) knowledge, skills, and attitudes to support people experiencing disability in exercise settings. In Canada, QEP training rarely includes standardized, disability-specific content, leaving many people experiencing disability to face persistent interpersonal and systemic barriers to participation in exercise. This article shares lessons learned from our first attempt at co-developing a disability-specific competency framework for QEPs at entry-to-practice, using a multi-phase, partnership-based approach that integrated lived experience, practitioner expertise, and research evidence. Drawing on the AGREE II Instrument and a six-step competency framework model, we engaged in iterative reflection, adaptation, and co-design alongside key partners. This paper highlights how the process evolved in response to partner input, shifts in scope, and moments of ethical tension. We conclude by reflecting on the relational, methodological, and structural considerations involved in partnership-based competency framework development and offer lessons to support others pursuing inclusive, evidence-informed training initiatives.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.608
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.001
Scholarly communication0.0000.001
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 head0.140
GPT teacher head0.581
Teacher spread0.441 · 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 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

Citations0
Published2025
Admission routes3
Has abstractyes

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