Co-developing a disability-informed competency framework for qualified exercise professionals at entry-to-practice: Reflections from a multi-phase partnership
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.084 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".