Disability-specific exercise prescription principles, learning opportunities and resources for exercise professionals to build competencies: A scoping review
Bibliographic record
Abstract
Persons living with disabilities (PWD) in Canada do not have equal opportunities to fully participate in exercise when compared to persons living without disabilities. The availability and support of qualified exercise professionals (QEPs) are essential factors in exercise options for PWD. However, the majority of QEPs are not sufficiently trained to work with PWD. The paucity of QEPs’ disability-specific knowledge, skills and attitudes (i.e., competencies) identifies a critical gap in the current training curriculums. This scoping review aimed to map the current state of disability-specific educational competencies taught in literature, learning opportunities, and resources for QEPs. Searches of library databases for academic and grey literature sources, online searches and content expert consultations were performed using scoping review methodology. Search results identified n=28 items for inclusion. Two reviewers independently coded for educational competencies using a modified version of the Theoretical Domains Framework. Almost all items (96%) provided disability-specific exercise knowledge. Some items provided opportunities for skill development (82%), and/or included information about the importance of working with clients with disabilities (71%). However, only two items (7%) and over two themes - safety, and inclusion for visual impairments, delivered complete disability-specific competency training (i.e., covered knowledge, skills, and attitudes). Consequently, educational opportunities for QEPs to learn about disability in exercise settings are limited. Determining content that is not being delivered in current training is an important next step to identify additional disability-specific competencies that QEPs should acquire prior to certification or through continuing professional development.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".