MétaCan
Menu
Back to cohort
Record W4407212552 · doi:10.1123/kr.2024-0051

Disability-Specific Learning Opportunities for Qualified Exercise Professionals to Build Competencies: A Scoping Review

2025· review· en· W4407212552 on OpenAlexaff
Alexandra J. Walters, Jennifer R. Tomasone, K. Jasmin, Michele Chittenden, Jennifer Leo, Zachary J. Weston, Dalton L. Wolfe, Amy E. Latimer‐Cheung

Bibliographic record

VenueKinesiology Review · 2025
Typereview
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsWestern UniversityUniversity of AlbertaLawson Health Research InstituteCanadian Society for Exercise PhysiologyUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsPsychologyMedical educationApplied psychologyMedicine

Abstract

fetched live from OpenAlex

Many qualified exercise professionals (QEPs) lack adequate training about exercise for persons with disabilities (PWDs), leaving them without the required abilities to provide PWDs with quality exercise experiences. Little is known about disability-specific knowledge, skills, and attitudes delivered in learning opportunities for QEPs. We aimed to identify (a) what content is delivered and (b) what modes are used to deliver disability-specific learning opportunities for QEPs. Peer-reviewed and gray literature searches, advanced Google searches, and expert consultations were undertaken. Twenty-five learning opportunities were identified. Most content focused on knowledge provision, with few opportunities for QEPs to develop skills and appropriate attitudes. Almost all content was delivered through passive learning methods, with little reported inclusion of PWDs in content development. It is essential to rethink QEP curriculum content and delivery about PWDs. This study provides valuable insight regarding the current limitations and areas for improvement concerning disability-specific learning opportunities available to QEPs.

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)0.007
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.449
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.246
GPT teacher head0.512
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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations4
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueKinesiology ReviewSame topicInclusion and Disability in Education and SportFrench-language works237,207