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Record W4319068063 · doi:10.1123/kr.2022-0036

Translational Physical Activity Research Involving People With Disabilities: A Review and a Call to Action

2023· review· en· W4319068063 on OpenAlexaff
Kathleen A. Martin Ginis, Sarah Lawrason, Haley A. Berrisford

Bibliographic record

VenueKinesiology Review · 2023
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of AlbertaInternational Collaboration On Repair DiscoveriesUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsTranslational researchGeneral partnershipDiversity (politics)Inclusion (mineral)Call to actionKinesiologyAction (physics)Action researchPublic relationsHealth equityEquity (law)PsychologyParticipatory action researchSociologyPolitical scienceMedical educationMedicinePublic healthNursingPedagogySocial psychologyBusiness

Abstract

fetched live from OpenAlex

The health and physical activity (PA) needs of people living with disabilities are underserved and understudied. This article provides an overview of research on PA and health research in people with disabilities. Research gaps and inequities are highlighted, along with their impact on advancing the fundamental rights of people with disabilities to fully participate in PA. The importance of translational PA research to disability communities is described. We provide case studies from two lines of PA and disability research that have been moved along the translational spectrum and into practice. The article concludes with three calls to action to kinesiology research and practitioners: (a) to include people with disabilities in research; (b) to advocate for adequate resources and support in alignment with equity, diversity, and inclusion efforts; and (c) to work in meaningful partnership with people with disabilities to support translational research programs that have real-world impacts.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.260
GPT teacher head0.487
Teacher spread0.227 · 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 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

Citations8
Published2023
Admission routes1
Has abstractyes

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