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Record W4220671220 · doi:10.1123/japa.2021-0292

Evaluating and Characterizing an Individually-Tailored Community Exercise Program for Older Adults With Chronic Neurological Conditions: A Mixed-Methods Study

2022· article· en· W4220671220 on OpenAlexaff
Vithusha Coomaran, Ali Khan, Erin Tyson, Holly A. Bardutz, Tristan Hopper, Cameron S. Mang

Bibliographic record

VenueJournal of Aging and Physical Activity · 2022
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of ReginaUniversity of Saskatchewan
Fundersnot available
KeywordsPhysical therapyPhysical medicine and rehabilitationMedicineSpinal cord injuryGrip strengthBalance (ability)RehabilitationStroke (engine)PsychologySpinal cordPsychiatry

Abstract

fetched live from OpenAlex

A mixed-methods approach was used to study an individually-tailored community exercise program for people with a range of chronic neurological conditions (e.g., stroke, spinal cord injury, brain injury, multiple sclerosis, Parkinson's disease) and abilities. The program was delivered to older adults (mean age: 62 ± 9 years) with chronic neurological conditions across a 12-week and an 8-week term. Participants attended 88% of sessions and completed 89% of prescribed exercises in those sessions. There were no adverse events. Clinically important improvements were achieved by all evaluated participants (n = 8) in at least one testing domain (grip strength, lower-extremity strength, aerobic endurance, and balance). Interviews with participants identified key program elements as support through supervision, social connection, individualized programming, and experiential learning. Findings provide insight into elements that enable a community exercise program to meet the needs of a complex and varied group. Further study will support positive long-term outcomes for people aging with neurological conditions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.418
Teacher spread0.375 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2022
Admission routes1
Has abstractyes

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