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Record W2028238936 · doi:10.1139/h08-037

Survey of fitness facilities for individuals post-stroke in the Greater Toronto Area

2008· article· en· W2028238936 on OpenAlexaffvenueabout
Amy Fullerton, M.J. MacDonald, Andrea Brown, Pha-Ly Ho, Jennifer Martin, Ada Tang, Kathryn M. Sibley, William E. McIlroy, Dina Brooks

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2008
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsCanada Research ChairsUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsStroke (engine)GerontologyPhysical fitnessPopulationMedicinePsychologyPhysical therapyEnvironmental health

Abstract

fetched live from OpenAlex

In light of the demonstrated importance of fitness programs after stroke, the current study set out to determine the availability and characteristics of fitness programs for individuals after stroke in the Greater Toronto Area (GTA). A questionnaire was distributed to 784 fitness programs in the GTA requesting information on the facility, program characteristics, and barriers to and willingness in offering specific programs for individuals post stroke. Of the 213 respondents, 146 (69%) reported that individuals with a chronic disability participated in their activities, 39 (18%) did not allow individuals with disabilities to participate, and 28 (13%) were unaware if individuals with disabilities accessed their facilities. Sixty-two facilities (29%) offered specific fitness programs for individuals with a chronic disability including 26 (12%) that offered exercise programs for people who have had a stroke. The study identified that a small percentage of fitness programs surveyed in GTA have fitness programs for individuals post-stroke. Since the occurrence of stroke is expected to increase as the population ages, the need for community fitness programs for individuals post-stroke will continue to rise. Many facilities expressed interest in offering specific fitness programs for individuals post-stroke; therefore, barriers must be addressed to facilitate the development of these programs.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.573

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.270
Teacher spread0.238 · 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 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

Citations26
Published2008
Admission routes3
Has abstractyes

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