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Record W2937171900 · doi:10.1177/1747493019840930

Improving life after stroke needs global efforts to implement evidence-based physical activity pathways

2019· article· en· W2937171900 on OpenAlexaff
Frederike van Wijck, Julie Bernhardt, Sandra A. Billinger, Marie‐Louise Bird, Janice J. Eng, Coralie English, Luci Fuscaldi Teixeira‐Salmela, Marilyn MacKay-Lyons, Rita Melifonwu, Katharina S. Sunnerhagen, John M. Solomon, Shamala Thilarajah, Gillian Mead

Bibliographic record

VenueInternational Journal of Stroke · 2019
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsDalhousie UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Stroke (engine)RehabilitationQuality (philosophy)Physical activityService (business)Physical medicine and rehabilitationGerontologyPhysical therapyNursingMarketingBusiness

Abstract

fetched live from OpenAlex

There is an urgent need to improve life after stroke across the world-especially in low-income countries-through methods that are effective, equitable and sustainable. This paper highlights physical activity (PA) as a prime candidate for implementation. PA reduces modifiable risk factors for first and recurrent stroke and improves function and activity during rehabilitation and following discharge. Preliminary evidence also indicates PA is cost-effective. This compelling evidence urgently needs to be translated into seamless pathways to enable stroke survivors across the world to engage in a more active lifestyle. Although more quality research is needed-particularly on how to optimize uptake and maintenance of PA-this should not delay implementation of high-quality evidence already available. This paper shares examples of best practice service models from low-, middle-, and high-income countries around the world. The authors call for a concerted effort to implement high-quality PA services to improve life after stroke for all.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.318
Teacher spread0.295 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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