MétaCan
Menu
Back to cohort
Record W2140416974 · doi:10.12968/ijtr.2015.22.1.31

Martial arts practice in community-based rehabilitation: A review

2015· review· en· W2140416974 on OpenAlexaff
Pavithra Rajan, Hector W. H. Tsang

Bibliographic record

VenueInternational Journal of Therapy and Rehabilitation · 2015
Typereview
Languageen
FieldSocial Sciences
TopicMartial Arts: Techniques, Psychology, and Education
Canadian institutionsShastri Indo-Canadian Institute
Fundersnot available
KeywordsMartial artsRehabilitationPsychologyPhysical therapyThe artsMedicineMedical educationVisual arts

Abstract

fetched live from OpenAlex

Aim: Martial arts are forms of self-defence or attack that have been modified for modern sport and exercise, and are reported to provide health benefits for people who practise them. This article discusses the evidence base for the use of martial arts, such as tai chi, karate and taekwondo, in the rehabilitation process and how they may play a role in community-based rehabilitation. Findings: Tai chi is a low-impact form of exercise that can help to: reduce the risk of falls among community-dwelling stroke survivors; lower blood pressure in patients with heart failure; and improve the wellbeing of breast cancer survivors. Karate is a more rigorous form of martial arts than tai chi and research has highlighted its benefits for children, including: improved memory and self-esteem for those with epilepsy; reduced stereotypy among children with autism; and improved developmental skills among schoolchildren with and without special needs. Taekwondo is another rigorous martial art form that has potential health benefits; limited research has been conducted into this, with a study reporting improvements in class behaviour and academic performance among children who practise it. Conclusions: While each martial art form has its own positive health benefits, future research must be undertaken to compare the effectiveness of different martial art forms in different communities.

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)0.013
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.107
GPT teacher head0.504
Teacher spread0.397 · 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 designNot applicable
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

Citations10
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Therapy and RehabilitationSame topicMartial Arts: Techniques, Psychology, and EducationFrench-language works237,207