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Record W2000422433 · doi:10.1016/s1013-7025(10)70007-5

The Effects of a Community-based Water Exercise Programme on Health Outcomes for Chinese People With Rheumatic Disease

2009· article· en· W2000422433 on OpenAlexaff
Lavinia K.Y. Wong, Rhonda J. Scudds

Bibliographic record

VenueHong Kong Physiotherapy Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicinePhysical therapyVitalityQuality of life (healthcare)Confidence intervalDiseaseRepeated measures designInternal medicineNursing

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the effects of a community-based water exercise programme (CBWEP) on physical disability, quality of life, and confidence in performing exercise for people with rheumatic disease. A multiple pretest, within-subject design was used. All subjects participated in a 4-week CBWEP followed by an 8-week maintenance period. Physical disability, pain intensity, quality of life, and level of confidence in performing the exercises were evaluated using a self-administered questionnaire at baseline, pre-class, post-class, and at follow-up. Thirty-one subjects (30 female, 1 male) participated in the study. Repeated measures analysis of variance showed a statistically significant difference in pain intensity (p < 0.001) as well as in six domains of the SF-36 questionnaire: general health (p < 0.001), physical function (p = 0.001), role-physical (p = 0.001), role-emotional (p = 0.001), vitality (p < 0.001), and bodily pain (p = 0.006). There was also an improvement in confidence in performing the exercises (p < 0.001) and exercise participation at the end of the study. A CBWEP and continued maintenance classes may be beneficial for pain reduction and improvement in quality of life. These findings provided evidence for the value of this type of community exercise programme for people with rheumatic disease in Hong Kong.

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 score0.795
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.330
Teacher spread0.318 · 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
Published2009
Admission routes1
Has abstractyes

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