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Record W1949622813

관절염 환자를 위한 타이치 프로그램 적용 후 통증, 피로, 인지된 건강상태, 관절뻣뻣함, 일상활동장애, 우울의 변화

2008· article· ko· W1949622813 on OpenAlexaboutno aff
이경숙, 소애영, 최정숙, 이은희, 유보비

Bibliographic record

VenueJournal of muscle and joint health · 2008
Typearticle
Languageko
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhysical therapyDepression (economics)WOMACOsteoarthritisRepeated measures designActivities of daily livingRating scaleGeriatric Depression ScaleHealth assessmentPsychiatryPsychologyAlternative medicineDepressive symptomsAnxiety
DOInot available

Abstract

fetched live from OpenAlex

Purpose: The purpose of this study was to observe the changes of the pain, fatigue, perceived health status, disability in ADL, and depression after Tai Chi for arthritis. Method: The subjects were 19 osteoarthritis women enrolled at one primary health care post. They have agreed to participate in this study, and participated in the 12-week Tai Chi program from Dec. 2006 to Mar. 2007, and all the measurement processes in right after, 2 months later, and 4 months later after Tai Chi program. The measurement tools of this study were all self-reported questionnaires such as Numerical Rating Scale (0-100) for pain, fatigue, and perceived health status, Korean-WOMAC (Western Ontario and McMaster University Osteoarthritis) Index for joint stiffness, Korean Health Assessment Questionnaire, and CES-Depression scale. Results: The repeated measure ANOVA revealed the significant differences on pain among the right after, 2 months later, and 4 months later the program. Especially 2 months later after program, pain was significantly increased than right after by t-test. There were no significant differences on fatigue, perceived health status, disability in ADL, and depression. Conclusion: Pain was increased after Tai Chi for arthritis program, but fatigue, perceived health status, disability in ADL, and depression were not changed until 4 months after Tai Chi program.

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)0.015
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0010.001

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 head0.125
GPT teacher head0.434
Teacher spread0.309 · 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.

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

Citations0
Published2008
Admission routes1
Has abstractyes

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