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Record W2132414597

관절염 자조관리과정이 통증, 피로, 일상활동 어려움, 관절뻣뻣함, 유연성에 미치는 효과

2007· article· ko· W2132414597 on OpenAlexaboutno aff
이경숙, 최정숙, 이은희, 서길희, 소애영, 최선하

Bibliographic record

VenueJournal of muscle and joint health · 2007
Typearticle
Languageko
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical therapyMedicineOsteoarthritisWOMACRating scaleFlexibility (engineering)Test (biology)PsychologyAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Purpose: The purpose of this study was to determine if osteoarthritis patients would benefit in terms of pain, fatigue, difficulty with physical activity, joint stiffness, and flexibility of the joints from a structured self-help program. Method: This self-help program was carried out 2-3 hours once a week for 6 weeks in 2005-2006, and evaluated in one group pretest-posttest pre-experimental design. The subjects of this study who were diagnosed osteoarthritis were recruited at two different Community Health Centers in Kangwon. The subjects who agreed with the purpose of this study and participated both pretest and post-test were 55 patients. Mean age is 63.48 (9.48) years, mean duration of disease is 7.95 (7.66) years. The self-help program was consisted of weekly health contract, exercise, health education, group discussion, group counseling, and recreation. At every meeting, researcher and trained public health nurse evaluated the program, and prepared the next program. The measurement tools were pain rating scale (0-10), fatigue rating scale (0-10), Korean-WOMAC (Western Ontario and McMaster University Osteoarthritis) Index, ruler, and goniometer. Results: At the completion of 6 weeks of self-help program, the subjects reported significantly less pain and difficulty with physical activity and more flexibility in both shoulder and knee joints compared to pretest. Conclusion: The self-help program would be helpful on pain, physical activity, and joint flexibility for arthritis patients.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.101
GPT teacher head0.420
Teacher spread0.319 · 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 designOther design
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
Published2007
Admission routes1
Has abstractyes

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