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Effect of self-management on life quality of patient with chronic pain

2013· article· en· W3032223670 on OpenAlexaboutno aff
Bo-yu Liu, Zhao-rong Bi

Bibliographic record

VenueZhonghua xiandai huli zazhi · 2013
Typearticle
Languageen
FieldMedicine
TopicTraditional Chinese Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsQuality of life (healthcare)MedicinePhysical therapyMcGill Pain QuestionnaireIntervention (counseling)Self-managementChronic painLife qualitySignificant differencePain managementInternal medicineVisual analogue scalePsychiatryNursing

Abstract

fetched live from OpenAlex

Objective To study the effect of self-management on life quality of patient with chronic pain.Methods Totals of 80 hospitalized patients who fit the diagnostic criteria for choric pain were randomly divided into the observation group and the control group,each with 40 cases.Both groups received conventional life care,while the observation group was given health education and skill training of self-management in addition.Short-Form McGill Pain Questionnaire (SF-MPQ) and Short Form Quality of Life Questionnaire-12 (SF-12) were used to evaluate the intervention effect of two groups.Results There was no statistically significant difference of SF-MPQ and SF-12 between two groups before intervention (P > 0.05).3 months after selfmanagement,the scores of PRI,emotional items,VAS and PPI were respectively (12.5 ±3.8),(6.3 ±2.9),(2.7 ± 3.8),(1.5 ± 1.9) in the observation group,and (22.9 ± 3.5),(9.0 ± 1.5),(4.7 ± 2.3),(2.5 ±2.0)in the control group,and the differences were statistically significant (t =11.08,9.07,8.17,8.22,respectively;P < 0.05).There were also statistically significant differences in 8 dimensions of SF-12 scale after intervention,except for body pain (P < 0.05).Conclusions Self-management can effectively improve the quality of life of patient with chronic pain. Key words: Pain; Quality of life; Self-management

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.646

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 studies0.0000.000
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 head0.008
GPT teacher head0.262
Teacher spread0.254 · 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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