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Pain characteristics, coping strategies and its relation with the quality of life in patients with chronic pain diseases

2017· article· en· W2590192635 on OpenAlexaboutno aff
Ángela María Orozco-Gómez, Viviana Villamil-Munévar, Lina María Mateus, Carlos Morales-Cruz, Ruby Osorio-Noriega

Bibliographic record

VenueSalud & Sociedad · 2017
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsnot available
Fundersnot available
KeywordsChronic painCoping (psychology)Quality of life (healthcare)MedicineRelation (database)Physical therapyPsychologyClinical psychologyComputer scienceNursingData mining

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the relationship among pain characteristics, coping strategies, and the perception of quality of life in patients with Rheumatoid Arthritis (RA), Osteoarthritis (OA) and Fibromyalgia (FM) through a descriptive correlational method. METHOD: 99 participants from a rheumatic diseases clinic in Bogota (Colombia) were surveyed. Variables were measured with the Visual Analog Scale (VAS), the McGill Pain Questionnaire (MPQ), the Coping Strategies Questionnaire (CSQ), and the Rheumatoid Arthritis Quality of Life Scale (RAQoL). RESULTS: The perception of quality of life was lower when patients reported higher intensity in the perception of pain and a higher score in the affective, sensorial, and evaluative domains of pain. Coping strategies varied among patients with RA, OA, and FM; however, catastrophic thinking is the cognitive strategy mostly used among the three pathologies. CONCLUSION: Intervention programs that help patients change or improve their coping strategies to reduce the intensity of pain and how it is valued are needed in order to produce a positive impact in the quality of life.

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)0.001
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 score0.002
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.028
GPT teacher head0.310
Teacher spread0.282 · 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
Published2017
Admission routes1
Has abstractyes

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