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Musculoskeletal Pain and Social Support in Older Adults of a Sample in Bucaramanga

2016· article· en· W2562481686 on OpenAlexaboutno aff
Leidy Johanna Plata Osma, ‪Ara Mercedes Cerquera Córdoba‬‬‬‬‬‬‬‬‬‬‬‬‬

Bibliographic record

VenuePensamiento Psicológico · 2016
Typearticle
Languageen
FieldMedicine
TopicAging, Health, and Disability
Canadian institutionsnot available
Fundersnot available
KeywordsSocial supportPsychologyMcGill Pain QuestionnaireClinical psychologySample (material)Physical therapyMedicineSocial psychology

Abstract

fetched live from OpenAlex

Objective. The aim of this study was to determine the relation between clinical characteristics of musculoskeletal pain and the social support. Method. A non experimental method was used, with a quantitative approach, cross-sectional and correlational scope, in a sample with 70 or older adults –women and menwho participate of selfhelp groups in Bucaramanga, sample which was applied with the abbreviated version McGill Pain Questionnaire and the Social Support Questionnaire. Results. It was found that 59% of the participants describe the pain as uncomfortable, and the 57% as internal, whereas, 33% perceive emotional support and the 39% a practical support in a moderate manner. No correlation was found between the general emotional support and pain intensity index. Conclusion. It is found that there is no significant correlation between variables, however, 1 Producto del proyecto de investigacion “Factores psicosociales en el dolor osteomuscular en adultos mayores. Un estudio transcultural”. Avalado y financiado por la Direccion General de Investigaciones (DGI) de la Universidad Pontificia Bolivariana Seccional Bucaramanga, con el codigo 096-03153100 2 Psicologa 3 Doctoranda en Ciencias Psicologicas de la Universidad de la Habana, Cuba. Universidad Pontificia Bolivariana, Seccional Bucaramanga, kilometro 7 via Piedecuesta. Correo de correspondencia: ara.cerquera@upb.edu.co LEIDY JOHANNA PLATA OSMA Y ARA MERCEDES CERQUERA CORDOBA 126 it is necessary to note that these kind of studies will help strengthen the understanding of the perception of pain and the variables that should be taken into account for its proper clinical 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.015
GPT teacher head0.297
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 source (direct Gemma or distilled Codex), 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

Citations33
Published2016
Admission routes1
Has abstractyes

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