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Record W2056224483 · doi:10.5539/gjhs.v6n3p155

Assessing the Association between the Degree of Pain and Socioeconomic Status among Older Persons in Ghana

2014· article· en· W2056224483 on OpenAlexvenueno aff
Kwame Annin, B. I. I. Saeed, Alfred Edwin Yawson, A. A. I. Musah, Emmanuel Kweku Nakua, Peter Agyei‐Baffour, N. N. N. Nsowah-Nuamah

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
FundersUniversity of GhanaWorld Health Organization
KeywordsSocioeconomic statusLogistic regressionAssociation (psychology)Ordered logitMedicineOrdinal regressionDemographyGerontologyAgeingPsychologyEnvironmental healthPopulationInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The current study sought to examine the association between the degree of pain and socioeconomic status among older male and female Ghanaians. METHOD: Data were drawn from the 2007-08 World Health Organization Global Ageing and Adult Health (SAGE) survey conducted in Ghana (Young adults=803, Adults=1689 and Older adults=2616). This includes bodily aches Ghanaians experienced in the last 30 days. Analyses of the association of pain with predisposing and enabling factors were carried out by means of ordinal logistic regression analysis. RESULTS: In the age-adjusted model, pain was statistically significantly associated with the cohabitating group as its marginal effect suggests that respondents in that category were less likely to experience pain as related to the others in women. CONCLUSION: This study established that Ghanaian men go through more pain than their women counterparts. This article is premier to our knowledge to apply ordered logistic for the degree of pain.

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)0.014
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 score0.031
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
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 head0.035
GPT teacher head0.343
Teacher spread0.308 · 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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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