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Record W2318746122 · doi:10.1017/s0144686x10001005

Understanding ageing in sub-Saharan Africa: exploring the contributions of religious and secular social involvement to life satisfaction

2010· article· en· W2318746122 on OpenAlexaff
Ivy Kodzi, Stephen Obeng Gyimah, Jacques Emina, Alex Ezeh

Bibliographic record

VenueAgeing and Society · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsQueen's University
FundersAfrican Population and Health Research Center
KeywordsReligiosityLife satisfactionContext (archaeology)PsychologyGeneral Social SurveySociologyGerontologySocial psychologyGeographyMedicine

Abstract

fetched live from OpenAlex

ABSTRACT Rapid urbanisation in sub-Saharan Africa is believed to have weakened the traditional family ties which sustained older people in the past, but there is little empirical evidence about how older people today perceive their ageing experience in sub-Saharan Africa. The international gerontology literature demonstrates that, apart from financial wellbeing and health status, religious and secular forms of social involvement are key predictors of life satisfaction in older ages. No formal analysis, however, exists on the effects of religious and non-religious social involvement on the subjective wellbeing of older people in sub-Saharan nations. This study sought to fill this gap by examining the relationship between religious identity, religiosity, and secular social engagement using survey data from a sample of 2,524 men and women aged 50 or more years living in informal settlements of Nairobi City. We found significant differences in life satisfaction between Moslems, Catholics and non-Catholic Christians. Secular social support, personal sociability and community participation had positive effects on subjective wellbeing. In this context, we also observed that next to health status, the social involvement of older people was very important for life satisfaction.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.095
GPT teacher head0.325
Teacher spread0.229 · 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 designQualitative
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

Citations26
Published2010
Admission routes1
Has abstractyes

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