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Record W2026834641 · doi:10.5539/ass.v6n6p54

Predictors of Financial Dependency in Old Age in Peninsular Malaysia: An Ethnicity Comparison

2010· article· en· W2026834641 on OpenAlexvenueno aff
Benjamin Chan Yin-Fah, Tengku Aizan Hamid, Jariah Masud, Laily Paim

Bibliographic record

VenueAsian Social Science · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupIndependence (probability theory)Government (linguistics)Perspective (graphical)Dependency (UML)Intervention (counseling)Financial independenceDependency ratioFinancePsychologyPolitical scienceDemographyBusinessSociology

Abstract

fetched live from OpenAlex

Aging is a global issue affecting countries including Malaysia. From an economic perspective, the government encourages senior citizens to be financially independent for as long as possible. To what extent the elderly is financially dependent is well documented but only few studies focus on an ethnic disparity perspective. This paper aims to identify the predictors of financial dependency among older Malaysians from the three ethnic groups. Data from an area study in Malaysia involving 806 older persons who participated in face-to-face interviews was used. Results showed that more than half of the respondents are financially independent while 44% depends on their children, sons or daughters in law, friends, neighbors or government financial assistance. Age and employment status were significant predictors of financial independence across all ethnic models. The study showed that there are different predictors of financial dependency by ethnic group and the result calls for different intervention strategies for the various ethnic elderly in achieving financial independence in old age.

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 score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.014
GPT teacher head0.266
Teacher spread0.253 · 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

Citations8
Published2010
Admission routes1
Has abstractyes

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