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Record W1973650383 · doi:10.5539/ass.v8n6p184

Intergenerational Relationships and Communication among the Rural Aged in Malaysia

2012· article· en· W1973650383 on OpenAlexvenueno aff
Rahimah Abdul Aziz, Fatimah Yusooff

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
FundersUnivers Foundation
KeywordsKinshipInstitutionUnit (ring theory)Extended familyQuality (philosophy)Nuclear familySocioeconomicsSociologyPsychologyGeographySocial science

Abstract

fetched live from OpenAlex

In Malaysia the family has been the main institution caring for the aged. However, the processes of development, directly or indirectly, have impacted the size, functions and structure of the family unit, which subsequently impacted the family intergenerational relationships and communication. This study is about the intergenerational relationships and communication specifically among the rural aged with their family members. The main objective is to further understand the relationship of the aged with those around them and its contribution to productive and quality ageing process. Survey was the main method used in the study and was conducted among 200 older persons residing in the rural area of mukim Sungai Petani, in the district of Kuala Muda, Kedah, Malaysia. A total of 186 co-residing adult children were also interviewed. The study shows that the intergenerational relationship is still stable and harmonious. The study also shows that the family structure in the area is mainly extended rather than nuclear with the family members exchanging mutual support. In fact, family and kinship network have remain important support network for the families. Generally, the children show positive and responsible attitudes towards their parents. This is in line with Malaysian culture that places importance on good and close relationships between generations.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.292
Teacher spread0.271 · 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.

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

Citations18
Published2012
Admission routes1
Has abstractyes

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