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Record W2885609096 · doi:10.1177/0164027518792662

Cell Phone Use and Happiness Among Chinese Older Adults: Does Rural/Urban Residence Status Matter?

2018· article· en· W2885609096 on OpenAlexaff
Xiangnan Chai, Hina Kalyal

Bibliographic record

VenueResearch on Aging · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsWestern University
Fundersnot available
KeywordsHappinessResidenceChinaMainland ChinaPhonePsychologyRural areaDemographyOddsGerontologyLogistic regressionGeographySocioeconomicsMedicineSocial psychologySociology

Abstract

fetched live from OpenAlex

This study explores the relationship between cell phone use and self-reported happiness among older adults in Mainland China and whether rural/urban residence status moderates this relationship. The analysis is based on a sample of 6,952 respondents over the age of 60, from the 2010 wave of China Family Panel Studies. Findings show that using own cell phone is positively associated with self-reported happiness among Chinese older adults (odds ratio [ OR] = 1.283, p < .001). This relationship remains for respondents residing in rural areas ( OR = 1.616, p < .01) but not for their urban counterparts. Findings reflect on how the happiness of Chinese older adults has been affected by a growing shift in the traditional family values due to the unprecedented economic growth. Results also highlight the disparities between state support for older adults in rural and urban areas as well as the necessity to develop relevant policies to improve the subjective well-being of China's rapidly growing population of older adults.

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)0.001
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.352
Teacher spread0.331 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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