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Record W4308768655 · doi:10.1177/07334648221138282

The Effect of a Long-Term Care Insurance Program on Subjective Well-Being of Older Adults with a Disability: Quasi-Experimental Evidence from China

2022· article· en· W4308768655 on OpenAlexaff
Hongli Fan, Yingcheng Wang, Jinyan Gao, Zixuan Peng, Peter C. Coyte

Bibliographic record

VenueJournal of Applied Gerontology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Toronto
FundersNatural Science Foundation of Shandong ProvinceNational Social Science Fund of ChinaNational Natural Science Foundation of China
KeywordsLong-term care insuranceGerontologyLong-term careMedicineChinaNational health insurancePsychologyNursingPopulationEnvironmental health

Abstract

fetched live from OpenAlex

China launched its long-term care insurance (LTCI) program for older adults in 2016. Although the scheme has shown some promising outcomes, little is known about whether it improves subjective well-being. This study explored this topic among older persons with a disability and identified the underlying mechanisms associated with the channel of this effect using data from a national survey. The LTCI program was shown to improve the subjective well-being among older persons with a disability and this effect increased over time. The LTCI program has great positive effect among women and those who lived alone compared to their counterparts. Mechanism analysis revealed that the main channel by which the LTCI program has positive effect occurred through the satisfaction of long-term care needs and improved self-reported health. This study suggests promising benefits of the LTCI program for older Chinese 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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.329
Teacher spread0.319 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

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