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Record W2121448377 · doi:10.1177/2158244014566365

A Review of Ethnicity, Culture, and Acculturation Among Asian Caregivers of Older Adults (2000-2012)

2015· review· en· W2121448377 on OpenAlexaboutno aff
Christina E. Miyawaki

Bibliographic record

VenueSAGE Open · 2015
Typereview
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsAcculturationVietnameseEthnic groupSocioeconomic statusReciprocity (cultural anthropology)ImmigrationFamily caregiversPsychologyMulticulturalismGerontologyGender studiesSociologyMedicineSocial psychologyPolitical scienceDemographyAnthropologyPopulation

Abstract

fetched live from OpenAlex

This review identified domains of care experiences among studies of Chinese, Filipino, Japanese, Korean, and Vietnamese caregivers in the United States and Canada between 2000 and 2012. Using a narrative approach, 46 peer-reviewed journal articles were found through electronic databases and references. Considering caregivers' assimilation to host countries, attention was given to their culture, socioeconomic resources, immigrant status, filial responsibility, generation, and acculturation. Three primary domains were identified across subgroups. The caregivers' experiences domain was a strong sense of filial responsibility and its varied effects on caregiving experience; in the cultural values domain, reciprocity, and familism. In the acculturation domain, caregivers' generations influenced their experiences. Because our society is rapidly changing demographically and culturally, studies of older adults and their caregivers that are not only inclusive of all racial/ethnic groups but also sensitive to specific racial/ethnic and cultural subgroup differences are necessary to inform policy and practice.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.010
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.371
Teacher spread0.334 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations111
Published2015
Admission routes1
Has abstractyes

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