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Record W2775840764 · doi:10.1080/01634372.2017.1419395

Family conflict and future concerns: Opportunities for social workers to better support Chinese immigrant caregiver employees

2017· article· en· W2775840764 on OpenAlexafffundabout
Bharati Sethi, Allison Williams, Élise Desjardins, Hanzhuang Zhu, Emile Shen

Bibliographic record

VenueJournal of Gerontological Social Work · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsMcMaster UniversityWestern UniversityKing's University College
FundersInstitute of Gender and HealthCanadian Institutes of Health Research
KeywordsImmigrationEthnic groupQualitative researchService providerSocial workHealth careService (business)SociologyPsychologyNursingEconomic growthPolitical scienceMedicineBusiness

Abstract

fetched live from OpenAlex

This paper explores the experiences of Chinese immigrant caregiver employees (CEs) residing in Southern Ontario, Canada. Qualitative analysis of participant interviews with thirteen Mandarin Chinese immigrant CEs revealed family conflicts due to cultural differences and an intergenerational gap between CEs and their care recipients. CEs also had future concerns in regards to their own health and the lack of long-term care facilities that offer cultural services for immigrant seniors. These findings provide an opportunity for social workers to collaborate with other service providers to provide ethno-specific and culturally sensitive health, community. and employment services to immigrant ethnic minority CEs.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0220.005
Scholarly communication0.0040.003
Open science0.0010.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.090
GPT teacher head0.359
Teacher spread0.269 · 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 designQualitative
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

Citations9
Published2017
Admission routes3
Has abstractyes

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