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Record W3043674718 · doi:10.1080/13557858.2020.1791316

The impact of China-to-US immigration on structural and cultural determinants of HIV-related stigma: implications for HIV care of Chinese immigrants

2020· article· en· W3043674718 on OpenAlexaff
Timothy D. Becker, Ohemaa B. Poku, Xinlin Chen, Jeffrey Man Hay Wong, Amar Mandavia, Minda Huang, Yuqi Chen, Debbie Huang, Hong Ngo, Lawrence H. Yang

Bibliographic record

VenueEthnicity and Health · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsColumbia College
FundersNational Institute of Mental HealthRobert Wood Johnson Foundation
KeywordsImmigrationStigma (botany)ChinaHuman immunodeficiency virus (HIV)PsychologyDemographic economicsSociologyPolitical scienceGender studiesMedicinePsychiatryVirologyEconomics

Abstract

fetched live from OpenAlex

Objectives: Asian Americans have poor HIV-related outcomes, yet culturally salient barriers to care remain unclear, limiting development of targeted interventions for this group. We applied the ‘what matters most’ theory of stigma to identify structural and cultural factors that shape the nature of stigma before and after immigration from China to the US.Design: Semi-structured interviews were conducted with 16 immigrants to New York from China, recruited from an HIV clinic and community centers. Deductive followed by focal inductive qualitative analyses examined how Chinese cultural values (lian, guanxi, renqing) and structural factors influenced stigma before and after immigration.Results: In China, HIV stigma was felt through the loss of lian (moral status) and limited guanxi (social network) opportunities. A social structure characterized by limited HIV knowledge, discriminatory treatment from healthcare systems, and human rights violations impinged on the ability of people living with HIV to fulfill culturally valued goals. Upon moving to the US, positions of structural vulnerability shifted to enable maintenance of lian and formation of new guanxi, thus ameliorating aspects of stigma.Conclusions: HIV prevention and stigma reduction interventions among Chinese immigrants may be most effective by both addressing structural constraints and facilitating achievement of cultural values through clinical, peer, and group interventions.

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)0.000
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 score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.040
GPT teacher head0.420
Teacher spread0.380 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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