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Record W2341058834 · doi:10.1080/13691058.2016.1162328

Culture, but more than culture: an exploratory study of the HIV vulnerability of Indian immigrants in Canada

2016· article· en· W2341058834 on OpenAlexafffundabout
Yanqiu Zhou, Basanti Majumdar, Natasha Vattikonda

Bibliographic record

VenueCulture Health & Sexuality · 2016
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsImmigrationVulnerability (computing)InequalityContext (archaeology)NeglectSociologyGender studiesQualitative researchSocial psychologyPolitical scienceGeographyPsychologySocial science

Abstract

fetched live from OpenAlex

Explanations of immigrant health that ascribe to culture a fundamental causal role neglect the broader structural and contextual factors with which culture intersects. Based on a qualitative study of Indian immigrants' vulnerability to HIV in Canada, this paper presents a contextualised understanding of these individuals' understanding of, perceptions about, and responses to the HIV risk in their post-immigration lives. The study reveals that although culture - both traditional values and the norms of the diaspora community - appears to have constrained Indian immigrants' capacities to respond to the risk, this effect can be properly understood only by situating such constraint in the context of the settlement process that has shaped participants' living conditions, including their relationship with the diasporic community in Canada. We argue that HIV vulnerability should be conceptualised as a health inequality associated with broader systems of power relations (eg socio-economic marginalisation, gender inequality, discrimination, and racism). This more holistic conceptualisation of the intersection of culture, integration, and HIV vulnerability will facilitate exploration of HIV prevention strategies, through which interconnected inequalities of gender, race, and access to knowledge and resources can be challenged.

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)0.002
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 score0.203
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.362
Teacher spread0.316 · 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

Citations21
Published2016
Admission routes3
Has abstractyes

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