Culture, but more than culture: an exploratory study of the HIV vulnerability of Indian immigrants in Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.036 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".