The partition of India through the lens of historical trauma: Intergenerational effects on immigrant health in the South Asian diaspora
Bibliographic record
Abstract
South Asian immigrants constitute the world's largest diaspora, with an estimated 25 million people tracing their ancestry to the Indian subcontinent. While recent studies find disproportionately high rates of cardiometabolic diseases and some mental conditions among South Asian immigrants across diverse settings, most research on the root causes of these disparities was conducted with limited engagement with the historical forces that shape the South Asian diasporic experience. This review contextualizes the health challenges currently facing the South Asian diaspora within their shared history of over four centuries of European colonization that culminated in the Partition of India in 1947. Building on existing historical trauma theory, we propose a novel theoretical model connecting the collective trauma of the Partition with the health of affected immigrant populations, postulating plausible social, economic, psychological, and biological mechanisms that may explain current patterns. Since no research has explored these links directly, we summarize prior work examining trauma-related mental and physical health outcomes among this group, with a particular focus on populations living in Canada, the United Kingdom, and the United States, where the bulk of diasporic research has been conducted. We also theorize how a historical trauma perspective can be integrated into future public health research and practice to better meet the health needs of South Asian immigrants with origins that trace back to affected regions and discuss opportunities to expand this model to other postcolonial immigrant populations worldwide.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".