African journal of biomedical research
Bibliographic record
Abstract
Migration is a transformative process that brings both opportunities and challenges, particularly for individuals from distinct cultural and religious backgrounds. This study focuses on the psychological health of Namdhari Sikh migrants from Ellenabad Block in Sirsa, Haryana, and explores how they navigate the psychological impacts of migration, including loneliness, anxiety, depression, and acculturation stress. The Namdhari Sikh community, known for its unique religious beliefs and cultural practices, faces particular challenges in maintaining their identity and adapting to the norms of host countries. The study examines how cultural adaptation, identity struggles, and social support systems influence mental health outcomes. Data were collected through in-depth, semi-structured interviews with 755 migrants who relocated to countries such as Canada, Australia, the UK, and the USA. The results highlight significant psychological challenges, with 50% of participants reporting loneliness and 15% experiencing anxiety and stress. Cultural conflicts and the loss of religious practices were also common concerns, as 49% of migrants faced difficulties in maintaining their spiritual practices. Social support, particularly from family and religious communities, emerged as a key factor in mitigating psychological distress. Economic pressures, such as the need to send remittances and job insecurity, also contributed to stress. The study emphasizes the importance of cultural identity preservation and social support networks in enhancing the mental well-being of migrants. These findings provide valuable insights for policy development and community support initiatives aimed at addressing the unique challenges faced by marginalized migrant groups.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.256 | 0.113 |
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".