A Systematic Literature Review of the Experiences of Skilled South Asian Migrant Women Employees
Bibliographic record
Abstract
This systematic literature review explores the employment experiences of skilled South Asian migrant women in host countries, including the United Kingdom, Canada, Australia, New Zealand, and the United States. The study aims to address two central research questions: (1) What barriers do skilled South Asian migrant women face in seeking suitable employment after migration? and (2) How do skilled South Asian migrant women navigate and overcome these work barriers? Using a structured search strategy guided by the keywords 'skilled,' 'South Asian,' 'migrant women,' 'job,' and 'seek,' relevant peer-reviewed articles published in English between 2010 and 2025 were systematically identified across major academic databases. Following PRISMA guidelines, a total of 47 peer-reviewed articles were selected for inclusion in this review. Studies were included if they focused on primary research related to the employment experiences of skilled migrants from South Asian countries, specifically: Afghanistan, Bangladesh, Bhutan, India, Iran, the Maldives, Nepal, Pakistan, and Sri Lanka. Excluded were grey literature and non-employment-related studies. The synthesis reveals multiple, intersecting barriers, including the devaluation of foreign credentials, limited recognition of prior experience, gendered discrimination, and structural barriers within host-country labour markets. Cultural expectations and family responsibilities further constrain employment participation and advancement. Despite these challenges, skilled South Asian migrant women employ a range of adaptive strategies such as reskilling, professional networking, volunteering, and leveraging ethnic and community connections to re-enter the workforce or attain professional recognition. The review highlights the complexity of their integration journeys, emphasising the intersection of gender, ethnicity, and migration in shaping personal and labour market outcomes. The findings contribute to a deeper understanding of global talent mobility, offering insights for policymakers, employers, and professional bodies to develop more inclusive credential recognition systems, culturally responsive employment support, and equitable workplace practices. By identifying both barriers and navigation strategies, this review provides a foundation for future empirical research and practical interventions aimed at improving employment equity for skilled migrant women from South Asia.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".