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Record W7115821581

SOCIO-ECONOMIC IMPACT ON INDIAN IMMIGRANT MOTHERS' WELL-BEING

2025· dissertation· en· W7115821581 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2025
Typedissertation
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationIndigenousPatriarchyColonialismIntersectionalityNarrativeKinshipGrounded theoryFeminismIdeology
DOInot available

Abstract

fetched live from OpenAlex

I critically examine the intersection of immigration, socio-economic stability, gendered labour roles, systemic racism, patriarchy, and colonial legacies, highlighting how systemic barriers, cultural expectations, and social support structures shape the lived experiences of Indian immigrant mothers in Canada. I adopted a Global South Indigenous arts-based methodology - 'Kolam' amplifying Indian immigrant mothers' voices in the Greater Toronto Area (GTA), exploring their struggles, determination, and conceptualizations of emotional well-being in the face of economic precarity and socio-cultural transition. I begin by tracing the historical trajectory of Indian women's socio-political status, from ancient matriarchal traditions to the rise of patriarchal structures and the impact of colonial rule. I examine how patriarchy and colonial ideologies marginalize Indian women by restructuring labour, education, and mobility, confining them to roles of dependence and caregiving. These historical patterns continue to shape their experiences even after transnational migration, reinforcing gendered hierarchies within both Indian diasporic kinship and Canadian institutions. I used a Global South Indigenous arts-based participatory approach and in-depth interviews grounded in Critical Race Feminism to capture the complexities of Indian immigrant mothers' pre- and post-migration experiences. Twenty Indian immigrant mothers from diverse socio-economic backgrounds in the GTA participated by creating visual art and verbal narratives about their experiences with support. I have used a narrative analysis approach to examine interview transcripts and to co-construct visual analysis of the artwork produced by the participants. The findings reveal key challenges such as employment insecurity, racialized gender norms, financial dependency, systemic racism, social isolation, and the lack of culturally sensitive mental health services. The study also interrogates the enduring impact of colonial histories on immigration policies, racialized labour markets, and the exclusion of immigrant women from social and economic mobility. By situating Indian immigrant mothers' experiences within the intersecting frameworks of patriarchy, colonialism, and immigration, this dissertation challenges dominant immigration narratives that often overlook the gendered and racialized struggles of Indian Immigrant Mothers in specificity. This research contributes to social work scholarship and immigrant mental health research by offering a critical analysis of the socio-economic determinants affecting Indian immigrant mothers. By centering their voices, this study advocates for a transformative, intersectional approach to immigrant mothers' well-being, ensuring that policy reforms and mental health services respond to the unique realities of racialized, gendered migration.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.903

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0030.000
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.247
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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