Trauma and Activism: Using a Postcolonial Feminist Lens to Understand the Experiences of Service Providers Who Support Racialized Immigrant Women’s Mental Health and Wellbeing
Bibliographic record
Abstract
The global Black Lives Matter movement and COVID-19 pandemic drew attention to the urgency of addressing entrenched structural dynamics such as racialization, gender, and colonization shaping health inequities for diverse racialized people. Canadian community-based research with racialized immigrant women recognized the need to enhance service provider capacity using a strengths-based activism approach to support client health and wellbeing. In this study, we aimed to understand the impacts of this mental health promotion practice on service providers and strategies to support them. Through purposeful convenience sampling, three focus groups were completed with 19 service providers working in settlement and mental health services in Toronto, Canada. Participants represented varied ethnicities and work experiences; most self-identified as female and racialized, with experiences living as immigrant women in Canada. Postcolonial feminist and critical mental health promotion analysis illuminated organizational and structural dynamics contributing to burnout and vicarious trauma that necessitate a focus on trauma- and violence-informed care. Transformative narratives reflected service provider resilience and activism, which aligned with and challenged mainstream biomedical approaches to mental health promotion. Implications include employing a postcolonial feminist lens to identify meaningful and comprehensive anti-oppression strategies that take colonialism, racialization, gender, and ableism and their intersections into account to decolonize nursing practices. Promoting health equity for diverse racialized women necessitates focused attention and multilevel anti-oppression strategies aligned with critical mental health promotion practices.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.071 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| 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".