Making the Invisible, Visible: Reimagining Immigrant Mental Health Through the Stories of Latinx Women
Bibliographic record
Abstract
This dissertation describes a community-based qualitative study called ¿Nos Escuchan? (Can You Hear Us?). I conducted this study in Spanish with 14 cis immigrant women from Latin America, in partnership with the Center for Spanish Speaking Peoples in Toronto. I used a critical mental health approach to listen to the stories of Latinx women, to examine the impact of migration on wellbeing. My objective was to explore ways of understanding immigrant mental health outside of colonialist biomedical approaches. I utilized a methodology that I developed for this study (Postcolonial Narrative Inquiry), which is a new approach to narrative inquiry. As methods, I incorporated virtual story circles, metaphorical drawings, and individual interviews, alongside a collaborative podcast, as a knowledge translation tool. The research process centered around an opportunity to build community: participants gathered virtually over the course of six weeks, collectively sharing migration stories.In Chapter 1, I provide a background on immigrant women’s mental health in Canada, the need for critical mental health approaches, and an overview of the study. In Chapter 2, I describe Postcolonial Narrative Inquiry, which utilizes four central components (Deep Seeing, Deep Listening, Deep Hearing, and Deep Feeling) and several concrete tools to contribute to epistemic justice. In Chapter 3, I explore the intersections of four macro elements that impact women’s mental health, and their origins in colonialism, capitalism, and patriarchy. I examine how new language (eg. migratory grief or performing strength) can validate experiences and ground distress within power structures. In Chapter 4, I use a desire-based analysis to examine migration as a process of transformation, and the simultaneous joy and struggle the women experienced in the areas of identity, stability, safety, and community. I step outside of binary analyses to explore the women's complex and non-linear pathways, and the importance of structural supports, in order for all women to “transform” post-migration. In Chapter 5, I reflect on how this dissertation contributes to epistemic justice in its disruption of dominant narratives, its legitimization of new language, and its emphasis on community-building as validation and healing in the face of migratory distress.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.022 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".