Experiences and socio-environmental contexts in the lead-up to psychosis: a qualitative analysis of the narratives of persons with psychosis from different ethnic, racial and immigrant backgrounds
Bibliographic record
Abstract
Introduction: Previous research, predominantly quantitative, has attributed the excess risk for psychosis among immigrants and ethnic minorities to social adversity, discrimination and structural inequities. Although calls have acknowledged their potential for yielding nuanced insights, qualitative methods focused on first-person narratives have not been used in research into the social determinants of the development of psychosis. Methods: We explored the experiences and socio-environmental contexts of individuals with psychosis from diverse ethno-racial and immigrant backgrounds. We also gathered their perspectives on the causality of psychosis. We conducted in-depth interviews with 24 participants at early intervention services for psychosis in Montreal, Canada. Results: Through thematic analysis, we identified five themes: "Spaces and societies of oppression"; "Nothing to hold on to"; "Mistreated, invisible or seen in the wrong light"; "Places of freedom, connection and safety"; and "Healing and well-being". Spaces described as oppressive fomented experiences of precarity, isolation and mistreatment. Spaces of freedom, connection and inclusivity enabled healing and well-being. Experiences of precarity, mistreatment and exclusion were more frequent for minoritized individuals. Participants attributed psychosis to multiple factors, many pertaining to social contexts. Discussion: Our findings shed light on the processes through which social contexts shape the lives and illness development of individuals from diverse backgrounds. By framing them within particular life stories and places, we gain a fuller, more fine-grained understanding of the social-structural determinants that have been identified in quantitative studies. Our work highlights the need to attend closely to patients' social contexts and narratives and advocate for inclusivity, equity, and connection at the societal level.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".