Reflections on the explanations of higher psychosis rates among migrant and ethnic minority populations: A critical discourse analysis
Bibliographic record
Abstract
A growing number of studies suggest that migrant and ethnic minority populations are at higher risk for being diagnosed with psychosis. However, the reasons why have been disputed. This study aims to explore different interpretations of the observed higher rates of psychosis diagnosis among immigrants and ethnic minorities in some parts of the world. We sought to examine these interpretations through a critical lens, acknowledging the social underpinnings of discourses and their power to shape real-world practices. Peer-reviewed editorials, commentaries and letters regarding the topics of interest were retrieved from database searches and subjected to a pattern-based critical discourse analysis. Across a 30-year span of literature, conceptualizations and explanations of higher psychosis rates amongst migrant and minoritized populations evolved in relation to the larger social context, at times opposing one another. Three discursive themes were identified, reflecting intersecting explanations: institutional racism in psychiatry; psychiatry as a scientific discipline that sees and treats all patients equally; and the social locus of high rates. Tensions surrounding psychiatry as a field, including issues of evidence, biological reductionism, and the conceptualization of psychiatric nosological categories have played out within the evolution of this discourse. Exploring how discursive constructions in relation to psychosis and minoritization have been shaped by historical and social factors, we consider the role of local and global dynamics of social power in favouring one explanatory model over another and how these may have affected efforts to prevent and better treat psychosis amongst immigrant and minoritized groups.
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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 | 0.062 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.018 | 0.039 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".