Decolonizing psychiatric diagnosis: Turning the Diagnostic and Statistical Manual of Mental Disorders on its head.
Bibliographic record
Abstract
The conception of this article came to us at the end of a land-based healing program informed by Indigenous approaches to wellness. In this article, we dismantle psychiatric diagnosis, particularly the Diagnostic and Statistical Manual of Mental Disorders (DSM). Drawing on Frantz Fanon's notion of sociodiagnosics, we put DSM diagnostic categories under a sociogenic microscope. We assert that the DSM and psychologizing discourses are cultural products born out of coloniality, which continue to serve as tools for the subjugation of iyiniwak (Indigenous peoples), a phenomenon termed psycholonization. After setting our intentions and describing Fanon's sociodiagnostics, we will examine various disorders and symptomatology from a decolonial lens. By using the very language of the DSM, we make visible and "diagnose" the colonial logics and ideologies inherent in these categories. This includes addiction to, and obsessions with, excessive material wealth and power, which has justified the dispossession of iyiniwak land and now is causing a climate crisis that threatens humanity and all our relations. We assert that these colonial logics and ideologies are pathogenic not only for iyiniwak but also for settlers and all people. In the second section, we recenter vastly different worldviews that underpin Indigenous approaches to "assessment" and "diagnosis," including a nonlinear understanding of time, listening to and engaging wisdoms, and the acknowledgment of diversity and divergence as a given that is celebrated and honored. We end this article by addressing the importance of conceptual humility to rectify epistemic violence that is at the core of jagged diagnostic worldviews colliding. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.034 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".