Depression and Global Mental Health in the Global South: A Critical Analysis of Policy and Discourse
Bibliographic record
Abstract
Over the past two decades, depression has become a prominent global public health concern, especially in low- and middle-income countries (LMICs). The World Health Organization (WHO) and the Movement for Global Mental Health have developed international guidelines to improve mental health services globally, prioritizing LMICs. These efforts hold promise for advancing care and treatment for depression and other mental, neurological, and substance abuse disorders in LMICs. The intervention guides, such as the WHO's mhGAP-Intervention Guides, are evidence-based tools and guidelines to help detect, diagnose, and manage the most common mental disorders. Using the Global South as an empirical site, this article draws on Foucauldian critical discourse and document analysis methods to explore how these international intervention guides operate as part of knowledge-power processes that inscribe and materialize in the world in some forms rather than others. It is proposed that these international guidelines shape the global discourse about depression through their (re)production of biopolitical assumptions and impacts, governmentality, and "conditions of possibility." The article uses empirical data to show nuance, complexity, and multi-dimensionality where binary thinking sometimes dominates, and to make links across arguments for and against global mental health. The article concludes by identifying several resistive discourses and suggesting reconceptualizing the treatment gap for common mental disorders.
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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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.023 | 0.104 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.008 | 0.014 |
| 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".