Toward a new architecture for global mental health
Bibliographic record
Abstract
Current efforts in global mental health (GMH) aim to address the inequities in mental health between low-income and high-income countries, as well as vulnerable populations within wealthy nations (e.g., indigenous peoples, refugees, urban poor). The main strategies promoted by the World Health Organization (WHO) and other allies have been focused on developing, implementing, and evaluating evidence-based practices that can be scaled up through task-shifting and other methods to improve access to services or interventions and reduce the global treatment gap for mental disorders. Recent debates on global mental health have raised questions about the goals and consequences of current approaches. Some of these critiques emphasize the difficulties and potential dangers of applying Western categories, concepts, and interventions given the ways that culture shapes illness experience. The concern is that in the urgency to address disparities in global health, interventions that are not locally relevant and culturally consonant will be exported with negative effects including inappropriate diagnoses and interventions, increased stigma, and poor health outcomes. More fundamentally, exclusive attention to mental disorders identified by psychiatric nosologies may shift attention from social structural determinants of health that are among the root causes of global health disparities. This paper addresses these critiques and suggests how the GMH movement can respond through appropriate modes of community-based practice and ongoing research, while continuing to work for greater equity and social justice in access to effective, socially relevant, culturally safe and appropriate mental health care on a global scale.
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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 | 0.020 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.081 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 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".