Strategic universality in the making of global guidelines for mental health
Bibliographic record
Abstract
Based on interviews with members of the Guideline Development Group (GDG) of the World Health Organization's (WHO) Mental Health Gap Action Programme (mhGAP) Guidelines for Mental, Neurological and Substance Use Disorders, this article adds empirical depth to understanding the contingent and strategic nature of universality in relation to mental health. Differently from debating whether or not mental health is global, the article outlines the people, ideas, and processes involved in making it global. Thematic analysis of interviews carried out with nine (out of 21) members of the original mhGAP GDG identified six intersecting strategies that enable the construction of universality in global mental health (GMH): 1) processes and practices of assembling expertise; 2) decisions on what counts as evidence; 3) framing cultural relativism as nihilistic; 4) the delaying of complexity to prioritize action; 5) the narration of tensions as technical rather than epistemological; and 6) the ascription of messiness to local contexts rather than to processes of standardization. Interviews showed that differently from the public-facing consensus often presented in GMH, GDG members hold contrasting and contingent understandings of the nature of universality in relation to mental health diagnoses and interventions. Thus, the universality of mental health achieved through the mhGAP Guidelines is partial and temporary, requiring continuous (re)iteration. 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.
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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 | 0.251 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.070 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.007 | 0.012 |
| 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".