Bibliographic record
Abstract
Global health has been defined as an area of study, research, and practice that places a priority on improving health and achieving equity in health for all people worldwide. This article provides an overview of some central issues in global mental health in three parts. The first part demonstrates why mental health is relevant to global health by examining three key principles of global health: priority setting based on the burden of health problems, health inequalities and its global scope in particular in relation to the determinants and solutions for health problems. The second part considers and addresses the key critiques of global mental health: (a) that the "diagnoses" of mental disorders are not valid because there are no biological markers for these conditions; (b) that the strong association of social determinants undermines the use of biomedical interventions; (c) that the field is a proxy for the expansion of the pharmaceutical industry; and (d) that the actions of global mental health are equivalent to "medical imperialism" and it is a "psychiatric export." The final part discusses the opportunities for the field, piggybacking on the surge of interest in global health more broadly and on the growing acknowledgment of mental disorders as a key target for global health action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".