Global Mental Health: Interdisciplinary challenges for a field in motion
Bibliographic record
Abstract
In recent years, efforts in Global Mental Health (GMH) have evolved alongside critical engagement with the field's claims and interventions. GMH has shifted its agenda and epistemological underpinnings, increased its evidence base, and joined other global policy platforms such as the Sustainable Development Goals. This editorial introduction to a thematic issue traces the recent shifts in the GMH agenda and discusses the changing construct of “mental health” as GMH moves away from a categorical biomedical model toward dimensional and transdiagnostic approaches and embraces digital technologies. We highlight persistent and emerging lines of inquiry and advocate for meaningful interdisciplinary engagement. Taken together, the articles in this special issue of Transcultural Psychiatry provide a snapshot of current interdisciplinary work in GMH that considers the socio-cultural and historical dimensions of mental health important and proposes reflexive development of interventions and implementation strategies.
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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 | 0.044 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.029 | 0.034 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.024 | 0.048 |
| Insufficient payload (model declined to judge) | 0.008 | 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".