Global Is Local: Leveraging Global Mental-Health Methods to Promote Equity and Address Disparities in the United States
Bibliographic record
Abstract
Structural barriers perpetuate mental health disparities for minoritized US populations; global mental health (GMH) takes an interdisciplinary approach to increasing mental health care access and relevance. Mutual capacity building partnerships between low and middle-income countries and high-income countries are beginning to use GMH strategies to address disparities across contexts. We highlight these partnerships and shared GMH strategies through a case series of said partnerships between Kenya-North Carolina, South Africa-Maryland, and Mozambique-New York. We analyzed case materials and narrative descriptions using document review. Shared strategies across cases included: qualitative formative work and partnership-building; selecting and adapting evidence-based interventions; prioritizing accessible, feasible delivery; task-sharing; tailoring training and supervision; and mixed-method, hybrid designs. Bidirectional learning between partners improved the use of strategies in both settings. Integrating GMH strategies into clinical science-and facilitating learning across settings-can improve efforts to expand care in ways that consider culture, context, and systems in low-resource settings.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".