Global mental health and psychosocial support programming: An expert review of major implementation and funding challenges
Bibliographic record
Abstract
The global mental health (GMH) field aims to equitably improve mental health and well-being everywhere. This article reviews persistent common challenges hindering sustained, high-quality delivery of mental health and psychosocial support (MHPSS). Our focus is on programming that is funded or implemented by external organizations, typically universities or international non-governmental organizations from high-income countries. It is a consensus statement of MHPSS practitioners, programmers and researchers working for these organizations and some who are locally based who observe these programs in action. We comment on progress to date, barriers and recommendations for change and the importance of promoting sustained integration of MHPSS into health and social service systems through a comprehensive, recovery-oriented system of care. We call for prioritizing often-neglected issues (e.g., stigma, severe mental health conditions and neurodevelopmental conditions), strengthening workforce training and supervision and monitoring and evaluation systems to ensure program quality. The continued dominance of the Global North in shaping GMH programming priorities remains a concern. We advocate for a greater involvement of local workers and communities in agenda-setting for programs, culturally grounded implementation and long-term capacity building. Evidence-based practices must be met with contextual relevance, and comprehensive guidelines for sustained support are needed for development settings. For persistent funding challenges, we recommend clearer funder objectives, investment in in-house mental health expertise and funder coordination with prioritization of complementary programming. These recommendations are essential to realizing equitable, comprehensive, evidence-based and contextually grounded GMH programming.
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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 | 0.059 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".