Scaling up psychological treatments: Lessons learned from global mental health.
Bibliographic record
Abstract
Evidence-based psychological treatments are among the most effective interventions in medicine and are recommended as the first line of treatment to address the significant burden of depression, anxiety, and stress-related disorders worldwide. Despite this evidence, these treatments remain inaccessible for the great majority of the world's population. Global Mental Health (GMH) is an evolving discipline of research and practice that places a priority on improving mental health and achieving equity in mental health for all people worldwide. Equity is a driving principle, and this recognizes that inequalities exist within all nations and between nations. At the heart of this equity, there is the need for person-centered care. This essay discusses how GMH has sought to address a range of barriers to scale up the delivery of psychological treatments for common mental disorders. While the initial focus of the field has been to address access to quality care in low- and middle-income countries, this article also draws attention to how similar strategies are being implemented at scale in some high-income countries, with appropriate modifications to suit the context. In considering some of these evidence-based, contextually driven strategies, psychological communities have potential to address the growing burden of depression and anxiety worldwide. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".