Scaling up psychological treatments: Lessons learned from global mental health.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it