Global mental health: A call to action.
Bibliographic record
Abstract
Mental health needs have been recognized as a priority area by the World Health Organization (WHO), and a Comprehensive Mental Health Action Plan (2013) was proposed to address the needs of millions of people around the world. Concerns have been raised about the degree to which current global efforts are appropriate and sufficient for promoting mental health (MH), reducing the risk for common MH disorders, and addressing the needs of individuals experiencing mental illness. This commentary expands on the presentation of the Global Alliance for Behavioral Health and Social Justice's Task Force on Global Mental Health at the 16th Biennial Conference of the Society for Community Research and Action, held in Ottawa, Ontario, Canada June 21-24, 2017, "Building Capacity to Address Mental Illness and Emotional Distress in Low-Resource Settings and Among Refugee Populations." Utilizing a socioecological framework, this commentary offers a call to action in addressing global mental health by emphasizing the need for greater investments in wellness promotion, prevention, treatment, and recovery. Importantly, such efforts need to value local knowledge and culture, harness natural existing resources and assets, and ensure equitable distribution of key resources for MH. (PsycINFO Database Record (c) 2019 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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.036 | 0.039 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".