Developing a holistic policy and intervention framework for global mental health
Bibliographic record
Abstract
INTRODUCTION: There are significant gaps in the accessibility and quality of mental health services around the globe. A wide range of institutions are addressing the challenges, but there is limited reflection and evaluation on the various approaches, how they compare with each other, and conclusions regarding the most effective approach for particular settings. This article presents a framework for global mental health capacity building that could potentially serve as a promising or best practice in the field. The framework is the outcome of a decade of collaborative global health work at the Centre for Addiction and Mental Health (CAMH) (Ontario, Canada). The framework is grounded in scientific evidence, relevant learning and behavioural theories and the underlying principles of health equity and human rights. METHODS: Grounded in CAMH's research, programme evaluation and practical experience in developing and implementing mental health capacity building interventions, this article presents the iterative learning process and impetus that formed the basis of the framework. A developmental evaluation (Patton M.2010. Developmental Evaluation: Applying Complexity Concepts to Enhance Innovation and Use. New York: Guilford Press.) approach was used to build the framework, as global mental health collaboration occurs in complex or uncertain environments and evolving learning systems. RESULTS: A multilevel framework consists of five central components: (1) holistic health, (2) cultural and socioeconomic relevance, (3) partnerships, (4) collaborative action-based education and learning and (5) sustainability. The framework's practical application is illustrated through the presentation of three international case studies and four policy implications. Lessons learned, limitations and future opportunities are also discussed. CONCLUSION: The holistic policy and intervention framework for global mental health reflects an iterative learning process that can be applied and scaled up across different settings through appropriate modifications.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".