Effective training practices for non-specialist providers to promote high-quality mental health intervention delivery: A narrative review with four case studies from Kenya, Ethiopia, and the United States
Bibliographic record
Abstract
Mental health needs and disparities are widespread and have been exacerbated by the COVID-19 pandemic, with the greatest burden being on marginalized individuals worldwide. The World Health Organization developed the Mental Health Gap Action Programme to address growing global mental health needs by promoting task sharing in the delivery of psychosocial and psychological interventions. However, little is known about the training needed for non-specialists to deliver these interventions with high levels of competence and fidelity. This article provides a brief conceptual overview of the evidence concerning the training of non-specialists carrying out task-sharing psychosocial and psychological interventions while utilizing illustrative case studies from Kenya, Ethiopia, and the United States to highlight findings from the literature. In this article, the authors discuss the importance of tailoring training to the skills and needs of the non-specialist providers and their roles in the delivery of an intervention. This narrative review with four case studies advocates for training that recognizes the expertise that non-specialist providers bring to intervention delivery, including how they promote culturally responsive care within their communities.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".