Lay social workers implementing a task-sharing approach to managing depression in Vietnam
Bibliographic record
Abstract
BACKGROUND: While depression is a leading contributor to burden of disease in Vietnam, there is a critical gap in depression care due to the shortage of mental health specialists and extremely limited mental health services in general health care settings. We have previously reported the effectiveness of a supported self-management (SSM) task-sharing intervention for depression, delivered by social collaborators (lay social workers). The purpose of this study was to identify factors influencing the effectiveness of delivery of SSM by social collaborators and delineate areas for further attention that are relevant for scale-up. METHODS: A hundred and ten (110) key informant interviews were conducted with three stakeholder groups (patients, social collaborators, experts) from eight provinces in Vietnam. Participants were identified through records from a recently completed randomized trial that showed the effectiveness of SSM in community-based settings in Vietnam. Qualitative descriptive methods and thematic analysis were used to examine the interviews. A coding framework and corresponding themes were developed deductively, based on the findings from the randomized trial and the literature, and through inductive analysis, to describe the contextual factors that impacted the social collaborators' role in successfully implementing the SSM intervention. RESULTS: Our analysis identified the following benefits of working with social collaborators: (1) increased awareness of mental health in the family and community; (2) reduced stigma; (3) a better understanding that depression is treatable; (4) increased help-seeking; and (5) improved access to care. There were also significant challenges, including social collaborator characteristics (age, education, pre-existing training and skills) and contextual factors influencing their work (roles and responsibilities, training, compensation, support from government). CONCLUSIONS: Engaging social collaborators in the delivery of SSM in the community can help fill a critical gap in depression care in Vietnam. However, several contextual challenges that are an impediment to increased engagement and sustainable integration into health and social systems need to be resolved through policy change to regulate their practice, define their scope of work, and provide adequate remuneration.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".