Client experiences of a task-shifting supported self-management intervention for depression in Vietnam
Bibliographic record
Abstract
Abstract Background The global burden of mental illness is substantial, with depression impacting close to 300 million people worldwide. This has been exacerbated within the context of the COVID-19 pandemic. Yet, in many low- and middle-income countries including Vietnam, there is a substantial treatment gap, with many requiring mental health care unable to access it. Task-shifting is an evidence-based approach that seeks to address this gap by utilizing non-specialist providers to provide care. While there is a large body of literature exploring task-shifting, there is little that explores the client experience. This paper describes the facilitators and barriers impacting the client experience of a task-shifting supported self-management (SSM) intervention for depression in Vietnam. SSM involves a client workbook and supportive coaching by non-specialist providers. Methods This paper is situated within a randomized controlled trial that demonstrated the effectiveness of the SSM intervention in adult populations across eight provinces in Vietnam. Semi-structured interviews were conducted with a convenience sample of clients (recipients of the intervention) with depression caseness as measured by the Self-Report Questionnaire-20 depression screening measure, and providers (non-specialist “social collaborators”) to explore SSM’s acceptability and factors influencing participation and adherence. This paper presents the qualitative findings from an analysis of the interviews, focusing on the client perspective. Qualitative descriptive methods and thematic analysis were used. Results Forty-five clients were interviewed. Sub-themes reported for the facilitators and benefits for the client experience of the SSM intervention were client-provider relationship building and family and community connections. Sub-themes reported for the barriers were clients’ responsibilities, clients’ health conditions, and consequences of stigma. Conclusions Due to challenges with sustaining and scaling up the in-person SSM intervention in Vietnam, the research team has pivoted to delivering the SSM intervention digitally through a smartphone-based app adapted from SSM, with direction from the Government of Vietnam. Findings from this study suggest that while digital interventions may support accessibility and convenience, they may neglect the critical human contact component of mental health care. Ultimately, a model that combines digital delivery with some form of human contact by a support person may be important.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".