An integrated knowledge translation (iKT) approach to advancing community-based depression care in Vietnam: lessons from an ongoing research-policy collaboration
Bibliographic record
Abstract
BACKGROUND: Evidence-based mental health policies are key to supporting the expansion of community-based mental health care and are increasingly being developed in low and middle-income countries (LMICs). Despite this, research on the process of mental health policy development in LMICs is limited. Engagement between researchers and policy makers via an integrated Knowledge Translation (iKT) approach can help to facilitate the process of evidence-based policy making. This paper provides a descriptive case study of a decade-long policy and research collaboration between partners in Vietnam, Canada and Australia to advance mental health policy for community-based depression care in Vietnam. METHODS: This descriptive case study draws on qualitative data including team meeting minutes, a focus group discussion with research team leaders, and key informant interviews with two Vietnamese policy makers. Our analysis draws on Murphy et al.'s (2021) findings and recommendations related to stakeholder engagement in global mental health research. RESULTS: Consistent with Murphy et al.'s findings, facilitating factors across three thematic categories were identified. Related to 'the importance of understanding context', engagement between researchers and policy partners from the formative research stage provided a foundation for engagement that aligned with local priorities. The COVID-19 pandemic acted as a catalyst to further advance the prioritization of mental heath by the Government of Vietnam. 'The nature of engagement' is also important, with findings demonstrating that long-term policy engagement was facilitated by continuous funding mechanisms that have enabled trust-building and allowed the research team to respond to local priorities over time. 'Communication and dissemination' are also crucial, with the research team supporting mental health awareness-raising among policy makers and the community, including via capacity building initiatives. CONCLUSIONS: This case study identifies factors influencing policy engagement for mental health system strengthening in an LMIC setting. Sustained engagement with policy leaders helps to ensure alignment with local priorities, thus facilitating uptake and scale-up. Funding agencies can play a crucial role in supporting mental health system development through longer term funding mechanisms. Increased research related to the policy engagement process in global mental health will further support policy development and improvement in mental health care in LMICs.
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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 | 0.121 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".