Evaluating the delivery of Problem Management Plus in primary care settings in rural Rwanda: a study protocol using a pragmatic randomised hybrid type 1 effectiveness-implementation design
Bibliographic record
Abstract
INTRODUCTION: Evidence-based low-intensity psychological interventions such as Problem Management Plus (PM+) have the potential to expand treatment access for depression and anxiety, yet these interventions are not yet effectively implemented in rural, public health systems in resource-limited settings. In 2017, Partners In Health adapted PM+ for delivery by primary care nurses in rural Rwanda and began integrating PM+ into health centres in collaboration with the Rwandan Ministry of Health, using established implementation strategies for mental health integration into primary care (Mentoring and Enhanced Supervision at Health Centers for Mental Health (MESH MH)). A gap in the evidence regarding whether low-intensity psychological interventions can be successfully integrated into real-world primary care settings and improve outcomes for common mental disorders remains. In this study, we will rigorously evaluate the delivery of PM+ by primary care nurses, supported by MESH MH, as it is scaled across one rural district in Rwanda. METHODS AND ANALYSIS: We will conduct a hybrid type 1 effectiveness-implementation study to test the clinical outcomes of routinely delivered PM+ and to describe the implementation of PM+ at health centres. To study the clinical effectiveness of PM+, we will use a pragmatic, randomised multiple baseline design to determine whether participants experience improvement in depression symptoms (measured by the Patient Health Questionnaire-9) and functioning (measured by the WHO-Disability Assessment Scale Brief 2.0) after receiving PM+. We will employ quantitative and qualitative methods to describe and evaluate PM+ implementation outcomes using the Reach, Effectiveness, Adoption, Implementation and Maintenance framework, using routinely collected programme data and semistructured interviews. ETHICS AND DISSEMINATION: This evaluation was approved by the Rwanda National Ethics Committee (Protocol #196/RNEC/2019) and deemed exempt by the Harvard University Institutional Review Board. The results from this evaluation will be useful for health systems planners and policy-makers working to translate the evidence base for low-intensity psychological interventions into practice.
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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 | 0.088 | 0.064 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".