Effectiveness and cost-effectiveness of a community intervention in enhancing access to care and improving clinical outcomes for depression: a protocol for a cluster randomised controlled trial in India
Bibliographic record
Abstract
BACKGROUND: Although depression is the leading cause of disability worldwide, treatment coverage for the condition is inadequate. Supply-side barriers (e.g. shortage of specialist mental health professionals) and demand-side barriers (e.g. lack of awareness about depression) lead to limited availability of evidence-based interventions, poor demand for care, and low levels of adherence to care. The aim of our study is to examine if the addition of a community intervention delivered by community volunteers enhances the population-level impact of an evidence based psychosocial intervention (Healthy Activity Program [HAP]) in routine primary care by increasing demand for HAP and improving HAP adherence and effectiveness. METHODS: A hybrid type 2 effectiveness implementation cluster randomised controlled trial will be implemented in the state of Goa, India. Twenty-eight clusters of villages and their associated public sector health centres will be randomly allocated through restricted randomisation. Clusters will be randomly allocated to the 'Community Model' or 'Facility Model' arms. All clusters will offer the HAP and clusters in the 'Community Model' arm will additionally receive activities delivered by community volunteers ("Sangathis") to increase awareness about depression and support demand for and adherence to HAP. The primary outcomes are Contact Coverage (Patient Health Questionnaire [PHQ-9] score > 4 as a proportion of those screened) and Effectiveness Coverage (mean PHQ-9 score amongst those who score ≥ 15 at baseline, i.e. those who have moderately severe to severe depression) at 3 months post-recruitment. Additional outcomes at 3 and 6 months will assess sustained effectiveness, remission, response to treatment, depression awareness, social support, treatment completion, and activation levels. Economic and disability outcomes will be assessed to estimate incremental cost-effectiveness ratios. Implementation will be evaluated through process data and qualitative data informed by the RE-AIM framework. A minimum of 79488 primary care attenders will be screened for the Contact Coverage outcome, and 588 individuals with PHQ-9 ≥ 15 will be recruited for the Effectiveness Coverage outcome. DISCUSSION: If effective, our community intervention will have relevance to India's Ayushman Bharat universal healthcare programme which is scaling up care for depression in primary care, and also to other low- and middle- income countries. TRIAL REGISTRATION: Registered on ClincalTrials.gov ( NCT05890222 .) on 12/05/2023.
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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 | 0.043 | 0.035 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".