Protocol for process evaluation of SMART Mental Health cluster randomised control trial: an intervention for management of common mental disorders in India
Bibliographic record
Abstract
INTRODUCTION: In India about 95% of individuals who need treatment for common mental disorders like depression, stress and anxiety and substance use are unable to access care. Stigma associated with help seeking and lack of trained mental health professionals are important barriers in accessing mental healthcare. Systematic Medical Appraisal, Referral and Treatment (SMART) Mental Health integrates a community-level stigma reduction campaign and task sharing with the help of a mobile-enabled electronic decision support system (EDSS)-to reduce psychiatric morbidity due to stress, depression and self-harm in high-risk individuals. This paper presents and discusses the protocol for process evaluation of SMART Mental Health. METHODS AND ANALYSIS: The process evaluation will use mixed quantitative and qualitative methods to evaluate implementation fidelity and identify facilitators of and barriers to implementation of the intervention. Case studies of six intervention and two control clusters will be used. Quantitative data sources will include usage analytics extracted from the mHealth platform for the trial. Qualitative data sources will include focus group discussions and interviews with recruited participants, primary health centre doctors, community health workers (Accredited Social Health Activits) who participated in the project and local community leaders. The design and analysis will be guided by Medical Research Council framework for process evaluations, the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework, and the normalisation process theory. ETHICS AND DISSEMINATION: The study has been approved by the ethics committee of the George Institute for Global Health, India and the Institutional Ethics Committee, All India Institute of Medical Sciences (AIIMS), New Delhi. Findings of the study will be disseminated through peer-reviewed publications, stakeholder meetings, digital and social media platforms. TRIAL REGISTRATION NUMBER: CTRI/2018/08/015355.
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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 | 0.111 | 0.145 |
| Meta-epidemiology (narrow) | 0.010 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.135 | 0.028 |
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".