Evidence map›Paper›PMID 35715180›Full record

ArticleBMJ open2022

Protocol for process evaluation of SMART Mental Health cluster randomised control trial: an intervention for management of common mental disorders in India.

Ankita Mukherjee, Mercian Daniel, Sudha Kallakuri, Amanpreet Kaur, Siddhardha Devarapalli, Usha Raman, Graham Thornicroft, Beverley M Essue, D Praveen, Rajesh Sagar and 5 more

Abstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Ankita MukherjeeThe George Institute for Global Health, New Delhi, India.ORCID 0000-0002-6236-1317
Mercian DanielThe George Institute for Global Health, New Delhi, India.
Sudha KallakuriThe George Institute for Global Health, Hyderabad, India.
Amanpreet KaurThe George Institute for Global Health, New Delhi, India.
Siddhardha DevarapalliThe George Institute for Global Health, Hyderabad, India.
Usha RamanDepartment of Communication, University of Hyderabad, Hyderabad, India.
Graham ThornicroftCentre for Global Mental Health Centre for Implementation Science, Health Service and Population Research Department, King's College London Institute of Psychiatry Psychology and Neuroscience, London, UK.
Beverley M EssueInstitute of Health Policy, Management and Evaluation, University of Toronto Dalla Lana School of Public Health, Toronto, Ontario, Canada.
D PraveenThe George Institute for Global Health, Hyderabad, India.
Rajesh SagarDepartment of Psychiatry, All India Institute of Medical Sciences, New Delhi, India.
Shashi KantCentre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India.
Shekhar SaxenaDepartment of Global Health and Population, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA.
Anushka PatelThe George Institute for Global Health, UNSW Sydney, SydneyAustralia.
David PeirisThe George Institute for Global Health, UNSW Sydney, SydneyAustralia.ORCID 0000-0002-6898-3870
Pallab K MaulikThe George Institute for Global Health, New Delhi, India pmaulik@georgeinstitute.org.in.

Funding

Medical Research Council MR/S001255/1
6 · The paper itself

Abstract

introductionIn 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.

Indexed as

Mental DisordersMental HealthCommunity Health WorkersHumansIndiaRandomized Controlled Trials as TopicReferral and ConsultationClinical trialsDepression & mood disordersMENTAL HEALTHPRIMARY CAREProtocols & guidelinesSuicide & self-harm

Identifiers

PMID35715180
PMCPMC9207925

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.