Evidence map›Paper›PMID 33487808›Full record

ArticleIndian journal of psychological medicine2020

Impact Evaluation of VKN-NIMHANS-ECHO Model of Capacity Building for Mental Health and Addiction: Methodology of Two Randomized Controlled Trials.

Channaveerachari Naveen Kumar, Prabhat Kumar Chand, Narayana Manjunatha, Suresh Bada Math, Harihara Nagabhushana Shashidhara, Vinay Basavaraju, Jagadisha Thirthalli, Adarsha Alur Manjappa, Rajani Parthasarathy, Pratima Murthy and 9 more

Open access · diamondAbstract read
In one paragraph

Article in Indian journal of psychological medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.4field-weighted citation impact, top 5% of its field
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

15 citing papers in PubMed, 25 citations in OpenAlex.

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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

19 authors at 4 institutions in 2 countries.

Channaveerachari Naveen KumarDept. of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India.
Prabhat Kumar ChandDept. of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India.
Narayana ManjunathaDept. of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India.
Suresh Bada MathDept. of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India.
Harihara Nagabhushana ShashidharaDept. of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India.
Vinay BasavarajuDept. of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India.
Jagadisha ThirthalliDept. of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India.
Adarsha Alur ManjappaDept. of Health and Family Welfare Service, Government of Karnataka, Bengaluru, Karnataka, India.
Rajani ParthasarathyDept. of Health and Family Welfare Service, Government of Karnataka, Bengaluru, Karnataka, India.
Pratima MurthyDept. of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India.
Ferose Azeez IbrahimDept. of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India.
Namrata JagtapDept. of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India.
Sonakshi JyrwaDept. of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India.
Shanivaram ReddyDept. of Psychiatric Social Work, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India.
Sanjeev AroraThe ECHO Institute, University of New Mexico, Albuquerque, NM, USA.
Mary HawkDept. of Behavioral and Community Health Sciences, University of Pittsburgh Graduate School of Public Health.
Supriya KumarDept. of Behavioral and Community Health Sciences, University of Pittsburgh Graduate School of Public Health.
James EganDept. of Behavioral and Community Health Sciences, University of Pittsburgh Graduate School of Public Health.
Margaret McdonaldUniversity of Pittsburgh, Pittsburgh, Pennsylvania, United States.
National Institute of Mental Health and Neurosciences · INUniversity of Pittsburgh · USGovernment of Karnataka · INUniversity of New Mexico · US

Funding

Psychiatric research infrastructure for intervention and implementation in India – TEAMSD43TW009114 · FIC · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Smita N Deshpande, Mary Elizabeth Hawk · 2014 to 2026
$3.2M
Training Program for Psychiatric Genetics in IndiaD43TW006167 · FIC · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI NIMGAONKAR, VISHWAJIT LAXMIKANT · 2002 to 2006
$1.7M
Tri National Training Program in Psychiatric GeneticsD43TW008302 · FIC · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MANSOUR, HADER A., NIMGAONKAR, VISHWAJIT LAXMIKANT · 2009 to 2013
$542k
FIC NIH HHS D43 TW006167FIC NIH HHS D43 TW008302FIC NIH HHS D43 TW009114
6 · The paper itself

Abstract

backgroundBridging the alarming treatment gap for mental disorders in India requires a monumental effort from all stakeholders. Harnessing digital technology is one of the potential ways to leapfrog many known barriers for capacity building. AIM AND CONTEXT: The ongoing Virtual Knowledge Network (VKN)-National Institute of Mental Health and Neurosciences (NIMHANS)-Extension of Community Health Outcomes (ECHO) (VKN-NIMHANS-ECHO: hub and spokes model) model for skilled capacity building is a collaborative effort between NIMHANS and the University of New Mexico Health Sciences Centre, USA. This article aims to summarize the methodology of two randomized controlled trials funded by the Indian Council of Medical Research (ICMR) designed to evaluate the effectiveness of the VKN-NIMHANS-ECHO model of training as compared to training as usual (TAU).

methodsBoth RCTs were conducted in Karnataka, a southern Indian state in which the DMHP operates in all districts. We compared the impact of the following two models of capacity building for the DMHP workforce (a) the VKN-NIMHANS-ECHO model and (b) the traditional method. We use the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) statement to describe the methods of these two trials.Trial 1 is to evaluate the "Effectiveness of addition of Virtual-NIMHANS-ECHO tele-mentoring model for skilled capacity building in providing quality care in alcohol use disorders by the existing staff of DMHP districts of Karnataka." Hub for trial 1 was set up at NIMHANS and the spokes were psychiatrists and other mental health professionals headquartered in the district level office. Trial 2 assesses the implementation and evaluation of the NIMHANS-ECHO blended training program for the DMHP workforce in a rural south-Indian district of Karnataka state. The hub for trial 2 was set up in the district headquarter of Ramanagaram. Hub specialists are DMHP psychiatrists, whereas spokes are the non-doctor workforce (including auxiliary nurse midwives [ANMs] and accredited social health activists [ASHA] workers) medical officers of primary health centers. The location of the HubHub differs in these two studies. Both trials are funded by the ICMR, Government of India. DISCUSSION: Both these trials, though conceptually similar, have some operational differences which have been highlighted. If demonstrated to be effective, this model of telementoring can be generalized and widely merged into the Indian health care system, thus aiding in reducing the treatment gap for patients unable to access care.

Indexed as

Addictioncommunity psychiatrycyberpsychiatryNMHP

Identifiers

PMID33487808
PMCPMC7802042
OpenAlexW3112250858

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.