Evidence map›Paper›PMID 31992906›Full record

ArticleJournal of neurosciences in rural practice2019

Telemedicine-Based Tobacco Treatment Model in Primary Care from a Low-Resource Setting.

Kumar Thamaraiselvan Santhosh, Mani Bhushan Pant, Fareed Uzzafar, Narayana Manjunatha, C Naveen Kumar, Suresh Bada Math

Open access · goldAbstract read
In one paragraph

Article in Journal of neurosciences in rural practice, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 26% 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

8 citing papers in PubMed, 9 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

6 authors at 1 institution in 1 country.

Kumar Thamaraiselvan SanthoshTelemedicine Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India.
Mani Bhushan PantTelemedicine Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India.
Fareed UzzafarTelemedicine Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India.
Narayana ManjunathaTelemedicine Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India.
C Naveen KumarTelemedicine Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India.
Suresh Bada MathTelemedicine Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India.
National Institute of Mental Health and Neurosciences · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tobacco addiction is one of the leading causes of premature mortality. Early and effective intervention in primary care, though possible and feasible, is seldom done in India. This case series describes the diagnosis and management of three patients of tobacco addiction by a primary care doctor (PCD) who is being trained in digitally driven four modules based "Primary Care Psychiatry Program" of National Institute of Mental Health and Neurosciences, Bengaluru, India. This article discusses about the way in which two modules (telepsychiatric on-consultation training and collaborative video consultation) helped a primary care doctor (M.B.P.) working approximately 1,500 miles away from an academic institute to treat tobacco dependence at a rural primary health center of India.

Indexed as

addictiondependenceIndianicotineprimary caretelemedicinetobacco

Identifiers

PMID31992906
PMCPMC6984966
OpenAlexW2995982981

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.