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Abhijit Nadkarni on Muck Rack

Abhijit Nadkarni

United Kingdom
As seen in: Nature, Wiley Online Library, Memory Studies, PLOS, PLOS Medicine, SAGE Publishing, The New Indian Express, ThePrint
NIHR Professor of Global Research @LSHTM UK; Co-Director, Centre for Global Mental Health, @LSHTM; Director-@SangathARG, India
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Articles

Effectiveness and cost-effectiveness of digital approaches for training frontline health workers in depression care: a randomized controlled trial

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By John A Naslund, Deepak Tugnawat, Aditya Anand, Zafra Cooper, Sona Dimidjian, Christopher G Fairburn, Bethany L. Hedt-Gauthier, Steven Hollon, Udita Joshi, Azaz Khan, Chunling Lu, Lauren Mitchell, Shital Muke, Abhijit Nadkarni, Carina Akemi Nakamura, Rohit Ramaswamy, Ritu Shrivastava, Abhishek Singh, Daisy R. Singla, Anant Bhan, Vikram Patel, Juliana L. Restivo Haney, G. Sai Teja
| Nature Verified
Abstract Task-sharing psychosocial interventions to frontline health workers can expand access to mental health care. However, training remains a major barrier to scale. Here, in this randomized controlled trial, we evaluated the effectiveness and cost-effectiveness of two digital training approaches compared with classroom-based training for improving knowledge to deliver behavioral activation for depression.
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Unpacking the ‘black box’ of suicide: A latent class analysis predicting profiles of suicidal ideation in a longitudinal cohort of adolescent girls from India

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By Anushka Patel, Kelly Dixon, Abhijit Nadkarni
| PLOS
Loading metrics Open Access Peer-reviewed Research Article Citation: Patel AR, Dixon KE, Nadkarni A (2024) Unpacking the ‘black box’ of suicide: A latent class analysis predicting profiles of suicidal ideation in a longitudinal cohort of adolescent girls from India. PLOS Glob Public Health 4(5): e0003130.
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Determinants of alcohol use among young males in two Indian states: A population‐based study

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By Abhijit Nadkarni, Benjamin Palafox, Grigsby TJ
| Wiley Online Library Verified
Modelling approach Multi-level, mixed-effect logistic regression models were developed to assess the determinants of each outcome (using Stata version 16.1). First, we analysed crude associations between the set of exposure variables and each outcome, controlling for age a priori as a known confounder for many exposures [19, 23]. We then constructed fully-adjusted models using a backward stepwise approach (Appendix ) [33, 34].
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