This paper is in the following e-collection/theme issue:

Users' and Patients' Needs for Mental Health Services (378) Infodemiology and Infoveillance (1270) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1010) e-Mental Health and Cyberpsychology (1388)

Published on in Vol 10 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43253, first published .
Leveraging Symptom Search Data to Understand Disparities in US Mental Health Care: Demographic Analysis of Search Engine Trace Data

Leveraging Symptom Search Data to Understand Disparities in US Mental Health Care: Demographic Analysis of Search Engine Trace Data

Leveraging Symptom Search Data to Understand Disparities in US Mental Health Care: Demographic Analysis of Search Engine Trace Data

Authors of this article:

Ben Rochford1 Author Orcid Image ;   Sachin Pendse1 Author Orcid Image ;   Neha Kumar1 Author Orcid Image ;   Munmun De Choudhury1 Author Orcid Image
Article Authors Cited by (5) Tweetations (20) Metrics

Journals

  1. Li J, He Z, Zhang M, Ma W, Jin Y, Zhang L, Zhang S, Liu Y, Ma S. Estimating Rare Disease Incidences With Large-scale Internet Search Data: Development and Evaluation of a Two-step Machine Learning Method. JMIR Infodemiology 2023;3:e42721 View
  2. Sinha G, Larrison C, Brooks I. Twitter sentiments and mental health services in the United States. Social Work in Mental Health 2024;22(1):91 View
  3. Pendse S, Kumar N, De Choudhury M. Marginalization and the Construction of Mental Illness Narratives Online: Foregrounding Institutions in Technology-Mediated Care. Proceedings of the ACM on Human-Computer Interaction 2023;7(CSCW2):1 View
  4. Monteith S, Glenn T, Geddes J, Whybrow P, Achtyes E, Bauer M. Implications of Online Self-Diagnosis in Psychiatry. Pharmacopsychiatry 2024;57(02):45 View
  5. Scutari M, Kerob D, Salah S. Inferring skin–brain–skin connections from infodemiology data using dynamic Bayesian networks. Scientific Reports 2024;14(1) View