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

Patient Engagement and Empowerment (129) Peer-to-Peer Support and Online Communities (836) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1970) Participatory Medicine & E-Patients (897) Basic Cancer Research (19) Cancer Epidemiology, Cancer Surveillance and Infodemiology (129) Emotional, Social, Psychological Support for Cancer (272) Participatory Cancer Care (122)

Published on in Vol 17 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68852, first published .
Engagement Methods in Brain Tumor Genomic Research: Multimethod Comparative Study

Engagement Methods in Brain Tumor Genomic Research: Multimethod Comparative Study

Engagement Methods in Brain Tumor Genomic Research: Multimethod Comparative Study

Authors of this article:

Matthew DeCamp1, 2 Author Orcid Image ;   Juliana G Barnard3, 4 Author Orcid Image ;   Carly Ritger3 Author Orcid Image ;   Laura J Helmkamp3 Author Orcid Image ;   Anowara Begum3 Author Orcid Image ;   Sandra Garcia-Hernandez3 Author Orcid Image ;   Rudy Fischmann5 Author Orcid Image ;   Nestelynn Gay5 Author Orcid Image ;   Ricardo Gonzalez-Fisher5 Author Orcid Image ;   Kevin C Johnson5, 6 Author Orcid Image ;   Lindsay A Lennox7 Author Orcid Image ;   Guy R Lipof5 Author Orcid Image ;   Jasmyn Ostmeyer5 Author Orcid Image ;   Ifeoma Perkins1, 8 Author Orcid Image ;   Laura Pyle4 Author Orcid Image ;   Liz Salmi9 Author Orcid Image ;   Talia Thompson3, 4 Author Orcid Image ;   Elizabeth B Claus10, 11 Author Orcid Image ;   Roel Verhaak6, 12 Author Orcid Image ;   Bethany M Kwan3, 7 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Kwan B, Barnard J, Ritger C, Staton E, Perkins I, Gonzalez-Fisher R, Lennox L, Gay N, Johnson K, Verhaak R, Claus E, DeCamp M, Salmi L. Community perspectives on the return of research results and ownership of data and specimens for brain tumor genomic research. SSM - Qualitative Research in Health 2026;9:100741 View
  2. Wu T, Han T, Zhang X, Dai Y, Meng X. A Telemedicine App for Nonrigid Facial Rehabilitation Training Enhanced by Efficient Fully Convolutional Neural Network With Residual Network (EffiFCNN-ResNet) to Improve Accessibility for Patients With Nasopharyngeal Carcinoma Cancer: Randomized Controlled Trial. JMIR mHealth and uHealth 2026;14:e72560 View