The CAUSALab is excited to invite you to join us for the 14th Kolokotrones Symposium, Suicide Prevention: How do we know what we know, and how can we know more?, on Friday, November 4, 2022. This event will be hosted both in-person at Harvard T.H. Chan School of Public Health, and virtually via Zoom.
Registration is now open. Information including directions, access to the building, and up-to-date COVID-19 policies will be sent directly to registrants in the weeks leading up to the event.
Please note: In-person attendees MUST be fully vaccinated against COVID-19, as required by Harvard University.
Please reach out to Kathleen Tajmajer (firstname.lastname@example.org) with any questions. We look forward to seeing you there!
Program (All Times in EST)
10:30am: Welcome – Albert Hofman
10:40am: Suicide as a preventable public health problem – Katherine Keyes
11:00am: Interventions to prevent suicide? – Chaired by Barbra Dickerman
• Firearm interventions – Matthew Miller
• Psychosocial interventions – Kate Bentley
• Just-in-time interventions – Matthew Nock
• Medications – Gonzalo Martínez-Alés and Alejandro Szmulewicz
12:45pm: Break for Lunch for In-Person Attendees
1:30pm: Suicide prevention: What’s next and why it hasn’t been done yet? – Philip Wang
1:50pm: Expert panel: Where do we go from here? Research priorities and challenges for suicide prevention – Chaired by Miguel Hernán
Kate Bentley, Katherine Keyes, Mathew Miller, Matthew Nock, Philip Wang
3:00pm: Reception for In-Person Attendees
The Kolokotrones Symposium on Data Science is a bi-annual gathering focused on discussing methodologic issues that arise in data science, including epidemiology and biostatistics. The discussion topics can encompass any aspect of data science, including database management, design of observational analyses, machine learning algorithms, and causal inference techniques.
Do you have ideas for future Kolokotrones symposium topics? If so, we would love to hear from you! Please send any thoughts to Kathleen Tajmajer (email@example.com).