However, upon further consideration, the team recognized that there have been numerous asks of the community for their participation in community listening sessions. We planned a follow-up listening session that would focus on creatively designed access to mental health care for Black youth and their families. Participants were given additional opportunities to be a part of this project, including joining the Codesign team, participating in one-on-one interviews, or being a part of a Community Accountability Council. They shared their struggles and their hopes for change while providing insightful suggestions. Eleven caregivers attended the first listening session and shared stories of seeking care at Seattle Children’s Hospital. Although numbers do not tell the whole story, it gives us an idea of the needs that are and are not being met.įrom the information gathered, the Codesign team decided to focus on the Black community and identify policies and programs that could be re-designed to better serve the needs of Black families seeking mental health care at SCH.Ĭaregiver listening sessions were held to further inform this process. The Codesign team gathered and examined data from community needs assessments, SCH emergency department data, outpatient data, and conversations with Odessa Brown clinic, to identify communities who consistently showed poor mental health outcomes and low access to care. The purpose was to collaboratively re-design mental health services and bring about much-needed change to improve access to mental health services at Seattle Children’s Hospital. See the next section.Seattle Children’s Hospital (SCH) staff, leadership, and community leaders came together to create a Codesign team, facilitated by CoLab for Community and Behavioral Health Policy. %md: Allows you to include various types of documentation, including text, images, and mathematical formulas and equations. For more information, see How to work with files on Databricks. For example, to run the dbutils.fs.ls command to list files, you can specify %fs ls instead. %fs: Allows you to use dbutils filesystem commands. To run a shell command on all nodes, use an init script. This command runs only on the Apache Spark driver, and not the workers. To fail the cell if the shell command has a non-zero exit status, add the -e option. ![]() ![]() %sh: Allows you to run shell code in your notebook. Notebooks also support a few auxiliary magic commands: REPLs can share state only through external resources such as files in DBFS or objects in object storage. Variables defined in one language (and hence in the REPL for that language) are not available in the REPL of another language. When you invoke a language magic command, the command is dispatched to the REPL in the execution context for the notebook. This includes those that use %sql and %python. If your notebook contains more than one language, only SQL and Python cells are formatted. This includes those that use %sql and %python.įormat all Python and SQL cells in the notebook If you select cells of more than one language, only SQL and Python cells are formatted. Select multiple cells and then select Edit > Format Cell(s). Notebook Edit menu: Select a Python or SQL cell, and then select Edit > Format Cell(s). This menu item is visible only in Python notebook cells or those with a %python language magic. This menu item is visible only in SQL notebook cells or those with a %sql language magic.įormat Python cell: Select Format Python in the command context dropdown menu of a Python cell. You can trigger the formatter in the following ways:įormat SQL cell: Select Format SQL in the command context dropdown menu of a SQL cell. You must have Can Edit permission on the notebook to format code. ![]() Use the Databricks notebook and file editor. ![]() Open or run a Delta Live Tables pipeline from a notebook.
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