Data driven mental health and wellbeing support

Oqea

About

Oqea is the first person-centric mental health and wellbeing digital platform and consumer app to connect consumers, health providers, businesses, family and friends all in one private, safe and secure online place. Oqea is designed to be preventative in nature and empowers people of all walks of life to make healthy connections with helpful people, information, and tools anytime, anywhere, when they need them.

https://oqea.com/

https://twitter.com/oqea_au

https://www.facebook.com/oqea.me/

https://www.instagram.com/oqea.me/

https://au.linkedin.com/company/oqea

Project 

The project’s overall goal is to help enhance the wellbeing and mental health of Oqea’s members. Members of Oqea input their own data into the platform (through self-report wellbeing measures, personal preferences), but other de-identified data is/can also be captured (for example wearable sensor, usage, goals, group-level cohort data). The goal of the project is to ensure these data is captured/structured in the most appropriate way, and to identify simple ways to utilise these data to best support members (i.e. by providing personalised recommendations of support; resources, the best matched mental health professionals, etc.).

Working on this project students will:

  • Examine and evaluate current data types, sources, and structures within the Oqea platform

  • Identify optimal ways to evaluate/report individual wellbeing over time

  • Identify and/or label appropriate inputs to drive a recommendation engine for personalised support

  • Identify and/or implement ways to utilise data driven insights to provide support to members

Skills

We are ideally seeking candidates with the following skillsets:

  • Data Science/Data analysis

  • Database organisation

  • Data visualisation (i.e graph-based data analysis)

  • Psychology/mental health workflows understanding

  • Digital systems

  • Literature review skills (desirable: systematic/scoping review experience)

Mode

  • 7 weeks placement

  • Combination of remote and in-person engagement

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