
Me & Autism: Building a Space That Says Yes
Navigating a world not built for her, Dr. Jilleun Tenning found belonging by creating InFocus, a space that celebrates neurodivergence so thers can too.

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Navigating a world not built for her, Dr. Jilleun Tenning found belonging by creating InFocus, a space that celebrates neurodivergence so thers can too.
VICTORIA, BC (October 22, 2024) – HiBoop, a new mental health assessment platform designed to assist healthcare providers, has launched its Clinical Pilot Program in partnership with The Healing Institute.
One Life Recovery, founded by Brett Johnson, seeks urgent community support after a devastating fire displaced residents and destroyed belongings at their Victoria recovery home. Learn how you can help these men continue their recovery journey through donations and support.
Mental health diagnostics have traditionally relied on in-office assessments that can be long, repetitive, and sometimes feel impersonal. These standardized questionnaires are valuable tools, but they often follow a "one-size-fits-all" approach, which doesn’t reflect the complexity of each individual’s mental health journey.
Mental health assessments are essential for identifying conditions such as anxiety, ADHD, and trauma. However, traditional assessments can be lengthy and repetitive, leading to question fatigue—when individuals feel overwhelmed, disengaged, or frustrated by the sheer number of questions. This fatigue can compromise the accuracy of responses and, ultimately, the quality of the diagnosis.
Machine learning (ML) is reshaping mental health diagnostics by improving the accuracy of assessments and enabling early interventions. However, applying ML in mental healthcare requires careful consideration of privacy, user consent, and emotional impact. Unlike artificial intelligence (AI) systems that attempt to mimic human interactions, HiBoop's approach focuses on controlled machine learning—using data-driven insights to enhance existing, proven assessments without replacing the human element. In this article, we explore the ethical considerations guiding the responsible use of machine learning in mental health assessments and highlight how HiBoop balances innovation with empathy and privacy.
Mental health diagnostics are evolving through machine learning (ML), enabling faster, more accurate assessments and reducing the time to diagnosis. This post explores how HiBoop's innovative use of controlled ML enhances clinical assessments, minimizes question fatigue, and prioritizes data privacy. We also highlight how the platform supports conditions such as anxiety, ADHD, and trauma, with a focus on personalized care and adherence to global data security standards.