The National Collaborating Centre for Methods and Tools (NCCMT) is looking for new members to join its National Advisory Board. This is a great opportunity to contribute to public health decision-making in Canada and support evidence-informed practices.

The NCCMT is one of six National Collaborating Centres for Public Health in Canada, established in 2005 in response to the SARS epidemic. Hosted by McMaster University’s School of Nursing, it provides tools, training, and mentorship to help public health professionals integrate research into practice.

Role of the National Advisory Board

As a member of the National Advisory Board, you will:

• Attend semi-annual virtual meetings: Engage in discussions that shape NCCMT initiatives.
• Analyze public health needs: Identify opportunities where the NCCMT can offer support.
• Champion EIDM: Advocate for evidence-based practices and the NCCMT’s mission.

The commitment involves approximately 6–8 hours annually over a three-year term.

Seeking Diverse Perspectives

To ensure a comprehensive approach to public health challenges, the NCCMT is particularly interested in individuals with expertise in:

• First Nations, Inuit, and Métis health
• 2SLGBTQIA+ communities
• Health in communities of colour
• Rural public health
• Environmental health
• Local/regional public health practice
• Middle management
• Early- and mid-career professionals

How to Apply

To express your interest, please email nccmt@mcmaster.ca with the following:

• Full name and contact details
• A one-page summary: Detail your professional background, expertise, and interest in joining the National Advisory Board. Highlight any relevant perspectives from the list above.
• Relevant affiliations or memberships

This is your chance to contribute to the advancement of public health in Canada by guiding an organization committed to integrating evidence into practice. Join the NCCMT’s National Advisory Board and be a catalyst for positive change.

For more information about the NCCMT and its initiatives, contact nccmt@mcmaster.ca or visit www.nccmt.ca.

📢 Exciting News! The first-ever Canada-Africa Academic Collaborations Conference (CAACC) 2025 is set to take place on February 27, 2025. This virtual event will bring together scholars, researchers, and professionals from Canada and Africa to foster meaningful academic partnerships.

With the theme “Academic Collaboration for Posterity,” this virtual conference will create a dynamic platform for discussing research, knowledge-sharing, and cooperative initiatives including education, policy, climate, and health that can drive sustainable development in both regions. The event is co-chaired by Pauline Omolo (University of Nairobi, Kenya) and Seyram Afealete (Western University, Canada) and promises an engaging program featuring keynote addresses, panel discussions, and thematic presentations.

📅 Date: February 27, 2025
⏰ Time: 8:45 AM – 3:25 PM (EST)
📍 Location: Online (Zoom Webinar)
🔗 More details & registration: The Africa Institute – CAACC 2025

Grounded Roots, Open Wings (GROW) is seeking two dedicated and passionate individuals to join its Board of Directors for a three-year term. GROW provides comprehensive pre- and postnatal care to individuals in the community who do not have a family doctor, as well as wrap-around support for those facing socioeconomic and psychosocial challenges.

Role & Responsibilities

Board members play a crucial role in providing governance, financial, and strategic oversight to help advance GROW’s mission. The Board meets once a month, with a combination of in-person and virtual meetings.

Who Should Apply?

Candidates with strong leadership and strategic skills who align with GROW’s mission and can effectively communicate its vision are encouraged to apply. This year, specific skills of interest include:
✅ Experience in fundraising and development
✅ Governance experience
✅ Coordination of volunteers

Commitment to Diversity

GROW is committed to broadening the diversity of its Board of Directors and encourages applications from individuals with a variety of lived experiences.

How to Apply

Expressions of interest, including resumes and any questions, can be sent to growboardofdirectors@prismahcc.ca for consideration by March 31, 2025.

This is an opportunity to contribute to equitable access to prenatal and postnatal care and support individuals in need. Those with relevant skills and experience are encouraged to apply.

Written by Mina Yu, CRHESI Student Collective, Community Engaged Learning placement, Bachelor of Health Sciences, Western University

Artificial Intelligence (AI) has undoubtedly transformed healthcare. Its applications—from improving diagnostic accuracy and resource allocation to streamlining workflows—promise to revolutionize patient care. AI’s potential to reduce human error, anticipate healthcare needs, and optimize management of chronic diseases paints an optimistic picture of timely, equitable healthcare. However, beneath its transformative capabilities lies a critical challenge: algorithmic bias.

AI, lauded for its potential to minimize human biases and inconsistencies, often inherits the same inequities embedded in healthcare systems. When trained on homogeneous datasets or data that over-represent privileged groups, AI systems perpetuate existing biases, undermining efforts to address systemic disparities. For instance, a review by Celi et al. (2022) revealed that most clinical AI models are built on datasets from middle- and high-income countries, especially the U.S. and China. When the relationships within data sources used to train AI models differ from those in the populations where these models are deployed, the resulting outputs are often inaccurate. This mismatch restricts the effectiveness of AI systems, particularly in underserved and underrepresented communities, including but not limited to those in lower-income countries. Consequently, AI-driven interventions are systematically misaligned with the needs of these individuals and groups, resulting in inappropriate or inaccessible care. Rather than reducing disparities, this failure to provide suitable care exacerbates existing inequities.

One striking example involves an AI algorithm used by insurance companies to predict healthcare needs. A 2021 study (NIHCM Foundation) found that while Black patients were significantly sicker than their white counterparts, both groups were assigned similar risk scores. This bias stemmed from the algorithm’s reliance on past healthcare expenditures—a metric influenced by systemic inequities in access and treatment. Because Black patients, for structural and systemic reasons, typically receive less care despite generally experiencing worse health outcomes, their needs are inaccurately assessed. This perpetuates a harmful cycle of unmet healthcare needs, declining individual health, and widening disparities in health outcomes between populations.

For Canada, algorithmic bias intersects with the digital divide, disproportionately affecting rural, remote, and Indigenous Peoples, who are more likely that non-Indigenous people to live in remote Northern communities. This highlights gaps in socioeconomic and demographic factors, limiting these groups’ ability to benefit from AI advancements. Researchers like Anawati et al. (2024) emphasize that AI and machine learning literature is only beginning to address the absence of diversity in training data, and the real-world impacts on the health and well-being of groups already facing marginalization.

AI offers incredible promise, but its true potential lies in fostering equity, not reinforcing disparity. To bridge these gaps, recalibrating the fundamental assumptions of AI systems is essential. Incorporating diverse datasets from underserved groups and regions can help mitigate bias and extend AI’s benefits to marginalized populations. Ultimately, for AI to truly revolutionize healthcare, its development must be inclusive, addressing not just technical challenges but also the societal inequities that shape healthcare outcomes.

References

Anawati, A., Fleming, H., Mertz, M., Bertrand, J., Dumond, J., Myles, S., et al. (2024). Artificial intelligence and social accountability in the Canadian health care landscape: A rapid literature review. PLOS Digit Health, 3(9): e0000597. https://doi.org/10.1371/journal.pdig.0000597 

Celi, L.A., Cellini, J., Charpignon, M.L., Dee, E.C., Dernoncourt, F., Eber, R., et al. (2022). Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review. PLOS Digital Health, 1(3): e0000022.  https://doi.org/10.1371/journal.pdig.0000022 

Igoe, K.J. (n.d.). Algorithmic Bias in Health Care Exacerbates Social Inequities — How to Prevent It. Artificial Intelligence and Technology. Retrieved from https://www.hsph.harvard.edu/ecpe/how-to-prevent-algorithmic-bias-in-health-care/     

Johns Hopkins. (2023, May 02). How Health Care Algorithms and AI Can Help and Harm. Retrieved from https://publichealth.jhu.edu/2023/how-health-care-algorithms-and-ai-can-help-and-harm 

Morales, K. (2022, January 5). Can Artificial Intelligence Help Increase Diversity in STEM? CSUF News. https://news.fullerton.edu/2022/01/can-artificial-intelligence-help-increase-diversity-in-stem/    

NIHCM Foundation. (2021, Sept 30). Racial Bias in Health Care Artificial Intelligence. Artificial Intelligence. Retrieved from https://nihcm.org/publications/artificial-intelligences-racial-bias-in-health-care

Panch, T., Mattie, H., Atun, R. (2019). Artificial intelligence and algorithmic bias: implications for health systems. J Glob Health, 9(2):020318. https://doi.org/10.7189/jogh.09.020318