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Beyond Coordination: Exploring Articulation Work Across People and Technologies in Chronic Condition and Mental Health Care Cover

Beyond Coordination: Exploring Articulation Work Across People and Technologies in Chronic Condition and Mental Health Care

By:   
Open Access
|Sep 2026

Abstract

Background: Integrated care depends not only on coordination across systems but on the articulation work that makes coordination possible. This includes the ongoing, often invisible effort required to align people, information, and processes across settings and systems. For those living with chronic conditions and co-occurring mental health challenges, this articulation work is continuous, complex, and emotionally demanding. The Technology-Enabled Collaborative Care (TECC) program was designed to make this work visible and supported through technology-enabled, team-based, compassion approaches to care.

 

Approach: This presentation synthesizes learning from multiple TECC research streams, including two completed feasibility trials (TECC-DM for adults living with type-2 diabetes; and TECC-T1D3 for young adults living with type-1 diabetes), a province-wide cross-sectional survey, an ongoing hybrid implementation effectiveness trial, and two evidence syntheses. Across this program of work, mixed methods approaches explored how human and digital actors share and sustain the relational, cognitive, and organizational work of integration. The focus of this combined analysis is on the potential of human and artificial intelligence partnerships to support and redistribute the articulation work required to deliver coordinated, compassionate care for people living with chronic conditions and mental health challenges.

People with lived experience of chronic conditions and mental health challenges, peer supporters, care providers, and researchers worked collaboratively to co-design intervention components, digital approaches, and evaluation metrics. Individuals with lived experience contributed throughout the design, implementation, and interpretation phases of research, with this inclusive and iterative approach supporting adaptation of the TECC model to diverse contexts and populations.

 

Results: Across TECC iterations, technology emerged as relational infrastructure – a mechanism that amplifies, rather than replaces human coordination. Findings reveal that while some individuals benefit from high-touch, relationship-driven support, others may benefit from low-touch, technology-facilitated options that offer autonomy, flexibility, and ease of access. Asynchronous communication and chatbot availability was viewed positively for sustaining engagement between sessions, extending the reach of care teams, and offering responsive support that complemented, rather than competed with, human-facilitated integration

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Across studies, participants described connectedness to care and community as both an outcome and essential contributor to wellbeing. Findings reinforce that digital and AI tools hold promise in redistributing articulation work by reducing administrative burden, enhancing communication pathways, and supporting proactive, coordinated care, when implemented ethically and equitably. Across all sources of evidence, and regardless of delivery mode or context, care integration was ultimately sustained through relational trust and shared understanding.

 

Implications: Findings from this program of work contribute to an emerging understanding of technology-enabled care as a partnership between human and AI that collectively performs the articulation work of integration. These insights advance theoretical and practical understanding of how coordination is enacted in everyday care and highlights transferable lessons for health systems internationally.

Journal eISSN: 1568-4156
Language: English
Page range: 030 - 030
Published on: Sep 11, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services

© 2026 Carly Whitmore, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.