Co-Creating the Future of Digital Health Education: A Participatory Curriculum Evaluation and Developmental Design of the BCIT Digital Health Advanced Certificate Program
Bibliographic record
Abstract
This doctoral research explores the co-creation and developmental evaluation of the British Columbia Institute of Technology (BCIT) Digital Health Advanced Certificate (DHAC) program, addressing the critical need for responsive, practice-oriented digital health education in Canada. As healthcare systems undergo profound digital transformation, the demand for skilled professionals capable of navigating the complexities of technology-enabled care continues to intensify. However, the educational landscape remains fragmented, with gaps in competency alignment, limited experiential learning opportunities, and insufficient integration of evolving workforce needs. Guided by Michael Quinn Patton’s Developmental Evaluation (DE) approach, this study responds to these challenges through an applied, participatory research approach, engaging diverse stakeholders, including learners, educators, digital health and clinical informatics specialists, in a collaborative process of curriculum analysis and design. This study is situated within the Doctor of Nursing (DN) context and is particularly relevant to nurses, who represent a substantial proportion of the healthcare workforce and are increasingly expected to engage with digital health technologies, health informatics, and data-driven care across clinical, educational, leadership, and system-level roles. Utilizing the World Café method as a participatory forum, a multiplicity of perspectives on the essential components of developing and sustaining an effective digital health curriculum were captured. Thematic analysis, guided by Braun and Clarke’s methodology, was used to synthesize the qualitative data and generate three overarching thematic domains: Curriculum Content and Structure, Experiential Learning and Industry Partnership, and Curriculum Evolution and Sustainability. These themes articulate a shared vision for a curriculum that balances foundational competencies with applied learning experiences, while remaining agile and adaptive to future trends. The analysis illuminated critical developmental insights, offering actionable guidance for iterative curriculum refinement. Insights of urgency underscore the accelerating pace of technological change, demanding a future-oriented curriculum responsive to technological advancements such as artificial intelligence and data interoperability. Insights of integration highlight the necessity of merging technical, clinical, and relational competencies to equip learners for complex, interdisciplinary practice environments. Balancing standardization and customization signal opportunities for modular curriculum design, allowing both consistency and contextual relevance. Participants also identified the potential of alumni and industry partnerships as co-creators of learning, advocating for embedded feedback loops and continuous professional development pathways towards a future ready program. To translate insights into actionable change, a cross-mapping exercise was conducted to compare the identified themes with the existing BCIT DHAC curriculum. This process revealed areas of alignment as well as critical gaps, informing targeted curriculum refinements and new opportunities for innovation and integration. This study offers a replicable model for participatory curriculum design and highlights the value of integrating developmental evaluation to sustain curriculum relevance amidst ongoing healthcare innovation. The outcomes of this research directly inform the future directions of the BCIT DHAC program, ensuring it remains an adaptive, learner-centered, and industry-aligned pathway for preparing Canada’s digital health workforce.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.084 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".