Co-Creating the Future of Digital Health Education: A Participatory Curriculum Evaluation and Developmental Design of the BCIT Digital Health Advanced Certificate Program
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,084 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,011 | 0,010 |
| Communication savante | 0,007 | 0,003 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».