Faculty perspectives on implementing a concept-based nursing curriculum: A qualitative study
Bibliographic record
Abstract
• Faculty perspectives on CBC reveal role shifts in nursing education. • Concept mapping and simulation improved student engagement and reasoning. • Findings call for faculty support to sustain curriculum reform in diverse settings. The Concept-Based Curriculum (CBC) promotes critical thinking and deep learning in nursing education. At the University of Calgary in Qatar, CBC was implemented to align with international standards and national healthcare goals. Faculty play a central role in this pedagogical shift, yet their perspectives remain underexplored in transnational settings. To explore faculty experiences in implementing CBC in a culturally diverse nursing program. A qualitative descriptive study used semistructured interviews with 15 faculty members involved in CBC development and teaching. Thematic analysis followed Braun and Clarke’s framework. Six themes emerged: (1) concept mapping as a scaffold for learning, (2) structured prebriefing and debriefing to promote reflection, (3) evolving faculty roles from content experts to facilitators, (4) balancing content with conceptual depth, (5) navigating institutional and curricular constraints, and (6) perceived gains in student engagement and clinical reasoning. These themes reflected systemic change in teaching philosophy, institutional support, and learner engagement. CBC implementation fostered pedagogical transformation and improved learning, requiring sustained faculty development and institutional support.
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".