An Innovative Doctor of Nursing Programme: Transforming Learning, Leadership and Health Systems
Bibliographic record
Abstract
AIM: To provide an in-depth description of an innovative Doctor of Nursing programme which prepares nurses for senior roles in healthcare and related organisations. This programme provides nurse leaders with the knowledge and skills to advance systems through healthcare innovation design, implementation, and evaluation. METHODS: A comparison of doctoral nursing programmes, highlighting the unique aspects of the University of Calgary Doctor of Nursing programme. RESULTS: The University of Calgary Doctor of Nursing programme addresses key gaps that currently exist within nursing education. Few existing programmes directly support the development of nurses as healthcare leaders and innovators. This programme enables nurse leaders to leverage their front-line experience into senior system-level leadership roles. Each core course includes a building block assignment that develops key doctoral skills: framing research questions, appraising literature, selecting methods and data, planning ethically sound projects, and translating evidence into persuasive arguments for policy or system change. CONCLUSION: Nurses play a vital role in healthcare around the world. The University of Calgary Doctor of Nursing programme recognises the value of investing in nursing leaders and emboldening them to leverage their frontline leadership experience to advance data-driven change, innovation, and policy development in the complex healthcare systems in which they work and lead. IMPLICATIONS FOR THE PROFESSION: Currently, there is a dearth of programmes available to prepare nurses for senior leadership roles in healthcare or related organisations, despite significant demand from prospective students and employers alike. The University of Calgary Doctor of Nursing programme meets the workforce demand for a programme focused on nursing leadership, to advance health systems through skill development in systems innovation, appraisal of evidence and implementation science, as well as quality assurance/quality improvement and programme evaluation. This programme focus also better equips students to examine and evaluate systemic inequities and challenges currently facing healthcare systems, practitioners and users.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".