Sustaining and Inspiring the Capacity of the Nursing Profession: The Case for Transformative Practice Education Models
Bibliographic record
Abstract
Decades of commissioned reports have pointed to solutions for nurturing nursing practice environments as essential to sustaining a nursing workforce. Beyond salary compensation and other solutions, we discuss the critical need for collaborative leadership in practice and education as a priority policy agenda aimed at confronting the shortage of nurses. The COVID-19 pandemic has intensified the nursing shortage and shortage of capacity in practice education, and we explore some learning in this context. Our paper draws on two initiatives in the province of British Columbia: the development of a transformative practice education model and an expanded Collaborative Learning Unit initiative. We propose building the following learning cultures: formal collaborative governance processes, intentional supports for graduate transitions and implementation of advanced nursing practice leadership and educator roles across the system. While transformative solutions are a tough sell in crisis-oriented contexts, this paper is a call for nurse leaders in all sectors to advance deep policy solutions with lasting impact on sustainable nursing human resources.
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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 | 0.041 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.104 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".