Education: Reaping What We Sow: Nursing Education and Leadership in Canada and the United States
Bibliographic record
Abstract
Nurse educators in Canada and the United States have a tremendous responsibility in relation to leadership and the nursing profession.Nursing schools are charged with ensuring that graduates are competent practitioners.Moreover, graduates must actualize leadership within the profession.Leadership occurs at those relationship intersections where nurses come into contact with the public: patients and families, agencies and institutions, the healthcare system and government at all levels.The seeds of leadership are planted in entry-to-practice programs, taking root and growing strong in supportive practice settings.It is at the master's and doctoral levels of education that many of our leaders fully blossom in all nursing domains: practice, education, administration and research.When we (Gregory, Russell) reflect on our roles as clinicians, educators, researchers and education administrators over the past two decades, we can make two major observations: • The development of our leadership skills was mostly ad hoc, "on the job" and occasionally "post-hoc."There was no systematic or integrated leadership immersion within and across the programs we completed.The seeds of leadership were scattered haphazardly.Ongoing and sustained nurturing with respect to leadership was lacking.Leaders most often grew by chance, in spite of what was or was not done to enhance that growth.• Explicit leadership connections among education, practice, administration and research were often Reaping What We Sow: Nursing Education and Leadership in Canada and the United States
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".