Practice: What Is the Greatest Challenge Currently Facing Leaders in Nursing Practice?
Bibliographic record
Abstract
precedence over patient well-being, interdisciplinary team cohesion and nurse satisfaction. Time for quality nursing care became a prized and contested commodity.” This quote, from a recent study conducted by Rodney and colleagues (2002) in British Columbia, struck me like a ton of bricks when I read it. The study explored the “enactment of the ethical practice” of the staff nurse and described the angst and the moral struggles that nurses face as they attempt to keep their commitments to the client and their passion for their profession. It’s not that the sentiments were a surprise. I see these struggles in my work with staff nurses as they work hard to keep their anxieties from their vulnerable clients. They worry about being forced to increase the client-tonurse ratio, about lack of support staff and about the persistent feeling that their perspectives are neither sought nor valued. For me, it seems as though we are still having the same discussion after all these years. It’s the seemingly neverending cycle of trying to foster and protect the values and principles of nursing in the corporate world’s vision of healthcare. It’s that nurses are often treated as commodities and their numbers increased or decreased according to the latest corporate trends rather than according to current available data about outcomes for clients or about impact on recruitment or retention. As I see it, the challenge is to continue to strengthen our profession so that nurses can keep their commitments to clients and be proud of their profession and their role in it. If we are LEADERSHIP PERSPECTIVES 33
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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 | 0.030 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.027 | 0.030 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".