The potential for nurses to contribute to and lead improvement science in health care
Bibliographic record
Abstract
AIM: A discussion of how nurses can contribute to and lead improvement science activities in health care. BACKGROUND: Quality failures in health care have led to the urgent need for healthcare quality improvement. However, commonly quality improvement interventions proceed to practice implementation without rigorous methods or sufficient empirical evidence. This lack of evidence for quality improvement has led to the development of improvement science, which embodies quality improvement research and quality improvement practice. This paper discusses how the discipline of nursing and the nursing profession possesses many strengths that enable nurses to lead and to play an integral role in improvement science activities. However, we also discuss that there are insufficiencies in nursing education that require attention for nurses to truly contribute to and lead improvement science in health care. DESIGN: Discussion paper. DATA SOURCES: This paper builds on a collection of our previous work, a 12-month scoping review (March 2013-March 2014), baseline study on a quality improvement management system (Lean), interviews with nurses on quality improvement implementation and supporting literature. IMPLICATIONS FOR NURSING: This paper highlights how nurses have the philosophical, theoretical, political and ethical positioning to contribute to and lead improvement science activities. However up to now, the potential for nurses to lead improvement science activities has not been fully used. CONCLUSION: We suggest that one starting point is to include improvement science in nursing education curricula. Specifically, there needs to be increased focus on the nursing roles and skills needed to contribute to and lead healthcare improvement science activities.
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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 | 0.131 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".