Bibliographic record
Abstract
Nurses and nursing practice are key in health policy and professional debate, and in healthcare organizations struggling with nurse shortages. In this context, the reorganization of nursing work is increasingly framed — politically and discursively — as an urgent global policy priority, sparking a wave of change initiatives in recent years. Too often, however, the political nature of change is pushed aside by those working on and studying change in healthcare practice, policy, and research. Across fields, there is a strong tendency to treat change as technical and apolitical processes; detached from the organizational, institutional and political arenas in which envisioned changes are negotiated, contested and ultimately decided. This research challenges this deficit by zooming in on the politics of organizational change that inform and shape the reorganization of nursing work. In doing so, it seeks to offer a more comprehensive —and realist — account of how organizational change unfolds ‘on the ground’ in (Dutch) hospital practice and its multifaceted outcomes for the profession. Drawing on extensive and engaged ethnographic research within ‘RN2Blend’ — a nationwide participatory research program tasked with studying and facilitating change in nursing — this research analyzes the politics of reorganizing nursing work ‘from within’. It shows how the reform of nursing work and the professionalization of nurses runs the risk of becoming stuck in politicized debates about identity, epistemic knowledge and organizational impact, arguing for the recognition of nurses’ practical and experimenting knowledge for organizational and policy change. The insights will be of interest to nurses, healthcare managers, professional nursing associations, policymakers, and anyone concerned practically or conceptually with power, politics, and organizational change — in nursing and beyond.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".