Nurses’ engagement with power, voice and politics amidst restructuring efforts
Bibliographic record
Abstract
Change is inevitable, and increasingly rapid and continuous in healthcare as organizations strive to adapt, improve and innovate. Organizational change challenges healthcare providers because it restructures how and when patient care delivery is provided, changing ways in which nurses must carry out their work. The aim of this doctoral study was to explore frontline nurses' experiences of living with rapid and continuous organizational change. A critical hermeneutic approach was utilized. Participants described feeling voiceless, powerless and apolitical amidst rapid and continuous organizational changes which fuelled apathy, cynicism and disengagement from the organization. However, critical analysis of the data showed that nurses actively engaged with power, voice and politics through resistant and transgressive behaviours in micro-ethical moments of practice. There is a need to reconceptualize the concepts of voice, power and politics in nursing as there is dissonance between nurses' beliefs about these concepts and what they are enacting in practice. Recognizing their enactment of power, voice and political agency at the micro-level may empower nurses. Empowerment would mitigate the high levels of reports of powerlessness experienced in practice during organizational changes.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".