Nurse Leaders’ Perceptions of Influence of Organizational Restructuring on Evidence-Informed Decision-Making
Bibliographic record
Abstract
AIM: To describe how organizational context and restructuring influenced nurse leaders' use of evidence in decision-making in their management practice. METHOD: Qualitative descriptive study. Fifteen leaders at executive and front-line manager levels in one organization were interviewed using a semi-structured format. FINDINGS: Inductive content analysis generated five main themes: leaders strove to keep relationships that preserve best decision-making ability; and sought the best knowledge to inform their decisions. However, a context of constant change; more scope; less autonomy; and decisional inertia in a sea of change had profound effects on their ability to employ evidence in decision-making. IMPLICATIONS: Evidence-informed decision-making is a dynamic social process highly influenced by political instability in work environments. Organizational restructuring creates threats to common decision-making strategies, including information flow, relationships and priority setting. Healthcare restructuring is now a global constant, and there is a need for hospital leaders to understand and mitigate the effect restructuring has on the ability of leaders to engage in evidence-informed decision-making. Strategies are proposed to manage uncertainty and support nurse leaders in their evidence-informed decision-making to deliver quality health services. This research provides an in-depth examination of how evidence-informed decision-making is influenced in the context of instability and uncertainty due to ever-present organizational restructuring.
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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 | 0.034 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".