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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".