The nurse manager’s role in perioperative settings: An integrative literature review
Bibliographic record
Abstract
AIM: To describe the nurse manager's role in perioperative settings. BACKGROUND: The nurse manager's role is complex and its content unclear. Research in this area is scarce. We need to better understand what this role is to support the nurse manager's work and decision-making with information systems. EVALUATION: An integrative literature review was conducted in May 2018. Databases CINAHL, Cochrane, PubMed and Web of Science were used together with a manual search. The review followed a framework especially designed for integrative reviews. Quality of the literature was analysed with an assessment tool. Nine studies published between 2001 and 2016 were included in the final review. KEY ISSUE: The findings from the review indicate that the nurse manager's role requires education and experience, and manifests in skills and tasks. A bachelor's degree with perioperative specialisation is the minimum educational requirement for a nurse manager. CONCLUSION: Research lacks a clear description of the nurse manager's role in perioperative settings. However, the role evolves by education. More education provides advanced skills and, thereby, more demanding tasks. Information technology could provide useful support for task management. IMPLICATIONS FOR NURSING MANAGEMENT: These findings can be used to better answer the current and future demands of the nurse manager's work.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".