Early Career Nurses’ Experiences of Engaging in a Leadership Role in Hospital Settings
Bibliographic record
Abstract
BACKGROUND: Early career nurses (ECNs) can be expected to assume shift charge nurse leadership roles quickly upon entering practice. Since the emergence of the COVID-19 pandemic, junior nurses may find their leadership capabilities tested further as the challenges of leadership are made increasingly complex in the context of an infectious disease outbreak. PURPOSE: The purpose of this qualitative study was to explore early career registered nurses' (RNs) experiences of engaging in shift charge nurse roles in hospital settings. METHODS: This study used an interpretive descriptive (ID) approach. Semi-structured, in-depth interviews were conducted with 14 RNs across Ontario, who had up to three years of experience and who had engaged in a shift charge nurse role in a hospital setting. Recruitment and data collection took place from January to May 2021 during the COVID-19 pandemic. Interviews were recorded, transcribed, and analyzed following the principles of content analysis. RESULTS: . CONCLUSIONS: Study findings provide insights into potential strategies to support ECNs in shift charge nurse roles, during the remaining course of the COVID-19 pandemic and beyond. Greater support for nurses who engage in these roles may be achieved by promoting collaborative unit and organizational cultures, prioritizing leadership training programs, and strengthening policies to provide greater clarity regarding charge nurse role responsibilities.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".