Understanding new graduate registered nurse's preparedness and readiness for leadership
Bibliographic record
Abstract
Background: The number of new graduate registered nurses (NGRN) is on the rise while the acuity and complexities of health care continues to climb. NGRNs are required to engage in informal and formal leadership in the clinical setting in a variety of ways, yet find this challenging. Research Design: The purpose of this, qualitative descriptive, study was to understand NGRNs experiences of clinical leadership within the first 14 months of their practice. Semi-structured interviews were held with nine NGRNs at one tertiary hospital in a western Canadian province. Findings: Four main categories were constructed. The first category, self-doubt in relation to leadership, describes participants’ feelings of uncertainty about the nature, expectations, and supports related to the role. The second category, preparing for leadership, details participants’ perceptions of their preparation for leadership prior to, and during, their undergraduate nursing education, and on the job. The third category, evolving leadership, describes the development of participants’ leadership abilities over their first 14 months of practice. The final category, navigating the challenges, articulates strategies the participants used to overcome challenges they faced while acting and developing as leaders. Discussion: Findings show that the majority of participants in this study did not feel ready for the complexities of being the RN leader. Participants were particularly challenged due to their self-doubt and lack of confidence at the beginning of their practice. Their self-doubt was reflected by a lack of preparation for leadership. Participants’ confidence grew and developed over time allowing them to feel comfortable and included in their workplace. Support from formal and informal leaders was vital to their developing confidence in leadership.
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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 | 0.032 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".