Exploring the use of simulation to develop leadership skills in undergraduate nursing students: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This review will explore the use of simulation to develop leadership skills in nursing students in undergraduate nursing education programs. INTRODUCTION: Leadership skills are traditionally incorporated into nursing curriculum as a stand-alone course. A demonstrated need for leadership skills in nursing graduates and recent evidence on the effectiveness of simulation in nursing education programs has led to increased use of simulation to develop leadership skills in undergraduate nursing students. Identification, explication, and mapping of the various strategies are required to help advance the use of simulation to develop leadership skills in nursing education. INCLUSION CRITERIA: Papers that focus on the use of simulation strategies specifically related to the development of nursing leadership skills in undergraduate nursing students will be included. Papers focused on post-registration/licensure nurses, graduate nurses, nurse practitioners, midwives, allied health care professionals, or psychomotor nursing skills will be excluded. METHODS: This review will be conducted in accordance with JBI methodology for scoping reviews and will consider English-language literature from 2000 to the present. Data will be extracted from the following databases: CINAHL Plus with Full Text (EBSCO); MEDLINE (R) and Epub Ahead of Print (Ovid), In-Process, In-Data-Review and Other Non-Indexed Citations, and Daily and Versions (R); PsycINFO (Ovid); Embase (Ovid); ERIC (EBSCO); and ProQuest Nursing and Allied Health Source. The search will also include unpublished non-peer-reviewed literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.088 | 0.086 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".