Mixed Methods Development of a Leadership Framework for Generation Y Bedside Nurses
Bibliographic record
Abstract
This study addresses the global leadership crisis in healthcare, which leaves an unprepared nursing workforce who are unable to achieve acceptable patient outcomes. Generation Y nurses will soon form the majority cohort of nurses; they therefore represent the future of the nursing profession worldwide. Their leadership ability will no doubt impact on healthcare globally. There has been a lack of academic research focusing on the leadership needs of Generation Y nurses, specifically bedside nurses who are closest to the point of care. There is also a lack of mixed methods research in this field. This research answers the question: How can the nursing profession prepare Generation Y nurses to become effective leaders? A multistage mixed methods advanced framework design was used, with data integration occurring at multiple levels. Data was collected on Generation Y nurses working at a hospital in Saudi Arabia, through the Values in action (VIA)-24 strengths survey, the American Organization of Nurse Executives (AONE) leadership survey on ‘The leader within’, and semi-structured face-to-face interviews. Data analysis included statistical measures and thematic analysis using Tesch’s coding. The aim of the study was to develop a sustainable leadership framework for generation Y bedside nurses, through data collected from them and for them. This study shows that Generation Y nurses have a clear leadership vision, and strongly desire leadership education that is creative, innovative, technology-driven and fun. It is vital that bedside nurses are given the opportunity to meet their full leadership potential, which will contribute towards the much needed transformation of healthcare globally.
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 | 0.077 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".