Strategy to stay ahead of the curve: A concept analysis of talent management
Bibliographic record
Abstract
AIM: The development of nurse leaders is critical to the future of the nursing profession. Strategies to address the current loss of nurse leaders are urgently needed. The aim of this analysis is to clarify the concept of talent management as an approach by which organizations can identify, strengthen, and support emerging and current nurse leaders. BACKGROUND: The nursing profession worldwide is experiencing a shortage of nurse leaders. As nursing leaders are retiring, too few nurses are prepared to replace them. Nursing leadership is vital to effectively navigate healthcare system challenges and improve patient outcomes. Talent management moves beyond succession planning to attract, develop, and retain nursing leaders. DESIGN: Walker and Avant's model is used for concept analysis. DATA SOURCE: A literature search was accomplished using Cumulative Index to Nursing and Allied Health, MEDLINE, PubMed, Business Source Premier, Canadian Major Dailies, and Management and Organization Studies. REVIEW METHODS: Keywords: talent management, succession planning, succession management, nursing, nursing leader, leadership, administration, and executive. RESULTS: Definitions for the concept of talent management are elusive in both the business and nursing literature. There is a lack of clarity with regard to the definition of talent management. CONCLUSION: The critical attributes for talent management of nursing leadership are the identification of emerging nurse leaders and engaging them in the development of their leadership competencies. The use of this concept analysis for talent management will enhance and facilitate the stability of nursing leadership positions in today's healthcare organizations.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".