Strategy to stay ahead of the curve: A concept analysis of talent management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it