Analyzing Nursing Leadership at an Academic Historical Event: A Descriptive Study Based on Social Networks
Bibliographic record
Abstract
PURPOSE: To analyze the leadership network structure among nursing leaders in Spain identified through the Grupo40Enfermeras y Universidad event. METHODS: A descriptive cross-sectional study using social network analysis was used. Study sample consisted of 210 individuals, of whom 119 received nominations as referents. Structural analysis of the network was conducted using centrality and cohesion. RESULTS: A network structure was generated in which different leadership strategies were identified through InDegree, Eigenvector, and Betweenness Centrality. Five leaders were identified as bridges to other individuals using Betweenness. The whole network presented little cohesion although two highly cohesive cores were detected by K-core measurements. CONCLUSION: A strategy is needed to support nursing leaders with high degree of Betweenness to serve as bridges to connect other nursing leaders.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".