Trauma-Informed Nursing Leadership: Definitions, Considerations and Practices in the Context of the 21st Century
Bibliographic record
Abstract
Trauma-informed practices have been widely adopted in clinical, educational and research domains of nursing practice, supporting trauma-informed care for patients and contributing to the knowledge base for trauma-informed care practices. However, trauma-informed concepts and frameworks have not been taken up as readily by nursing leadership in the administration and policy domains, presenting an opportunity for the exploration of the ways in which trauma-informed leadership might shape nursing leadership practice. In this paper, the concept of trauma-informed leadership is defined and considered as a possible direction for nursing leadership practice in the context of the 21st century, wherein increasing complexity and rapidly accelerating social divisiveness require leadership practices that centre compassion, well-being and justice. A real-world example from the COVID-19 pandemic response provides an opportunity to consider trauma-informed nursing leadership in practice as an approach to supporting individual, population and system wellness and resilience.
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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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".