Bibliographic record
Abstract
This research explores strategies for implementing a trauma-informed approach as a leader and nurse educator, addressing the question: How can I integrate trauma-informed practice (TIP) into my leadership? Using a first-person, action-orientated approach that included a one-hour semi-structured interview with a subject-matter expert (SME), completing bi-weekly journaling, and meeting twice with feedback partners (FPs) to ensure rigour within the data collection. An extensive literature review focused on defining trauma and exploring TIP practices. Two rounds of data collection and thematic analysis revealed three key components of trauma-informed leadership: awareness, connection, and a psychologically safe environment. The goal was to understand how as a leader I could implement TIP to support individuals who have experienced trauma. Recommendations include promoting educational opportunities and providing tools for trauma-informed leaders. Implementing TIP has enabled me to create a supportive, healing environment within my leadership and organization, connecting individuals to community resources and fostering positive change.
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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 | 0.015 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".