Trauma-informed Care for Nursing Education: Fostering a Caring Pedagogy, Resilience & Psychological Safety
Bibliographic record
Abstract
Trauma-informed care is designed to assist persons who have experienced adversity and focuses on change at the clinical and organizational level. Its goals center around prevention, intervention, and treatments that are evidence-based, encourage resilience, and enhance coping. This textbook is designed to give a comprehensive overview of trauma-informed care to students and faculty involved in nursing care programs. Key features: Explains the skill sets to assess and care for persons who have experienced trauma. Emphasizes key principles of trauma-informed care Includes the use of client-centered, person-centered, and resilience-based tools to deal with trauma Recommends trauma recovery from a positive psychology and post-traumatic growth perspective Utilizes a caring pedagogy intended to foster resilience and help offset the secondary traumatic stress and compassion fatigue experienced by student and practicing nurses. Communicates the value of fostering psychological safety, compassion satisfaction, and joy in work Includes narrative case studies and learning activities in all chapters to help the reader to actively engage with the subject matter. Presents self-care strategies to enhance physical and emotional well-being.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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".