The Prevalence and Impact of Trauma and Why Trauma-informed Care is Needed in Nursing Education
Bibliographic record
Abstract
Chapter one explores the reasons why student nurses need to be educated in trauma-informed care. Trauma-informed care endeavours to help people who have experienced trauma and targets change at the organizational and clinical level with the aim of improving client/patient outcomes. Various forms of adversity that exist are presented, and we are informed that trauma is not merely a childhood occurrence but may occur at any point across the lifespan. Stereotypical biases and racial stigma experienced by the following special populations are explored, those with differing sexual orientation or gender identity, older adults, refugees and immigrants, people of colour, and Indigenous people. The role that bias and implicit bias play in structural trauma aimed at specific populations is explained. An overview is given of the following specific trauma-related responses, trauma triggers, acute stress disorder, post-traumatic stress disorder, secondary traumatic stress, vicarious traumatization, and compassion fatigue. The Four Core Assumptions of Trauma-informed Care as recommended by the Substance Abuse and Mental Health Services Administration (SAMHSA are explored, because they are foundational for providing traumaresponsive care, and consist of realizing, recognizing, responding, and resisting retraumatization. Healthcare professionals are strongly encouraged to practice in a trauma-responsive and trauma-sensitive manner. Incorporating trauma-informed approaches into the Nursing School curriculum is recommended for the following reasons. Adversity is prevalent in society, and high number of people who access health services have experienced trauma. Student nurses are not currently learning these skills in a comprehensive way in all schools. Student nurses may have a history of trauma, and they are exposed to adverse and stressful events in clinical training. Two Narrative Case Studies are presented. The first shares the story of a Counsellor who developed compassion fatigue, and the second one reveals the complexity of the trigger response. The following learning activities are suggested: connecting with the goodness in life; changing prejudices and stigma; and participating in a trauma-sensitive practice challenge. A self-care strategy that promotes self-compassion is included at the end of the chapter.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".