Mitigate the Negative Effects of Secondary Traumatic Stress and Compassion Fatigue by Cultivating a Caring Pedagogy and Resilience
Bibliographic record
Abstract
Students and practicing nurses are at risk of developing empathy-based stress conditions related to caring for people who have been traumatized. Caring is a known factor in all suggested interventions for empathy-based stress conditions. Therefore, Chapter Five explores ways to mitigate the negative effects of secondary traumatic stress and compassion fatigue through employing a caring pedagogy and resilience. Caring pedagogy in nursing education is important because it incorporates caring components into the delivery of the core curriculum, creates a community of learning that prioritizes students, is inclusive, and engaging, and protects the emotional integrity of student nurses. Noddings’ elements of moral education such as modeling, dialogue, practice, and confirmation are identified as essential to a caring learning environment. For example, student nurses can learn what it means to care by observing the behavior of their instructor, by a dynamic exchange of ideas, by prioritizing caring, and by encouraging the best in others. A learning environment that is caring must also be based on civility and is the shared responsibility of both faculty and students. Selfcare is identified as a known strategy to reduce the emotional stress experienced by nurses and student nurses. Watson’s Caritas processes are subsequently recommended as the basis for self-care and consist of demonstrating sensitivity toward oneself and everyone else, through spiritual practices that support loving, caring relationships. Resilience consists of the ability to quickly return to normal functioning after experiencing adversity. Resilience skills can be learned through the development of protective factors and mechanisms and may prevent empathy-based stress conditions related to trauma, can assist a trauma survivor to bounce back more quickly, and teach people how to deal with the stress of everyday life. The following ways to cultivate resilience in nurses are presented, building positive nurturing relationships and networks; maintaining positivity; developing emotional insight; achieving life balance and spirituality; and becoming more reflective. Three strategies to foster resilience in nursing education include resilience training in the school curriculum; prioritizing role modelling; and enabling generativity. Two Narrative Case Studies are presented. The first one tells the story of how a Psychiatric Nurse developed the signs of secondary traumatic stress after one of her clients ended their life through suicide. The second one describes how a student nurse was unaware that she was experiencing emotional strain. The following four learning activities are proposed, sharing examples of being cared for; exploring ways to enhance learning; nurturing caring experiences in educational settings; and implementing Watson’s caring processes and strategies to enhance selfcare. The Chapter ends by recommending a self-care challenge that promotes emotional appraisal to manage negative emotions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".