Mitigate the Negative Effects of Secondary Traumatic Stress and Compassion Fatigue by Cultivating a Caring Pedagogy and Resilience
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».