Prevalence of Compassion Fatigue and Its Association with Professional Identity in Junior College Nursing Interns: A Cross-Sectional Study.

Background: The issue of compassion fatigue among clinical nurses has received considerable attention, particularly during the COVID-19 pandemic. Yet, the current status of compassion fatigue among junior college nursing interns remains unclear. Additionally, professional identity can modulate the impact of compassion fatigue or burnout on psychological well-being; however, whether professional identity still works in this group is also unclear. This study aimed to reveal the current status of compassion fatigue among nursing interns in junior colleges and also investigate the association between compassion fatigue and professional identity. Methods: This cross-sectional survey evaluated the levels of participants' compassion fatigue (The Compassion Fatigue Short Scale) and professional identity (Professional Identity Scale) in 2256 nursing interns. Results: The mean score of compassion fatigue was 44.99, and 19.5% of the participants scored above The Compassion Fatigue Short Scale median scores for compassion fatigue. A moderate negative correlation was detected between compassion fatigue and professional identity. Conclusions: The level of compassion fatigue among nursing interns is low but nearly one in five nursing students is at risk of compassion fatigue. More attention should be paid to nursing interns with a high risk of compassion fatigue. Future studies are warranted to explore which pathways could mediate the relationship between professional identify and comparison fatigue.

A Bibliometric Analysis of the Association Between Compassion Fatigue and Psychological Resilience From 2008 to 2021.

Aims: A negative association between the lower level of psychological resilience (PR) and increased risk of compassion fatigue (CF) and higher Coronavirus disease 2019 (COVID-19) stress has been revealed. However, bibliometric studies have not been performed to comprehensively investigate this topic. This study aimed to identify the status and trends in the CF and PR field from 2008 to 2021 and during the COVID-19 pandemic. Methods: We identified relevant literature from the Web of Science Core Collection® database using "resilience" and "compassion fatigue" on September 30, 2021. All search results were exported in plain text format for collaboration network analysis, reference-based co-citation analysis, analysis of journals, and keywords-based co-occurrence analysis, which were performed using Citespace® 5.8.R1. Results: A total of 388 publications were identified finally, and there has been an increasing trend in the annual number of publications with light fluctuations. The analysis of journals and keywords indicated that nurses and social workers are the main research targets, and their mental problems are the main research topics. The turnover intention of health care providers has been a research focus, particularly during the COVID-19. Conclusion: The results of the present study help us understand the status of the CF and PR field and its recent developments.

Patients' lived experiences of breathlessness prior to prehospital care - A phenomenological study.

AIMS AND OBJECTIVES: The study aimed to describe how breathlessness is experienced by patients prior to prehospital care. DESIGN: A qualitative phenomenological design. METHODS: Lifeworld interviews were conducted with 14 participants. The analysis was carried out within the descriptive phenomenological framework. RESULTS: The essential meaning of the breathlessness phenomenon is described as an existential fear in terms of losing control over one's body and dying, which involves a battle to try to regain control. This is further described by four constituents: being in an unknown body, striving to handle the situation, the ambiguity of having loved ones close and reaching the utmost border. CONCLUSIONS: Patients describe a battling for survival. It is at the extreme limit of endurance that patients finally choose to call the emergency number. It is a challenge for the ambulance clinician (AC) to support these patients in the most optimal fashion.

Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain.

BACKGROUND: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. METHODS: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient's factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. RESULTS: The database included a total of 2022 patients (mean age 64 [IQR 5-71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10-17]) and SOFA score (5 [IQR 3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. CONCLUSION: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a "one-size-fits-all" model in practice.