The use of cluster analysis to identify different burnout profiles among nurses and care assistants for older adults

Hazardous traffic scenarios for motorcyclists in Indonesia: a comprehensive insight from police accident data and self-reports

Motorcycle safety remains a concern in low- and middle-income countries. This study addresses this issue by identifying hazardous scenarios for motorcyclists in Indonesia. We conducted a two-step cluster analysis and injury analysis to examine motorcycle accidents based on the police accident dataset (2020-2021) of Brebes Regency, Indonesia. We integrated the findings with accident self-reports from 104 young motorcyclists using a joint display to obtain a more comprehensive insight. As a result, we identified four hazardous traffic scenarios: motorcycle-to-vehicle collisions on median roads, motorcycle-to-vehicle collisions on non-median roads, motorcycle-to-pedestrian collisions, and single-motorcycle collisions. We suggest countermeasures for each scenario and propose a public transport policy as a safer mobility solution. Applying a two-step cluster analysis on accident data and integrating the findings of accident data and self-report analysis proved beneficial in this study. Therefore, we encourage the use of this novel approach in future studies.

Development of an Artificial Intelligence-Based Tailored Mobile Intervention for Nurse Burnout: Single-Arm Trial

BACKGROUND

Nurse burnout leads to an increase in turnover, which is a serious problem in the health care system. Although there is ample evidence of nurse burnout, interventions developed in previous studies were general and did not consider specific burnout dimensions and individual characteristics.

OBJECTIVE

The objectives of this study were to develop and optimize the first tailored mobile intervention for nurse burnout, which recommends programs based on artificial intelligence (AI) algorithms, and to test its usability, effectiveness, and satisfaction.

METHODS

In this study, an AI-based mobile intervention, Nurse Healing Space, was developed to provide tailored programs for nurse burnout. The 4-week program included mindfulness meditation, laughter therapy, storytelling, reflective writing, and acceptance and commitment therapy. The AI algorithm recommended one of these programs to participants by calculating similarity through a pretest consisting of participants' demographics, research variables, and burnout dimension scores measured with the Copenhagen Burnout Inventory. After completing a 4-week program, burnout, job stress, stress response using the Stress Response Inventory Modified Form, the usability of the app, coping strategy by the coping strategy indicator, and program satisfaction (1: very dissatisfied; 5: very satisfied) were measured. The AI recognized the recommended program as effective if the user's burnout score reduced after the 2-week program and updated the algorithm accordingly. After a pilot test (n=10), AI optimization was performed (n=300). A paired 2-tailed t test, ANOVA, and the Spearman correlation were used to test the effect of the intervention and algorithm optimization.

RESULTS

Nurse Healing Space was implemented as a mobile app equipped with a system that recommended 1 program out of 4 based on similarity between users through AI. The AI algorithm worked well in matching the program recommended to participants who were most similar using valid data. Users were satisfied with the convenience and visual quality but were dissatisfied with the absence of notifications and inability to customize the program. The overall usability score of the app was 3.4 out of 5 points. Nurses' burnout scores decreased significantly after the completion of the first 2-week program (t=7.012; P<.001) and reduced further after the second 2-week program (t=2.811; P=.01). After completing the Nurse Healing Space program, job stress (t=6.765; P<.001) and stress responses (t=5.864; P<.001) decreased significantly. During the second 2-week program, the burnout level reduced in the order of participation (r=-0.138; P=.04). User satisfaction increased for both the first (F=3.493; P=.03) and second programs (F=3.911; P=.02).

CONCLUSIONS

This program effectively reduced burnout, job stress, and stress responses. Nurse managers were able to prevent nurses from resigning and maintain the quality of medical services using this AI-based program to provide tailored interventions for nurse burnout. Thus, this app could improve qualitative health care, increase employee satisfaction, reduce costs, and ultimately improve the efficiency of the health care system.

From cause and effect to causes and effects

It is now-at least loosely-acknowledged that most health and clinical outcomes are influenced by different interacting causes. Surprisingly, medical research studies are nearly universally designed to study-usually in a binary way-the effect of a single cause. Recent experiences during the coronavirus disease 2019 pandemic brought to the forefront that most of our challenges in medicine and healthcare deal with systemic, that is, interdependent and interconnected problems. Understanding these problems defy simplistic dichotomous research methodologies. These insights demand a shift in our thinking from 'cause and effect' to 'causes and effects' since this transcends the classical way of Cartesian reductionist thinking. We require a shift to a 'causes and effects' frame so we can choose the research methodology that reflects the relationships between variables of interest-one-to-one, one-to-many, many-to-one or many-to-many. One-to-one (or cause and effect) relationships are amenable to the traditional randomized control trial design, while all others require systemic designs to understand 'causes and effects'. Researchers urgently need to re-evaluate their science models and embrace research designs that allow an exploration of the clinically obvious multiple 'causes and effects' on health and disease. Clinical examples highlight the application of various systemic research methodologies and demonstrate how 'causes and effects' explain the heterogeneity of clinical outcomes. This shift in scientific thinking will allow us to find the necessary personalized or precise clinical interventions that address the underlying reasons for the variability of clinical outcomes and will contribute to greater health equity.

Analyzing Latent Burnout Profiles in a Sample of Spanish Nursing and Psychology Undergraduates

There is abundant literature suggesting that university students in helping professions experience high levels of stress, leading to an increased risk of developing burnout. The objective of this study was to identify burnout profiles in a sample of 1162 Spanish nursing and psychology undergraduates using latent profile analysis, a person-oriented statistical method that can identify hidden homogenous subgroups within a heterogeneous population. We expected to replicate in university students the five-profile structure (burnout, overextended, disengaged, ineffective, and engagement) proposed by Leiter and Maslach using the burnout dimensions (emotional exhaustion, cynicism, and inefficacy) as indicators. The results showed that burnout, overextended, and engagement profiles were adequately replicated. Given that levels of inefficacy and cynicism were medium to low, the ineffective and disengaged profiles somewhat deviated from those identified by Leiter and Maslach. We found differences between the five latent profiles in several psychological variables, such as depression and anxiety. These results suggest that psychosocial factors (e.g., workload) are significant among students and may adversely impact their health, leading to psychosomatic and emotional disorders. Hence, designing effective interventions to prevent health problems associated with burnout seems advisable, considering the specific burnout profile that a student exhibits.

The role of self-compassion and compassion toward others in burnout syndrome in a sample of medical students

Burnout produces negative effects on academic performance, and, in turn, feelings of inefficiency that are detrimental to students' psychosocial well-being. The aim of this research is to determine the effects that self-compassion and compassion toward others have on various burnout dimensions in a sample of medical students. A total of 235 medical students (61.7% women) aged between 16 and 32 years old (M = 19.82; SD = 2.37) belonging to a Colombian university participated. A cluster analysis to segment the population according to burnout was carried out along with nonparametric contrasts to identify differences in the levels of self-compassion and compassion toward others between each profile. A series of regression analyses was designed to find out how each type of compassion was associated with burnout on each profile. The cluster analysis allowed us to identify three profiles. The low-involvement profile (n = 51) is characterized by low depersonalization, intermediate levels of emotional exhaustion and personal accomplishment and exhibits low levels of self-compassion and compassion fatigue compared with the other profiles. The positive-adaptation profile (n = 104) is characterized by low depersonalization levels, intermediate degrees of emotional exhaustion and high levels of personal accomplishment and exhibits the highest levels of self-compassion and compassion fatigue compared with the other profiles. The high-demand profile (n = 104) is characterized by intermediate depersonalization levels, medium-high levels of emotional exhaustion and high levels of personal accomplishment and exhibits intermediate levels of self-compassion and low levels of compassion fatigue. Establishing profiles based on burnout allows students to be segmented and for precise knowledge to be acquired about the effects that both types of compassion have on the dimensions of burnout.

Effect of Cough Machine Assistance and Care Bundle Combined Therapy on Burned Patients With Moderate To Severe Inhalation Injury

To investigate the effectiveness of cough machine assistance combined with care bundle on mental resilience and self-efficacy on burned patients with moderate to severe inhalation injury. Totally 98 burned patients with moderate to severe inhalation injury from April 2021 to April 2022 were enrolled in this retrospective analysis. They were divided into the control group (n = 49, conventional therapy) and combined-therapy group (n = 49, cough machine assistance and care bundles). The mental resilience, posttraumatic growth, and self-efficacy were conducted. The arterial blood gas analysis indicators and incidence of pulmonary infection were also evaluated. The length of hospital stay in combined-therapy group was significantly less than that of control group. There was no difference of pulmonary infections between two groups before treatment. However, the incidence of pulmonary infection in the combined-therapy group was significantly lower. After the therapy, the combined-therapy group showed significantly higher partial pressure of oxygen, and lower levels of oxyhemoglobin saturation and partial pressure of carbon dioxide compared with the control group. The evaluation with the mental resilience, posttraumatic growth, and self-efficacy after the therapy, indicated that the patients in the combined-therapy group had significantly higher scores of optimism, strength, tenacity, relating to others, new possibilities, personal strength, appreciation of life and spiritual change, and self-efficacy. The combined therapy with cough machine assistance and care bundles can effectively improve the mental resilience and self-efficacy level of burned patients with moderate to severe inhalation injury, showing potential for possible clinical applications.

Burnout profiles among French healthcare professionals caring for young cancer patients

PURPOSE

The aims of this study were to identify specific burnout profiles among healthcare workers caring for young cancer patients and to investigate their specificities in terms of sociodemographic characteristics, stress, coping mechanisms, and perceived rewards.

METHODS

A total of 262 French healthcare professionals working with young cancer patients completed the study protocol (sociodemographic characteristics, Pediatric Caregiver Stress Questionnaire, Work Rewards Scale-Pediatric Oncology, Ways of Coping Checklist - Revised, Maslach Burnout Inventory). A cluster analysis was performed to identify burnout profiles. ANOVAs and chi-tests were performed in order to identify the differences between the clusters.

RESULTS

The cluster analysis performed showed two end-points profiles with three low or high dimensions (Engagement, Burnout) and three intermediate profiles with only one high dimension (Overextended, Disengaged, Ineffective). The Burnout profile was characterized by a high level of stress, particularly related to working conditions, a low level of perceived rewards, and a low use of problem-focused coping. The Overextended profile was characterized by a high level of stress, particularly related to working conditions. The Disengaged profile had a higher level of stress and a lower level of perceived rewards than the Engagement profile. The Ineffective profile was characterized by low levels of perceived rewards, problem-focused coping, and social support seeking.

CONCLUSION

Interventions to improve the health of healthcare professionals caring for young cancer patients should focus primarily on improving working conditions (work overload, work/life balance) and promoting a stable work environment, enabling healthcare workers to develop their resources at work.

The Complexity of Burnout Experiences among Care Aides: A Person-Oriented Approach to Burnout Patterns

Care aides working in nursing homes experience burnout attributed to various workplace stressors. Burnout dimensions (exhaustion, cynicism, and reduced professional efficacy) interact to form distinct burnout patterns. Using a person-oriented approach, we aimed to identify burnout patterns among care aides and to examine their association with individual and job-related factors. This was a cross-sectional, secondary analysis of the Translating Research in Elder Care 2019-2020 survey data collected from 3765 care aides working in Canadian nursing homes. We used Maslach Burnout Inventory to assess burnout and performed latent profile analysis to identify burnout patterns, then examined their associations with other factors. We identified an engaged pattern (43.2% of the care aide sample) with low exhaustion and cynicism and high professional efficacy; an overwhelmed but accomplished pattern (38.5%) with high levels of the three dimensions; two intermediate patterns-a tired and ineffective pattern (2.4%) and a tired but effective pattern (15.8%). The engaged group reported the most favorable scores on work environment, work-life experiences, and health, whereas the tired and ineffective group reported the least favorable scores. The findings suggest complex experiences of burnout among care aides and call for tailored interventions to distinct burnout patterns.

Characteristics of Occupational Burnout among Nurses of Various Specialties and in the Time of the COVID-19 Pandemic-Review

Occupational burnout is particularly common among nurses due to their work being associated with stress, showing understanding, compassion, and commitment, along with the simultaneous need to maintain the necessary emotional distance. The aim of this review was to assess the occurrence and characterization of burnout among nurses working within neurology, geriatric care, intensive care units and with patients infected with the novel COVID-19 virus. PRISMA guidelines were used to conduct the review. The search for literature was limited to articles meeting the inclusion criteria and published from 2017 to 2022 in PubMed, Scopus, Science Direct, Google Scholar, and Wiley. A total of 768 articles from this category have been found. Ultimately, after in-depth analysis, 20 articles were included in the study. The group of respondents ranged from 49 to 3100 participants. According to the data, the percentages of nurses suffering from burnout in the presented research ranged from 14.3% to 84.7%, with the highest value of burnout among nurses who worked in the ICU during the COVID-19 pandemic. There are certain factors among nurses that significantly affect the occurrence of burnout. These include, among others, working time, age, exposure to infection and contact with infected patients, lack of training on COVID-19 prevention, providing care to an increased number of COVID-19 patients per shift, lack of personal protective equipment, lack of support of administration, lack of pay satisfaction, intrinsic motivation and turnover intention.