Profiling health professionals' personality traits, behaviour styles and emotional intelligence: a systematic review

A long and resilient life: the role of coping strategies and variability in their use in lifespan among women

OBJECTIVES

Associations of stress-related coping strategies with lifespan among the general population are understudied. Coping strategies are characterized as being either adaptive or maladaptive, but it is unknown the degree to which variability in tailoring their implementation to different contexts may influence lifespan.

METHOD

Women (N = 54,353; Mage = 47) completed a validated coping inventory and reported covariate information in 2001. Eight individual coping strategies (e.g., Acceptance, Denial) were considered separately. Using a standard deviation-based algorithm, participants were also classified as having lower, moderate, or greater variability in their use of these strategies. Deaths were ascertained until 2019. Accelerated failure time models estimated percent changes and 95% confidence intervals (CI) in predicted lifespan associated with coping predictors.

RESULTS

In multivariable models, most adaptive and maladaptive strategies were associated with longer and shorter lifespans, respectively (e.g., per 1-SD increase: Active Coping = 4.09%, 95%CI = 1.83%, 6.41%; Behavioral Disengagement = -6.56%, 95%CI = -8.37%, -4.72%). Moderate and greater (versus lower) variability levels were similarly and significantly related to 8-10% longer lifespans. Associations were similar across age, racial/ethnic, residential income, and marital status subgroups.

CONCLUSIONS

Findings confirm the adaptive and maladaptive nature of specific coping strategies, and further suggest benefits from both moderate and greater variability in their use for lifespan among women.

Decision-Making Styles in Medical Students and Healthcare Professionals: The Roles of Personality Traits and Socio-Emotional Intelligence Factors

Intuitive and deliberative styles can be considered the best-known decision-making styles, which are thought to be linked to actual workplace performance. However, there is a limited research on individual differences in these styles among individuals who provide healthcare. Therefore, adopting the self-report approach, this study examines the roles of the Big Five personality traits and socio-emotional intelligence factors in intuitive and deliberative decision-making styles among medical students and healthcare professionals. The research sample consists of 203 participants (50 medical students, 153 healthcare professionals) who completed the Big Five Inventory, the Trait Meta-Mood Scale, the Tromsø Social Intelligence Scale, and the Preference for Intuition and Deliberation Scale. The regression analyses revealed that attention to one's emotions and social information processing were positively related to intuitive decision-making style, while the clarity of one's emotions and social awareness were negatively related to intuitive decision-making style. It was further shown that conscientiousness, neuroticism, repair of one's emotions, and social information processing were positively related to deliberative decision-making style. The findings highlight the importance of personality and socio-emotional intelligence in understanding decision-making. Specifically, they point out that Big Five personality traits better explain deliberative style, while socio-emotional intelligence factors better explain intuitive style.

Nurses' Personality Traits and Perceived Work Environments During Public Health Emergencies: Implications for Nursing Workforce Planning

Background

The nursing workforce faces substantial challenges, particularly in the aftermath of the COVID-19 era. Developing an effective strategy for workforce maintenance and the strategic deployment of nurses is crucial.

Purpose

This study aimed to explore and categorize nurses' personality traits, with a focus on analyzing differences in their perceptions of the nursing work environment.

Participants and Methods

Between January 2023 and February 2023, a multi-center cross-sectional study was carried out involving nurses from 12 tertiary hospitals actively engaged in frontline COVID-19 response duties. Through cluster sampling, surveys were distributed among eligible nursing staff, comprising a general information questionnaire, the Chinese Big Five Personality Questionnaire-Short Form, subjective evaluations of emergency nursing management, and the Chinese Nursing Work Environment Scale for Public Health Emergencies. Various statistical analyses, such as descriptive analysis, cluster analysis, non-parametric tests, and general linear model analysis, were employed to investigate the correlation between personality types and the perception of nursing work environments.

Results

The analysis encompassed 1059 valid questionnaires, reflecting the experiences of frontline nurses. The majority of these nurses possessed 1-5 years of experience, held junior professional titles, volunteered for their roles, and served as attending nurses. Categorization based on personality traits revealed three groups: resilient (35.60%), ordinary (16.15%), and distressed (48.25%) types. Significantly distinct perceptions of nursing work environments emerged among these categories, with resilient and ordinary types expressing notably higher satisfaction compared to the distressed group (H value = 256.487, p < 0.001).

Conclusion

This study illustrates the connection between nurses' perceived working environment and their personality traits. Nursing managers should factor in nurses' personality traits when choosing and deploying frontline responders during public health emergencies. Prioritizing resilient-type nurses and crafting a supportive work environment that aligns with nurses' characteristics is indispensable for an effective emergency response.

Occupational, academic, and personal determinants of wellbeing and psychological distress in residents: results of a survey in Lyon, France

Introduction

The mental health of residents is a growing significant concern, particularly with respect to hospital and university training conditions. Our goal was to assess the professional, academic, and psychological determinants of the mental health status of all residents of the academy of Lyon, France.

Materials and methods

The Health Barometer of Lyon Subdivision Residents (BASIL) is an initiative which consists in proposing a recurrent online survey to all residents in medicine, pharmacy, and dentistry, belonging to the Lyon subdivision. The first of these surveys was conducted from May to July 2022. Participants should complete a series of validated questionnaires, including the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), and the Kessler Psychological Distress Scale (K6), respectively, and ad-hoc questions assessing their global health and hospital and academic working conditions. A Directed Acyclic Graph (DAG) analysis was conducted prior to multivariable analyses, to explore the determinants associated with low wellbeing (WEMWBS <43) and high psychological distress (K6 ≥ 13).

Results

A total of 904 residents (response rate: 46.7%) participated in the survey. A low level of wellbeing was observed in 23% of participants, and was significantly associated to job strain (OR = 2.18; 95%CI = [1.32-3.60]), low social support (OR = 3.13; 95%CI = [2.05-4.78]) and the experience of very poor university teaching (OR = 2.51; 95%CI = [1.29-4.91]). A high level of psychological distress was identified for 13% of participants, and associated with low social support (OR = 2.41; 95%CI = [1.48-3.93]) and the experience of very poor university teaching (OR = 2.89, 95%CI = [1.16-7.21]).

Conclusion

Hospital working conditions, social support, and the perception of teaching quality, were three major determinants of wellbeing and psychological distress among health profession residents. Demographic determinants, personal life and lifestyle habits were also associated. This supports a multilevel action in prevention programs aiming to enhance wellbeing and reduce mental distress in this specific population and local organizational specificities.

Personality Traits, Burnout, and Psychopathology in Healthcare Professionals in Intensive Care Units-A Moderated Analysis

This study explored the associations between personality dimensions, burnout, and psychopathology in healthcare professionals in intensive care units (ICUs). This study further aimed to discern the differences in these relationships when considering the variables of critical care experience (less than 5 years, 5-10 years, and more than 10 years), profession (nurses versus intensivists), and the urban size of the city where the ICU is located (metropolitan cities versus smaller urban cities). This cross-sectional investigation's outcomes are based on data from 503 ICU personnel, including 155 intensivists and 348 nurses, in 31 ICU departments in Greece. Participants underwent a comprehensive assessment involving a sociodemographic questionnaire, the Eysenck Personality Questionnaire (EPQ), the Maslach Burnout Inventory (MBI), and the Symptom Checklist-90 (SCL-90). To analyze the interplay among critical care experience, burnout status, and psychopathology, a moderation analysis was conducted with personality dimensions (i.e., psychoticism, extraversion, and neuroticism) serving as the mediator variable. Profession and the urban size of the ICU location were considered as moderators influencing these relationships. Male healthcare professionals showed higher psychoticism levels than females, aligning with prior research. Experienced nurses reported lower personal achievement, hinting at potential motivation challenges for professional growth. Psychoticism predicted high depersonalization and low personal achievement. Neuroticism and psychoticism negatively impacted ICU personnel's mental well-being, reflected in elevated psychopathology scores and burnout status. Psychoticism appears to be the primary factor influencing burnout among the three personality dimensions, particularly affecting intensivists. In contrast, nurses are more influenced by their critical care experience on their mental health status.

An artificial intelligence approach to predicting personality types in dogs

Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted to develop reliable personality assessment tools for dogs. Most previous work has analysed dogs' behavioural patterns collected via questionnaires using traditional statistical analytic approaches. Artificial Intelligence has been widely and successfully used for predicting human personality types. However, similar approaches have not been applied to data on canine personality. In this research, machine learning techniques were applied to the classification of canine personality types using behavioural data derived from the C-BARQ project. As the dataset was not labelled, in the first step, an unsupervised learning approach was adopted and K-Means algorithm was used to perform clustering and labelling of the data. Five distinct categories of dogs emerged from the K-Means clustering analysis of behavioural data, corresponding to five different personality types. Feature importance analysis was then conducted to identify the relative importance of each behavioural variable's contribution to each cluster and descriptive labels were generated for each of the personality traits based on these associations. The five personality types identified in this paper were labelled: "Excitable/Hyperattached", "Anxious/Fearful", "Aloof/Predatory", "Reactive/Assertive", and "Calm/Agreeable". Four machine learning models including Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naïve Bayes, and Decision Tree were implemented to predict the personality traits of dogs based on the labelled data. The performance of the models was evaluated using fivefold cross validation method and the results demonstrated that the Decision Tree model provided the best performance with a substantial accuracy of 99%. The novel AI-based methodology in this research may be useful in the future to enhance the selection and training of dogs for specific working and non-working roles.

Development and psychometric properties testing of the Non-Cognitive Skills Scale for nursing students

BACKGROUND

Non-cognitive skills are crucial for nursing students to achieve their academic performance successfully and perform clinical practice effectively. Although different non-cognitive assessment tools have been established in various disciplines, few are focused on nursing students.

OBJECTIVE

The study aims to develop and psychometrically validate the Non-Cognitive Skills Scale (NCSS) for nursing students.

DESIGN

Qualitative and quantitative research design.

SETTING

One university in Singapore.

PARTICIPANTS

A total of 347 nursing educators and students participated in this study.

METHODS

A two-phase design was used. In the first phase, focus group discussions and literature reviews were conducted to generate the preliminary items of the NCSS. Content validity was established with an expert panel of 10 members, and the tool was pilot tested on 20 students. In the second phase, the number of items was reduced based on correlation coefficients and factor loading. The psychometric properties of the NCSS were tested on survey responses from 300 students. The Brief Resilience Scale, Trait Emotional Intelligence Questionnaire, and Big Five Inventory-10 were used to investigate concurrent and convergent validity.

RESULTS

A 39-item six-factor NCSS was developed with satisfactory model fit indices (χ2/df: 1.60, IFI: 0.92, TLI: 0.90, CFI: 0.92 and RMSEA: 0.05). Content validity was confirmed with a content validity index of 0.90. A pilot test was conducted to improve readability and clarity. Known-group validity discriminated against age and gender group differences in a predictable way. Concurrent validity and convergent validity were confirmed with acceptable correlation coefficients. Cronbach's α was 0.91, and test-retest reliability was 0.84.

CONCLUSIONS

The NCSS is a reliable and valid tool for assessing the non-cognitive attributes of nursing students. This scale can be considered an admission tool for selecting the suitability of nursing applicants for nursing programmes. Further validation of NCSS is needed using a longitudinal study design in multiple settings across countries.

Healthcare employment as a risk factor for functional neurological disorder: A case-control study

BACKGROUND AND PURPOSE

Female gender, younger age and stressful life events are known predisposing factors for functional neurological disorders (FNDs). Employment in a healthcare profession has also been suggested to be a predisposing factor. We set out to conduct a large-scale case-control study to estimate the rate employment in a healthcare profession among people with FND.

METHODS

We included 200 consecutive patients with a confirmed diagnosis of FND, referred to our clinic at University Hospital Bern Switzerland between October 1, 2016, and August 1, 2019. In addition, we included a control group of 200 patients with a confirmed neurological disorder, matched for age and gender, seen during the same period. The primary endpoint was to compare the prevalence of healthcare professionals between the groups. We also describe the clinical manifestations and concomitant psychiatric diagnoses in the FND cohort.

RESULTS

Female gender was predominant (70%), and the participants' mean age was 37 years. The proportion of healthcare professionals in the FND patients was 18% (33/186), which was significantly higher than in the control group, in which it was 10.6% (17/189; p = 0.019, 95% confidence interval odds ratio 1.168-4.074). Most healthcare professionals in both cohorts were nurses (21/33 among FND patients, 10/17 among controls). Among FND patients, 140 (70%) had motor symptoms and 65 (32.5%) had a concomitant psychiatric diagnosis.

CONCLUSION

This case-control study confirmed a higher rate of employment in healthcare professions in patients with FND, suggesting two potential mechanisms of FND: exposure to models/specific knowledge about neurological symptoms or stress-related professional factors. This warrants further studies on underlying mechanisms and prevention.

Oncology nurse competency in chimeric antigen receptor T-cell therapy: A qualitative study

BACKGROUND

Chimeric antigen receptor (CAR) T-cell therapy is a promising therapeutic method in the field of tumor immunotherapy. An increasing number of patients are beginning to select CAR T-cell therapy in mainland China. It is characterized by a complex process, a long period of treatment, high individualization, quick disease status changes and unique side effects. Oncology nurses play a crucial role in the provision of CAR T-cell therapy.

OBJECTIVE

To explore oncology nurses' competency in CAR T-cell therapy with guidance from the iceberg model.

DESIGN

A descriptive qualitative study.

SETTINGS

This study was conducted with nurses on lymphoma wards in three tertiary hospitals in China.

PARTICIPANTS

A total of 13 nurses and 2 nursing managers were approached, and all of them took part in this study.

METHODS

Data were collected face-to-face or via online video using a semistructured interview guide between November 2022 and February 2023 by the first author. The study was performed and reported following the Consolidated Criteria for Reporting Qualitative Research. Content analysis was used to analyze the data.

RESULTS

Nursing competency in CAR T-cell therapy was identified as including four main categories encompassing 12 subcategories and 40 codes. The main categories were theoretical knowledge, operation skills, personality traits and motives. The subcategories were basic knowledge of chemotherapy, professional knowledge of CAR T-cell therapy, basic skills, professional skills, relevant discipline skills, communication/coordination ability, critical thinking, basic traits, professional personality, enthusiasm for the nursing profession, empathy with patients and motivation to promote professional development.

CONCLUSIONS

Medical staff can develop a competency-based nurse training program to improve the professional competencies of oncology nurses in CAR T-cell therapy and meet patients' supportive needs for optimal care. Additionally, the findings may be helpful for building measurement standards to assess oncology nurses' performance.

The Association between Nursing Students' Happiness, Emotional Intelligence, and Perceived Caring Behavior in Riyadh City, Saudi Arabia

BACKGROUND

Caring behavior is a major focus of the nursing profession and an important dimension of nursing practice that sets nurses apart from other healthcare professionals. Effective patient-centered care requires ensuring nurses have the emotional intelligence and happiness to address the daily demands of practice. The purpose of this study is to examine the emotional intelligence and happiness among nursing students and their relationship with caring behaviors.

METHODS

A cross-sectional, descriptive correlational study was conducted on nursing students (n = 363) from Riyadh, Kingdom of Saudi Arabia, via an online survey. Measures include demographic data survey, Oxford Happiness Questionnaire, Trait Emotional Intelligence Questionnaire, and Caring Behaviors Inventory scale. Descriptive and multiple regression analyses were conducted for this study.

RESULTS

Nursing students reported their highest degree of caring was in terms of 'respectful differences to others', while their lowest was in 'knowledge and skills'. Emotional intelligence and happiness were significant predictors of caring behaviors and explained the variance in assurance of human presence (17.5%), knowledge and skills (17.5%), respectful differences to others (18%), and positive connectedness (12.9%). In the final regression model, emotional intelligence and happiness were significant predictors of caring behaviors and explained 19.5% of the variance.

CONCLUSIONS

Emotional intelligence and happiness among nursing students were found to be important factors to improve their caregiving behaviors. Therefore, nursing educators should consider integrating emotional intelligence and happiness interventions for students into their curriculum.

Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023

Background

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) provides clinicians an overview of Artificial Intelligence, focused on the management of patients with obesity.

Methods

The perspectives of the authors were augmented by scientific support from published citations and integrated with information derived from search engines (i.e., Chrome by Google, Inc) and chatbots (i.e., Chat Generative Pretrained Transformer or Chat GPT).

Results

Artificial Intelligence (AI) is the technologic acquisition of knowledge and skill by a nonhuman device, that after being initially programmed, has varying degrees of operations autonomous from direct human control, and that performs adaptive output tasks based upon data input learnings. AI has applications regarding medical research, medical practice, and applications relevant to the management of patients with obesity. Chatbots may be useful to obesity medicine clinicians as a source of clinical/scientific information, helpful in writings and publications, as well as beneficial in drafting office or institutional Policies and Procedures and Standard Operating Procedures. AI may facilitate interactive programming related to analyses of body composition imaging, behavior coaching, personal nutritional intervention & physical activity recommendations, predictive modeling to identify patients at risk for obesity-related complications, and aid clinicians in precision medicine. AI can enhance educational programming, such as personalized learning, virtual reality, and intelligent tutoring systems. AI may help augment in-person office operations and telemedicine (e.g., scheduling and remote monitoring of patients). Finally, AI may help identify patterns in datasets related to a medical practice or institution that may be used to assess population health and value-based care delivery (i.e., analytics related to electronic health records).

Conclusions

AI is contributing to both an evolution and revolution in medical care, including the management of patients with obesity. Challenges of Artificial Intelligence include ethical and legal concerns (e.g., privacy and security), accuracy and reliability, and the potential perpetuation of pervasive systemic biases.