What is personality? Two myths and a definition

Relationship between sun exposure and seasonal affective disorder symptoms in rural older people with different personalities: a cross-sectional study

OBJECTIVES

Evidence suggests that environmental support, personality traits, and psychological factors can influence seasonal changes in human mood and behavior, particularly in rural middle-aged women and older people. This study aimed to quantify the associations between personality traits, seasonal affective disorder symptoms, and sun exposure in rural older people.

METHODS

This study is a cross-sectional analytical study, the participants were 300 rural older persons from 12 natural villages and 5 geriatric service centers in 4 different cities in Jiangxi Province, China. The Eysenck Personality Questionnaire (EPQ), the Personal Inventory of Depression and Seasonal Affective Disorder (PIDS-SA-SimpChi), and the Sunlight Exposure Scale were used to conduct follow-up interviews throughout the year. Spatial analysis was performed using ArcGIS and Geodetic Probes. The data were analyzed using SPSS 21 and Amos 23.0 mediated models.

RESULTS

Rural older people with low sun exposure exhibited higher personality trait scores (p < 0.001). Personality traits were directly associated with seasonal affective disorder symptoms(p < 0.01); Sun exposure mediated this effect in rural older people (p < 0.05).

CONCLUSION

High-scoring personalities are more typical of rural older people with low sun exposure, and there is a greater risk of emotional and behavioral instability. Latitudinal differences are not a determinant of SAD. Increased sun exposure is associated with symptom relief. The promotion of light therapy devices in rural areas with low sunlight is warranted.

EFL teachers' burnout in technology enhanced instructions setting: The role of personality traits and psychological capital

The COVID-19 pandemic has caused a great alteration in teaching methodologies by adding technology to the syllabi. Indeed, the integration of technology has presented significant opportunities for language teachers to adjust to the current situation. However, it gives rise to challenges and stressors that cause burnout. Beyond work-associated issues, burnout is believed to be influenced by various factors, and with the advent of the Positive Psychology (PP) approach, there has been a shift in understanding that negative emotional states experienced by individuals can be redirected toward a positive trajectory. This shift has spurred an increasing interest in investigating the value of teachers' Psychological Capital (PsyCap) in dealing with burnout. Furthermore, the study recognizes the significance of other personal factors, including individual differences and the personality of teachers. Therefore, the present study makes efforts to unravel the intricate relationships among personality traits, PsyCap, and burnout experienced by Chinese EFL teachers in the context of Technology-enhanced instructions (TEIs). To achieve this, a total of 550 Chinese EFL teachers currently engaged in TEI settings were selected, and they responded to three relevant questionnaires. Using Structural Equation Modeling (SEM), the study found that teachers' burnout was negatively associated with PsyCap and certain personality traits, specifically openness, extroversion, agreeableness, and conscientiousness. Conversely, an increase in neuroticism was found to be positively associated with higher levels of teachers' burnout. These results highlight the importance of enhancing PsyCap and fostering positive personality traits to mitigate burnout among EFL teachers in technology-enhanced instructional settings.

Personality and Pain Outcomes in Rheumatic Disease: The Mediating Role of Psychological Flexibility

BACKGROUND

Chronic pain is associated with increased disability and vulnerability to emotional disorders. Personality and psychological flexibility (PF) describe interindividual differences that shape the adjustment to chronic pain. Specifically, PF was found to be associated with pain, fatigue, anxiety, and depression intensity. Although previous studies established strong correlations between personality and pain outcomes, evidence on the nature of this relationship is scarce. Therefore, the objective of this study is to explore the mediating effect of PF on the relationship between personality and distress.

METHODS

This transversal study included 108 participants (age M = 56.7, SD = 11.3) diagnosed with musculoskeletal chronic pain. Self-reported measures were administered by the medical care team. Multiple mediation models were performed for estimating the indirect effects on each outcome variable.

RESULTS

After controlling for age and gender covariates, we found that PF completely mediated the relationship between personality traits and all pain outcomes and partially mediated the impact of extraversion on anxiety. In addition, emotional stability also had an indirect effect on anxiety through PF.

CONCLUSIONS

Personality traits and PF are significant predictors of pain outcomes. PF represents a core process mediating the impact of personality traits on the perceived intensity of pain, fatigue, anxiety, and depression in patients with rheumatic disease. These results could facilitate the application of individualized psychological interventions in clinical contexts targeting the reduction of emotional avoidance and in chronic pain patients.

Social Elite in Imperial China: Their Destinies as Documented by the Historical Literature and Their Personality as Defined by the Contemporary Five-Factor Model

Background: The association between personality and life outcome has been widely studied in Western countries, and one might question whether the association exists in China. The official documentation from the Twenty-Six Histories of Imperial China, which presents life-long data on the social elite, may offer a convenient way to realize this effort. Meanwhile, a possible association might help identify competent personalities and offer treatment hints for personality disorders or other psychiatric deviations worldwide. Methods: Based on these historical records (about 618–1911 AD) on 18 social elite groups with long longevity (Macrobian group) and 30 with normal lifespans (Control group), we assessed personality traits/facets using the revised NEO Personality Inventory (NEO-PI-R) and destiny using the Destiny Evaluation Questionnaire (DEQ). Results: Compared to the Controls, the Macrobian group scored higher on the DEQ’s Health and Destiny in General and lower on the NEO-PI-R traits Openness to Experience and Extraversion and facets such as Openness to Fantasy, Openness to Aesthetics, Openness to Feelings, Excitement-Seeking, and Self-Consciousness. In the Macrobian group, the Trust and Compliance facets predicted the DEQ’s Family and Marriage and Social Relationships aspects, respectively; Conscientiousness and its facets Dutifulness, Self-Discipline, and Competence predicted Family and Marriage, Career Achievement, and Destiny in General, respectively; and the Self-Consciousness facet predicted worse performance in Career Achievement, Family and Marriage, and Social Relationships and the Depression facet of Destiny in General. In the Control group, Openness to Feelings positively and Anxiety negatively predicted Health. Conclusions: Less self-focused attention and more interdependence between individuals were beneficial to several aspects of individual destiny in Imperial China, which might be profound for the individual career development and clinical treatment of personality disorders in contemporary society.

Effect of Personality Type on the Occurrence of Temporomandibular Disorders-A Cross-Sectional Study

BACKGROUND

Personality traits are one of the major factors influencing the behavior and functioning of an individual, and they play a crucial role in the development of psychosomatic disorders and diseases. This paper aimed to evaluate the importance of personality traits in temporomandibular disorder (TMDs) development using the NEO-FFI Personality Inventory by Paul Costa and Robert McCrae (the Five-Factor Model of Personality, known as the Big Five). Moreover, the relationship between personality type and the intensity of dysfunctional changes in the stomatognathic system was assessed using the NEO-FFI Personality Inventory by Paul Costa and Robert McCrae (the Five-Factor Model of Personality, known as the Big Five).

MATERIAL AND METHODS

The study included a group of 75 adult participants (aged 19-52) with TMD diagnosed according to DC/TMD criteria and a control group of 75 participants without symptoms of dysfunction. The study consisted of a questionnaire and clinical study; the questionnaire included the NEO-FFI psychological questionnaire and a self-authored one. The clinical part consisted of extra- and intraoral dental examinations.

RESULTS

Participants who clenched their teeth showed a greater degree of conscientiousness than those who did not exhibit this symptom (p = 0.048). Presence of headaches was correlated with greater severity of neuroticism (p = 0.001). Moreover, participants with enamel cracks showed a lower intensity of extraversion (p = 0.039), and those with worn hard dental tissues showed a higher intensity of neuroticism (p = 0.03), a lower intensity of conscientiousness (p = 0.01), and a lower intensity of extroversion (p = 0.046). Acoustic symptoms during mandibular movements were found to be linked with a higher level of neuroticism (p = 0.020), a lower level of extraversion (p = 0.035), and a lower level of conscientiousness, whereas pain upon mandibular movements were linked to a lower level of conscientiousness (p = 0.025). Participants with pain upon palpation of the masticatory muscles showed a lower level of conscientiousness (p = 0.01) compared to those without pain symptoms. Episodes of mandibular blockage or problems with its adduction depend on the intensity of conscientiousness (p = 0.007). Moreover, people from the study group with high levels of neuroticism showed lower protrusion values (p = 0.016).

CONCLUSION

The intensity of individual personality traits was found to be associated with some TMDs in comparison to healthy controls.

Text-based automatic personality prediction using KGrAt-Net: a knowledge graph attention network classifier

Nowadays, a tremendous amount of human communications occur on Internet-based communication infrastructures, like social networks, email, forums, organizational communication platforms, etc. Indeed, the automatic prediction or assessment of individuals' personalities through their written or exchanged text would be advantageous to ameliorate their relationships. To this end, this paper aims to propose KGrAt-Net, which is a Knowledge Graph Attention Network text classifier. For the first time, it applies the knowledge graph attention network to perform Automatic Personality Prediction (APP), according to the Big Five personality traits. After performing some preprocessing activities, it first tries to acquire a knowing-full representation of the knowledge behind the concepts in the input text by building its equivalent knowledge graph. A knowledge graph collects interlinked descriptions of concepts, entities, and relationships in a machine-readable form. Practically, it provides a machine-readable cognitive understanding of concepts and semantic relationships among them. Then, applying the attention mechanism, it attempts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. We used 2467 essays from the Essays Dataset. The results demonstrated that KGrAt-Net considerably improved personality prediction accuracies (up to 70.26% on average). Furthermore, KGrAt-Net also uses knowledge graph embedding to enrich the classification, which makes it even more accurate (on average, 72.41%) in APP.

Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning

Social network analysis (SNA) is an increasingly popular and effective tool for modeling psychological phenomena. Through application to the personality literature, social networks, in conjunction with passive, non-invasive sensing technologies, have begun to offer powerful insight into personality state variability. Resultant constructions of social networks can be utilized alongside machine learning-based frameworks to uniquely model personality states. Accordingly, this work leverages data from a previously published study to combine passively collected wearable sensor information on face-to-face, workplace social interactions with ecological momentary assessments of personality state. Data from 54 individuals across six weeks was used to explore the relative importance of 26 unique structural and nodal social network features in predicting individual changes in each of the Big Five (5F) personality states. Changes in personality state were operationalized by calculating the weekly root mean square of successive differences (RMSSD) in 5F state scores measured daily via self-report. Using only SNA-derived features from wearable sensor data, boosted tree-based machine learning models explained, on average, approximately 28-30% of the variance in individual personality state change. Model introspection implicated egocentric features as the most influential predictors across 5F-specific models, with network efficiency, constraint, and effective size measures among the most important. Feature importance profiles for each 5F model partially echoed previous empirical findings. Results support future efforts focusing on egocentric components of SNA and suggest particular investment in exploring efficiency measures to model personality fluctuations within the workplace setting.

Trajectories and predictors of social avoidance in female patients with breast cancer

Background

Social avoidance plays an important role in influencing quality of life among patients with breast cancer. Social avoidance behaviors change with treatment periods. However, the trajectory patterns and the predictive factors have not been fully studied.

Objective

This study examined the growth trajectory of social avoidance and its predictors in patients with breast cancer.

Materials and methods

A total of 176 patients with breast cancer in a university hospital in Shaanxi Province, China, were followed up four times over 6 months following surgery, and data from the final 144 patients were analyzed. The growth mixed model (GMM) was used to identify the trajectory categories, and the predictive factors of the trajectory types were analyzed by logistic regression.

Results

The best-fit growth mixture modeling revealed three class models: persistent high social avoidance group (Class 1), social avoidance increased first and then decreased group (Class 2), and no social avoidance group (Class 3), accounting for 13.89, 31.94, and 54.17% of patients, respectively. Single-factor analysis showed that family income per capita, residence, and temperament type were related to the social avoidance trajectory. Logistic regression analysis showed that only temperament type was an independent predictor of the social avoidance trajectory, and patients with melancholia were more likely to have persistent high social avoidance.

Conclusion

Our study proved the heterogeneity of social avoidance behaviors and the influencing effect of temperament type on the development of social avoidance behaviors in Chinese patients with breast cancer. Health professionals should pay more attention to patients who are at higher risk of developing a persistent social avoidance pattern and provide target interventions.

A framework for antecedents of social entrepreneurial intention: Empirical evidence and research agenda

Social entrepreneurship (SE) increasingly contributes to diversity in entrepreneurship. The different approaches to SE suggest a variety of antecedents which drive individuals' intention to become social entrepreneurs. While this variety of antecedents is insightful, it also creates a need for systemisation and prioritization. We address this need by introducing an integrative, multi-level framework for person-based antecedents of SE-intention. Based on this multi-level framework the antecedents are grouped on three theoretical levels which refer to an individual's (1) personality, (2) cognition, and (3) entrepreneurial exposition. When testing our framework with 499 South African University students we find support for the multi-level framework and its notion that antecedents from the diverse levels complement each other. Therefore, this study provides a structure for person-based antecedents of SE-intention and additionally points to future research which may extend the proposed framework.

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications takes place there. The most prominent tool in such communications is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text, a knowledge graph, which is a set of interlinked descriptions of concepts, was built by matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving a more powerful representation, the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph, which is now a knowledgeable alternative for the input text, was embedded to yield an embedding matrix. Finally, to perform personality predictions, the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The results indicated considerable improvements in prediction accuracies in all of the suggested classifiers.

Do Social Ties Moderate the Association between Childhood Maltreatment and Gratitude in Older Adults? Results from the NEIGE Study

Background: Childhood maltreatment can impede gratitude, yet little is known about the older population and its moderators. The aim of this study is to clarify the association between childhood maltreatment and levels of gratitude of the older population, and the moderating effect of social ties on the association. Methods: We analyzed the data of 524 community-dwelling older adults aged 65–84 years without functional disabilities in Tokamachi City, Niigata, Japan, collected for the Neuron to Environmental Impact across Generations (NEIGE) study in 2017. Using a questionnaire, the participants rated three types of childhood maltreatment before the age of 18 (physical abuse, emotional neglect, and psychological abuse), level of gratitude, and social ties. Results: We found an inverse association between emotional neglect and gratitude. Furthermore, emotional neglect was inversely associated with gratitude only for those with lower levels of social ties. Conclusions: Promoting social ties may mitigate the adverse impact of emotional neglect on the level of gratitude.

Reliability and Validity of a Dog Personality and Unwanted Behavior Survey

Dogs have distinct, consistent personalities, but the structure of dog personality is still unclear. Dog personality and unwanted behavior are often studied with behavioral questionnaires. Even though many questionnaires are reliable and valid measures of behavior, all new questionnaire tools should be extensively validated. Here, we examined the structure of personality and six unwanted behavior questionnaire sections: noise sensitivity, fearfulness, aggression, fear of surfaces and heights, separation anxiety and impulsivity/inattention with factor analyses. Personality consisted of seven factors: Insecurity, Training focus, Energy, Aggressiveness/dominance, Human sociability, Dog sociability and Perseverance. Most unwanted behavior sections included only one factor, but the impulsivity/inattention section divided into two factors (Hyperactivity/impulsivity and Inattention) and the aggression section into four factors (Barking, Stranger directed aggression, Owner directed aggression and Dog directed aggression). We also examined the internal consistency, test-retest reliability, inter-rater reliability and convergent validity of the 17 personality and unwanted behavior traits and discovered excellent reliability and validity. Finally, we investigated the discriminant validity of the personality traits, which was good. Our findings indicate that this personality and unwanted behavior questionnaire is a reliable and valid tool that can be used to study personality and behavior extensively.

The effects of gender, educational level, and personality on online learning outcomes during the COVID-19 pandemic

With the rampant pandemic of COVID-19, an increasing number of people are acquiring knowledge through online learning approaches. This study aims to investigate how to improve online learning effectiveness during this special time. Through a mixed design, this study revealed the effect of educational levels, gender, and personality traits on online learning outcomes. It was concluded that postgraduates (N = 599) outperformed undergraduates (N = 553) in online learning, learners (N = 1152) with strong personality traits such as agreeableness, conscientiousness, and openness to a new experience outperformed those with strong extraversion and neuroticism. Future research could improve interpersonal interactions and encourage learners to post words in the online discussion forum, focus on how to design scaffolding online learning and how to improve the quality and dynamic of the online contents, and highlight blended learning rather than either merely online or traditional face-to-face learning.