Personality Features Research Articles

Personalised monitoring and feedback on risk-relevant features in forensic case formulation: a series of case-studies in men who have committed sexual offences

ABSTRACT The current study investigated to what extent personalised information on interrelated risk-relevant behavioural, psychological, and contextual features obtained using experience sampling method (ESM) can be deployed to inform forensic case formulations of adult men with a history of sexual offences. Five adult men in outpatient forensic treatment for committing sexual offences monitored personal risk-relevant features using ESM and discussed the resulting feedback report with their therapist. Data were collected using the Twente engagement with Ehealth technologies Scale, an online questionnaire, a semi-structured interview and log data. Participants reported increased awareness of personal patterns of risk-relevant features and their possible association with the risk of sexual reoffending. The participants did not perceive the ESM procedure as burdensome or intrusive. Obtaining personalised information on risk-relevant features by ESM was feasible and achievable for adult men with a history of sexual offences. Insights derived from ESM measurements could enhance traditional forensic case formulation. PRACTICE IMPACT STATEMENT Combining experience sampling method (ESM) and traditional forensic case formulation can help adult men with a history of sexual offences to better understand patterns in their risk-relevant characteristics. A more thorough understanding of such patterns may improve treatment plans focused on desistance of sexual reoffending.

Examining Teachers’ Professional Learning in an Online Asynchronous System: Personalized Supports for Growth and Engagement in Learning to Teach Statistics and Data Science

Teachers’ professional learning often includes online components. This study examined how a case of 37 teachers utilized a specific online asynchronous professional learning platform designed to support teachers’ growth in learning to teach statistics and data science in secondary schools in the United States. The platform’s features and learning materials were designed based on effective online learning designs, supports for self-guided learning, and research on the teaching and learning of statistics and data science. We paid particular attention to the features we designed into the platform to support self-regulation and personalizing the experiences to meet their preferred learning goals such as allowing for free choice of learning materials, flexibility of when and how long to engage, providing personal recommendations based on user input, internal systems to track progress, and generating certificates of completion. In this study, we used a case study with both quantitative and qualitative data to examine whether teachers had gains in meeting learning goals related to their development in teaching statistics and data science, had sustained engagement, and found the features for personalization supportive for their learning. Results showed, overall, positive growth towards meeting learning goals and making small changes towards improved classroom practice. Most teachers were generally engaged in sustained ways across the study period, though we found six different patterns of completion that highlight ways in which teachers’ goal-directed and self-regulated learning occurred within the busy schedules of educators. Several personalized features, especially the recommendations and tracking system, were highly utilized and perceived as supportive of teachers’ learning.

Associations Between Biodiversity, Forest Characteristics, Distance to Blue Space and Accelerometer-Measured Physical Activity—Northern Finland Birth Cohort 1966 Study

We studied how the quantity and quality of residential green spaces and distance to different types of blue spaces are associated with the accelerometer-measured physical activity (PA) among adults with different levels of total daily PA. The associations of the participants’ socio-demographic and personal characteristics and environmental features with light PA (LPA) and moderate-to-vigorous PA (MVPA) were analyzed using linear mixed models (n = 5470). The results indicated that greater residential forest area was associated with higher LPA among those with high level of total daily PA and with less MVPA among those with low level of total daily PA. Environments with high tree stand structure diversity or close to sea were associated with higher MVPA in high PA group. Varied natural environment can encourage leisure time activities in nearby nature, but individuals’ personal and socioeconomic variables seemed to be more strongly associated with LPA and MVPA than environmental characteristics.

Assessing the contributions of gender, clinical symptoms, and psychometric traits to non-suicidal self-injury behaviors in Chinese adolescents: a nomogram approach.

Non-suicidal self-injury (NSSI) behaviors among adolescents with mood disorders represent a significant global public health concern. This study aimed to assess the prevalence and identify key predictors of NSSI behaviors in Chinese adolescents diagnosed with depressive or bipolar disorders, addressing a critical gap in the literature. Data from 2343 participants in the Chinese Adolescent Depression Cohort (CADC) were analyzed. The study employed a comprehensive approach, utilizing medical records, clinical assessments, and validated psychometric instruments. Statistical analyses included chi-square tests, logistic regression, and ROC curve analyses, culminating in the development of a predictive nomogram. The prevalence of NSSI behaviors was strikingly high at 73.32%. Significant predictors included female gender (AOR = 2.14, 95% CI [1.70, 2.68]), presence of hallucinations (AOR = 1.52, 95% CI [1.18, 1.97]), borderline personality features (AOR = 1.03, 95% CI [1.01, 1.08]), and childhood trauma (AOR = 1.02, 95% CI [1.01, 1.03]). The study's key contribution is a predictive nomogram with an AUC of 0.74, demonstrating good efficacy in predicting NSSI behaviors. This research reveals an alarmingly high prevalence of NSSI behaviors in Chinese adolescents with mood disorders and identifies critical predictors spanning demographic, clinical, and psychometric domains. The developed nomogram offers a novel approach for risk assessment, highlighting the importance of comprehensive evaluations in adolescent mental healthcare. These findings have significant implications for developing targeted interventions and improving risk assessment strategies in clinical practice.

Pronouns beyond phi-features: the speaker–addressee relation in Japanese pronouns and its implications for formal pronouns

Abstract Greenberg’s Universal 42 states that all languages have pronominal categories involving at least three persons and two numbers. However, this characterization fails to capture the properties of pronouns in Japanese, which are not bundles of person, number and gender features (so-called phi-features); rather, they contain sociolinguistic information about the interlocutors. We propose that these properties are structurally determined. Following Ritter and Wiltschko, we assume that the highest layer of structure in nominals is interactional structure. As for phi-features, we adopt the standard assumption that they are represented internal to the determiner phrase (DP). We propose that the distinctive properties of Japanese pronouns follow from the hypothesis that they spell out elements of the interactional structure and not the DP. We show that the lack of phi-features in Japanese pronouns correlates with other properties of this language’s grammar. Support for this analysis comes from languages where pronouns with phi-features can optionally be used to encode formality (e.g. German and French). We propose that in these languages, formal pronouns originate within the DP but are interpreted in the interactional structure. Finally, we suggest that this analysis may extend to imposters and vocatives in that they may also be interpreted in the interactional structure.

Личностные особенности и самоотношение лиц с психогенным перееданием

<p><strong>Background. </strong>According to the World Health Organization, in 2023, excess weight was observed in 2,5 billion adults aged 18 and older. Individuals with obesity suffer from chronic somatic diseases, face social isolation. Understanding the psychological and social factors behind weight gain allows us to develop effective programs to prevent obesity and help people lose weight. <strong>Objective.</strong> The aim of this study is to analyze the analysis of personality traits and self-attitudes in individuals with excess body mass. <strong>Hypothesis.</strong> Individuals with psychogenic overeating have an increased level of neuroticism, perfectionism and internal conflict, as well as reduced self-acceptance. <strong>Materials and methods. </strong>101 people with eating disorders were examined. All respondents were divided into three groups according to the criterion of body mass index (BMI): 1 group - 33 people with pre-obesity and obesity of the first degree, average age 40±13 years; 2 group - 20 people with second degree obesity, average age 35±12 years; group 3 - 48 people with third degree obesity, average age 44±10 years. The socio-demographic characteristics of the respondents and medical documentation data were studied. Psychological testing was conducted using: the KON-2006 Neurotic Personality Questionnaire to assess neurotic personality traits, the Multidimensional Perfectionism Scale (MPS), a questionnaire to study self-esteem. <strong>Results. </strong>Analysis of the study results showed a high level of neuroticism for individuals with different BMIs in all groups, the violation of self-attitude is primarily associated with the perception of one's appearance, perfectionism is not a personal feature associated with psychogenic overeating. Low self-esteem of external data is combined in them with narcissism and a positive assessment of their personal characteristics. <strong>Conclusions. </strong>The results obtained can be used to develop and improve the effectiveness of psychological weight correction programs for psychogenic overeating, based on taking into account the personal factors of obesity.</p>

Ideology: Psychological Similarities and Differences Across the Ideological Spectrum Reexamined.

A key debate in the psychology of ideology is whether leftists and rightists are psychologically similar or different. A long-standing view holds that left-wing and right-wing people are meaningfully different from one another across a whole host of basic personality and cognitive features. Scholars have recently pushed back, suggesting that left-wing and right-wing people are more psychologically similar than distinct. We review evidence regarding the psychological profiles of left-wing and right-wing people across a wide variety of domains, including their dispositions (values, personality, cognitive rigidity, threat-sensitivity, and authoritarianism), information processing (motivated reasoning and susceptibility to misinformation), and their interpersonal perceptions and behaviors (empathy, prejudice, stereotyping, and violence). Our review paints a nuanced picture: People across the ideological divide are much more similar than scholars sometimes appreciate. And yet, they differ-to varying degrees-in their personality, values, and (perhaps most importantly) in the groups and causes they prioritize, with important implications for downstream attitudes and behavior in the world.

Early remission of deliberate self-harm predicts emotion regulation capacity in adulthood: 12.4 years follow-up of a randomized controlled trial of adolescents with repeated self-harm and borderline features

AbstractEmotion regulation capacity, critical for adult functioning and mental health, develops strongly during adolescence in healthy individuals. Deficits in emotion regulation is often referred to as emotion dysregulation [ED] and is associated with various mental health problems, including repeated deliberate self-harm [DSH] which peaks in adolescence. Dialectical Behaviour Therapy for adolescents [DBT-A] systematically targets ED through strategies such as changing coping behaviours and has previously been shown to effectively induce DSH remission in adolescents. However, whether such remission is associated with improved emotion regulation capacity in adulthood, and whether this effect is mediated by changes in use of coping strategies has not been previously studied. Prospective long-term follow-up study of an RCT comparing DBT-A with enhanced usual care [EUC] for adolescents presenting to community child and adolescent psychiatric outpatient clinics with borderline personality features and repeated self-harm. Assessments included both structured interviews and self-report at baseline and 1.6, 3.1 and 12.4 years follow-up. In the final follow-up, adult ED was measured and data were collected for 61 (80%) of the original 77 participants. DSH remission was assessed at 1.6 years follow-up, and use of coping strategies at 3.1 and 12.4 years follow-ups. A mediation analysis was conducted within a causal inference framework. Both treatment groups increased their use of functional coping skills from adolescence to adulthood, while only DBT-A was associated with decreases in dysfunctional coping. There was a direct effect of DSH remission 1 year after treatment on adult ED, particularly for participants who did not receive DBT-A. There was a negative association between reductions in dysfunctional coping and adult ED, however this did not mediate the effect of DSH remission. This is the first study to report that early DSH remission in adolescence predicted lower ED in adulthood. These results highlight the importance of early DSH remission and provides new insight into the long-term relationship between DSH and ED. Clinical trial registration information: “Treatment for Adolescents with Deliberate Self-harm”; http://ClinicalTrials.gov/;NCT00675129

Can mobile apps facilitate more sustainable and healthier diets? A systematic review

Abstract Background One third of all greenhouse gas emissions come from the food system, and current diets are insufficient to meet environmental and public health goals. Digital health apps offer promise to change behaviour, but little is known about the effects of different types of apps. This systematic review assessed the effectiveness of mobile apps in facilitating dietary changes across adults from high-income countries. Methods Seven electronic databases (MEDLINE, EMBASE, PsycINFO, CINAHL, Global Health, GreenFILE, Web of Science), two trial registries (Cochrane Trials and Clinicaltrials.gov), and Google were systematically searched for intervention studies aiming to facilitate dietary change. Interventions were characterised by their use of messaging (e.g., motivational statements) and personalised features (e.g., tailored nutrition advice). Outcomes for three food groups were synthesised through meta-analysis. Results Of the 5261 identified records, 21 met the eligibility criteria and were included. Interventions, recruiting 73,973 participants in total, lasted from three days to six months. Overall positive changes were observed in daily servings of fruit and vegetables (0.59, 95% CI 0.20, 0.99), legumes (0.05, 95% CI 0.01, 0.08), and meat (-0.14, 95% CI -0.23, -0.05), remaining significant after removing non-randomised controlled trials. Changes in fruit and vegetable intake were more pronounced for users of apps with messaging components (0.75, 95% CI 0.18, 1.32) and personalisation (0.67, 95% CI 0.16, 1.19), and meat intake changes were larger for messaging-based apps (-0.21, 95% CI -0.27, -0.15) than apps without these features. Conclusions Our findings suggest to practitioners and policymakers that mobile apps could be a useful and innovative way to change behaviour. Within a wider behaviour change approach, apps could help facilitate diets that meet food-based dietary guidelines, are predominantly plant-based, and are protective of environmental and human health. Key messages • Overall, the results from 21 studies showed positive dietary changes towards more fruits, vegetables, and legumes and less meat consumption following the use of mobile apps. • Apps including messaging and personalised elements appeared most effective and may be important for driving changes in fruits, vegetables, and meat consumption.

Double-finite Serial Verbs in Sanskrit and later Indo-Aryan

Abstract Steever (1988. The serial verb formation in the Dravidian languages. Delhi: Motilal Banarsidass) introduced to South Asian linguistics the notion “Serial Verb” – a Dravidian construction in which two morphologically finite verbs agree in person and number features in the same sentence. I show that similar “double-finite” structures are found in Sanskrit/Indo-Aryan, although details differ. One double-finite construction, inherited from Proto-Indo-European, does not seem to have continued in the post-Vedic language, but others do, and similar structures occur in Modern Indo-Aryan languages. While the historical developments are relatively clear for early Indo-Aryan, from Vedic down to Pāli, there are questions about whether the double-finite constructions of Modern Indo-Aryan are inherited , since comparable structures have not been noted in late Middle Indo-Aryan Apabhraṁśa, the near-ancestor of (peninsular) Modern Indo-Aryan. In the conclusion I discuss alternatives to Modern Indo-Aryan inheritance, including areal and typological factors and the proposal by Milizia (2018. Sul presente neoindoarico a doppio verbo finito. Atti del Sodalizio Glottologico Milanese 12(n.s.). 93–111) that the perhaps most widespread Modern Indo-Aryan double-finite construction results from analogical developments. (Unfortunately, the latter proposal does not account for the other Modern Indo-Aryan double-finite constructions.)

Schizotypy 17 years on: Prediction of schizotypic individual differences in midlife.

The picture for the long-term prediction of schizotypic individual difference features in relation to schizotypy assessed earlier in life remains opaque. Whereas schizotypy assessed earlier in life, typically during the late teen years, has been shown to predict nonaffective psychotic illness as well as the presence of nonaffective psychotic features (Chapman et al., 1994; Lenzenweger, 2021), the presence in midlife (mid-30s) of nonpsychotic schizotypic features in those assessed for schizotypy earlier in life remains to be demonstrated. The current study, which reports on a 17-year follow-up study, addresses this void in the schizotypy literature. Seventeen years after an initial psychometric assessment for schizotypy, in a sample of emerging adults (age = 18) with no prior history of psychotic illness, Perceptual Aberration Scale scores predicted elevated schizotypal personality features, increased schizophrenia-related personality disorder features (particularly schizotypal and paranoid), and elevated schizophrenia proneness scores at age 35. This pattern of associations was maintained even after the removal of participants with a diagnosis of nonaffective psychosis. The associations also remained largely unchanged net of state anxiety levels at initial and later assessments. These results support the emergence or maintenance of schizotypic psychopathology features consistent with a model that views schizotypy as the underlying liability for schizotypic psychopathology phenotypes. The results also provide additional support for both the construct validity of the initial psychometric schizotypy measure (Perceptual Aberration Scale) as well as the validity of the psychometric high-risk paradigm. Longitudinal research remains an illuminating and informative approach to understanding the nature of schizophrenia-related psychopathology by utilizing time as an essential scientific lever. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Relationship of Tetrad Dark Personality Traits and Delinquent Behavior among Adolescents: Moderating Role of Social Support

The current research studies the relationship between Dark Tetrad personality dimensions: Machiavellianism, narcissism, psychopathy, sadism, and delinquency in adolescence while considering the role of social support as the moderator. For this study the cross-sectional research design was used and the sample included 300 adolescents comprising 150 males and 150 females in the age group of 10 to 19 years randomly selected from twin cities of Islamabad and Rawalpindi. The data were collected using the Short Dark Tetrad (SD4) self-reported delinquency – Problem Behavior Frequency Scale and Child and Adolescent Social Support Scale (CASSS). The analyses conducted found positive and significant correlations between the dark personality traits and the delinquent behaviors with each of the dark personality traits being positively correlated with the manifests of delinquent behaviour. Furthermore, the study revealed that social support acted as a moderator that could help decrease the delinquency rate among the adolescents who possess a high level of dark personality traits. These imply that dark personality facets and insufficient social support should be targeted to reduce delinquency among adolescents. The results imply that any prevention or treatment programs targeted at decreasing delinquency must take into account the presence of dark personality features in adolescents, especially through modulating the perceived usefulness of social support. The insights from the research can be quite useful for schools, families, and community-oriented programs to design effective interventions not only to address behavior management but also to address the potentially detrimental effects associated with these personality traits.

Goal-Setting, Active Practice, Self-Monitoring: a Web-Based System to Improve Programming Proficiency

Mastering programming fundamentals poses a significant challenge for students, especially in demanding higher education environments. This pilot study investigates the effectiveness of a web-based system, SoftwareSkills, designed to enhance programming proficiency among second-year bachelor students. SoftwareSkills leverages, active practice, and self-monitoring to support programming practice. Thirty-six students (n=36) participated in a quasi-experimental design, divided into independent exploration and instructional session groups. User interaction data, exercise performance, and behavioural patterns were analysed. The study revealed four distinct learning strategies: reviewing skill masteries, dedicating sufficient time to each question, striving for mastery, and adapting spaced practice. However, some students exhibited rapid responses and minimal practice, suggesting a need for deeper learning strategies. These findings highlight the suitability of self-regulated learning and deliberate practice in programming education. Future work will involve first and second-year programming students and integrate AI-driven personalised learning features to optimise the learning experience. This research contributes insights into effective practices and the potential of technology-aided learning to support student success in programming education.

Effects of childhood adversities on alexithymia features vary between sexes. Results of a prospective population study

ABSTRACT Introduction: Adverse childhood experiences (ACEs) associate with various mental disorders, including personality features. Our understanding of how ACEs influence alexithymia features in the general population is limited. In a prospective population setting, we studied whether ACEs associate with alexithymia, and the role of sex and emotional symptoms in this association. Methods: In a Finnish population-based prospective study, 3,142 individuals aged between 30 and 64 years completed eleven ACE questions and the Toronto Alexithymia Scale in 2000 and 2011, and the Hopkins Symptoms Checklist in 2011. The effect of ACEs on alexithymia and its subdomains – difficulty identifying feelings (DIF), difficulty describing feelings (DDF), and externally oriented thinking (EOT) in 2000 and 2011 – was analysed using repeated measures ANOVA. Results: The number of ACEs and their main component, childhood social disadvantage, associated positively with total alexithymia scores and its subdomains DIF and DDF, and negatively with EOT. After controlling for the effect of depression and anxiety, the strength of these associations was reduced, but the effect of social disadvantage on DIF and EOT remained significant in females. Childhood family conflicts associated positively with DIF in males and negatively with EOT in females. Additionally, maternal mental problems associated positively with DIF and DDF in females. Discussion: In the general population, ACEs, particularly social disadvantage, are associated with adult alexithymia features. Alexithymia features, detectable from youth, may predispose individuals to emotional disturbances caused by childhood adversities. The effect of family conflicts and maternal mental problems on alexithymia features varies between sexes.

When great places to work aren't for everyone: How top employer awards can backfire for narcissistic applicants

Drawing on signaling theory, the current experimental study (N = 593) investigated the influence of top employer awards on the connections between the full spectrum of narcissistic personality features (i.e., agentic, antagonistic, neurotic, and communal) and perceived company prestige. The results of our experiment show that employer awards enhance prestige perceptions and that all narcissism facets are positively related to perceptions of prestige. However, the positive associations between narcissistic personality features and company prestige were less pronounced in the award condition than in the no-award condition. Our study contributes to the recruitment and employer branding literature by demonstrating that, while employer awards generally enhance an organization’s prestige, they may not do so for individuals with particular personality features.

Edge Computing with Deep Learning and Internet of Things for Recognising and Predicting Students Emotions and Mental Health

A person's Mental Health (MH) dramatically influences their complete evolution in life, including their cognitive, emotional, and psychomotor components. A person with good MH is content with life and can be creative, learn new things, and take risks to accomplish more significant objectives. Currently, college students are dealing with MH concerns for various causes, which affect their academic performance and significantly contribute to poor academic results. Therefore, encouraging MH in college students presents a significant problem for educators, parents, teacher educators, and governments. Adolescence is a crucial and delicate time characterized by considerable physical, emotional, social, and religious changes. The physical, social, and psychological facets of an individual's growth are laid out in this period, with mental health as a crucial factor in promoting these gains. Therefore, it becomes crucial for researchers to use Deep Learning (DL) algorithms to study the association between MH and vital psychological characteristics, such as emotional intelligence, personality traits, and intelligence. The personal aspects, namely personality, emotional intelligence, and MH are all related ideas that influence one another. Individuals must have mental well-being and emotional harmony to have a good personality. The current study uses DL techniques to investigate the relationship between college students' MH, emotional intelligence, and personality features. To perform a thorough study on emotion identification and Mental Health Prediction (MHP) among college students, this project investigates the integration of edge computing enabled by the Internet of Things (IoT) in the context of intelligent systems. Innovative treatments are urgently needed due to this population's rising prevalence of MH issues. This paper aims to continuously monitor and predict college students' MH using Edge Computing (EC) and IoT technology.

Learning personalized drug features and differentiated drug-pair interaction information for drug–drug interaction prediction

Multi-drug combination therapies are increasingly used for complex diseases but carry risks of harmful drug interactions. Effective drug–drug interaction prediction (DDIP) is essential for assessing risks among numerous drug pairs. Most DDIP methods involve two main steps: drug representation and drug pair interaction extraction, respectively challenged by the loss of personalized drug information and the need for differentiated interaction data, and are rarely studied. Specifically, personalized drug information refers to the distinct features of each drug. These properties can be easily confused by neighboring information during graph propagation. This issue is especially prominent in drug interaction graphs with long-tail distributions, which poses challenges for personalized drug information learning. Furthermore, it is crucial to learn interactions with differentiation in order to identify diverse drug relationships. Some methods simply concatenate drug features, often ignoring the differences of different drug relationships, while other methods based on substructures rely on professional pharmacological knowledge and are computationally complex. To address these issues, we propose a novel method, learning personalized Drug Features and differentiated Drug-Pair interaction information for drug–drug interaction prediction (DFPDDI). This approach employs a contrastive learning network with edge-aware augmentations and mutual information estimators to capture personalized drug features across various graph distributions. Furthermore, it applies a mutual information constraint to drug-pair representations, enhancing the accuracy of interaction predictions by better distinguishing between different types of drug relationships. The results evaluated on three public datasets demonstrate competitive performance compared to baselines. It also shows potential for accurate predictions, particularly in imbalanced-distribution graphs.

To the problem of digital immortality

The idea of reproducing personality by means of digital (neural network) technologies (“digital immortality”), together with the concept of Digital Twin (DT), still attracts great attention. Recent advances in the DT industry permit to expect the production of perfect “mirror” DT in the near future. We argue that the “immortality in the memory of other people” could be approached quite closely due to creating an analogue of personal DT by simulating the personality. For this purpose, it is necessary to compose the “constructive portrait” of a chosen person (by extracting the key features and traits of personality) and try to reproduce it by means of a chosen model. We are developing an original model Natural Constructive Cognitive Architecture (NCCA) that inherently provides the interpretation of logical and intuitive thinking, subconscious, etc. This model should be adjusted to specific set of knowledge inherent in a particular person (books, films, photographs, etc.), with an emphasis on personal lexicons (verbal, emotion, behavioral). NCCA contains a large set of free model parameters, which enables us to reproduce a wide range of personality features, from thinking style to temperament. It is shown that popular Generative Pre-trained Transformers (GPTs) have much in common with NCCA and could be adapted and used as an analog of DT of a specific person. We argue that the proposed program would provide the possibility to create an analog of DT, which could give an impression (at least, an illusion) of communication with the desired specific person.

Qualifications of the Ideal Tourism School Administrator: A RepGrid Review

The school administrator has one of the most important roles in the success of an institution and in the execution of processes, especially in some fields like tourism including practical parts. For this success and effective management, there are certain qualities that the school administrator should have. The aim of this study is to examine the cognitive constructs of teachers and to reveal the qualities that the administrator working in tourism should have. Repertory Grid Technique was used in the study, which was prepared according to qualitative research patterns. The study group consisted of 40 teachers working in tourism schools, who worked with at least 6 different administrators. As a result of the study, honesty (η=8, 20.0%), (2) fairness (η=6, 15.0%), (3) personal feature; emotional intelligence; sensitivity (η=5, 12.5%) are the most important qualifications. When these results are taken into consideration, it becomes clear that ideal school administrators should pay attention to these qualifications. It is an important suggestion to pay attention to these qualities in the selection of tourism school administrators.

Subtypes of borderline personality features in adolescence: Insights from cross-lagged panel network analysis.

Borderline personality disorder (BPD) is a complex and severe psychiatric condition characterized by emotional, self-image, behavior, and relational instability. While adult BPD heterogeneity has been extensively studied, the phenomenological borderline personality features (BPFs) in adolescence remain uninvestigated. This study aimed to explore the potentially dynamic causal relationships between BPFs in adolescence and identify the subtypes through cross-lagged panel network (CLPN) analysis. Two independent Chinese adolescent samples were followed over 18 months (N₁ = 1,056, M1age = 15.37, SD1 = 1.86) and 6 months (N₂ = 723, M2age = 16.84, SD₂ = 0.48) to track BPFs. CLPN modeling was employed to investigate the stability, potential causal relations, and subtypes of adolescent BPFs. The results revealed a relatively stable overall adolescent BPF network structure with some subtle changes over time. Impulsivity emerged as the BPF with the highest out-expected influence, indicating its predictive role for other BPFs. A strong reciprocal causal relationship was observed between impulsivity and affective instability. Based on the CLPN estimation, two distinct BPFs subgroups were spontaneously clustered: externalized-dysregulation subtype (impulsivity, affective instability, and self-harm/suicide) and introjective-disturbance subtype (identity disturbance, chronic emptiness, and stress-related dissociation). The present study tentatively explores a potential typology for adolescent BPF based on these two clusters, which possibly have different pathological mechanisms, and moreover offer insights into the essential construct and clinical intervention of adolescent BPD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).