Virendra V. Singh, Bikram K. Dutta, Surender Sharma
et al.
Background:
WHO has revised International Classification of Diseases (ICD) to 11th edition. There are some important changes in description of the diseases. Transition is expected to throw some challenges. We focused on the expected challenges and implications with new description of Alcohol Dependence Syndrome and Harmful Use of Alcohol.
Aim:
To study concordance between diagnoses of alcohol dependence, harmful use of alcohol as per ICD-10 and ICD-11.
Materials and Methods:
233 personnel who visited psychiatry department of a service hospital first time for the evaluation and management of alcohol use disorders participated in the study. They were evaluated independently by two psychiatrists and diagnosed as per descriptions in ICD-10 and ICD-11 bluebook. The concordance was assessed using Cohen’s kappa.
Results:
190 participants were diagnosed with alcohol dependence syndrome as per both ICD-10 and ICD-11. Thirty-six participants were diagnosed with harmful use of alcohol as per ICD-11 and 26 as per ICD-10. The concordance between ICD-11 and ICD-10 alcohol dependence was almost perfect with kappa value 0.97 and between the harmful use strong with kappa value of 0.82. Ten participants (27.8%) were diagnosed with a history of harm to others, and one patient was diagnosed harmful use on single use.
Conclusion:
There is not likely any major change in person getting diagnosed with alcohol dependence, but new system would help persons getting benefit of early intervention for harmful use of alcohol.
As AI tutors enter classrooms at unprecedented speed, their deployment increasingly outpaces our grasp of the psychological and social consequences of such technology. Yet decades of research in automation psychology, human factors, and human-computer interaction provide crucial insights that remain underutilized in educational AI design. This work synthesizes four research traditions -- automation psychology, human factors engineering, HCI, and philosophy of technology -- to establish a comprehensive framework for understanding how learners psychologically relate to anthropomorphic AI tutors. We identify three persistent challenges intensified by Generative AI's conversational fluency. First, learners exhibit dual trust calibration failures -- automation bias (uncritical acceptance) and algorithm aversion (excessive rejection after errors) -- with an expertise paradox where novices overrely while experts underrely. Second, while anthropomorphic design enhances engagement, it can distract from learning and foster harmful emotional attachment. Third, automation ironies persist: systems meant to aid cognition introduce designer errors, degrade skills through disuse, and create monitoring burdens humans perform poorly. We ground this theoretical synthesis through comparative analysis of over 104,984 YouTube comments across AI-generated philosophical debates and human-created engineering tutorials, revealing domain-dependent trust patterns and strong anthropomorphic projection despite minimal cues. For engineering education, our synthesis mandates differentiated approaches: AI tutoring for technical foundations where automation bias is manageable through proper scaffolding, but human facilitation for design, ethics, and professional judgment where tacit knowledge transmission proves irreplaceable.
Karolin ME Nettelrodt,1 Ivonne Tomsic,1 Maike Stolz,2 Christian Krauth,2 Iris F Chaberny,3,4 Thomas von Lengerke1 1Department of Medical Psychology, Center of Public Health, Hannover Medical School, Hannover, Germany; 2Institute of Epidemiology, Social Medicine and Health Systems Research, Center of Public Health, Hannover Medical School, Hannover, Germany; 3Institute of Hygiene, Hospital Epidemiology and Environmental Medicine, Leipzig University Hospital, Leipzig, Germany; 4Christian-Albrecht University of Kiel and University Medical Center Schleswig-Holstein, Institute of Hospital Epidemiology and Environmental Hygiene, Kiel, GermanyCorrespondence: Thomas von Lengerke, Hannover Medical School, Center of Public Health, Department of Medical Psychology (OE 5430), Carl-Neuberg-Str. 1, Hannover, 30625, Germany, Tel +49 511 532 4445, Fax +49 511 532 4214, Email lengerke.thomas@mh-hannover.dePurpose: Psychosocial determinants influence healthcare workers’ compliance with surgical site infection (SSI) preventive interventions. In order to design needs-based interventions promoting compliance, such determinants must first be assessed using valid and reliable questionnaire scales. To compare professional groups without bias, the scales must also be measurement-equivalent. We examine the validity/reliability and measurement equivalence of four scales using data from physicians and nurses from outside the university sector. Additionally, we explore associations with self-reported SSI preventive compliance.Participants and Methods: N = 90 physicians and N = 193 nurses (response rate: 31.5%) from nine general/visceral or orthopedic/trauma surgery departments in six non-university hospitals in Germany participated. A written questionnaire was used to assess the compliance with SSI preventive interventions and the determinants of compliance based on the Capability-Opportunity-Motivation-Behavior-Model. Psychometric testing involved single- and multiple-group confirmatory factor analyses, and explorative analyses used t-tests and multiple linear regression.Results: The scales assessing individual determinants of compliance (capability, motivation, and planning) were found to be reliable (each Cronbach’s α ≥ 0.85) and valid (each Root-Mean-Square-Error of Approximation ≤ 0.065, each Comparative-Fit-Index = 0.95) and revealed measurement equivalence for physicians and nurses. The scale assessing external determinants (opportunity) did not demonstrate validity, reliability, or measurement equivalence. Group differences were found neither in compliance (p = 0.627) nor determinants (p = 0.192; p = 0.866; p = 0.964). Capability (β = 0.301) and planning (β = 0.201) showed associations with compliance for nurses only.Conclusion: The scales assessing motivation, capability, and planning regarding SSI preventive compliance provided reliable and valid scores for physicians and nurses in surgery. Measurement equivalence allows group comparisons of scale means to be interpreted without bias.Keywords: infection prevention and control, surgical site infections, compliance, nurses, physicians, capability-opportunity-motivation-behavior (COM-B) model, measurement equivalence
Sarah J. Egan, Catherine Johnson, Tracey D. Wade
et al.
Perfectionism is a transdiagnostic process associated with a range of psychological disorders. Cognitive Behaviour Therapy for Perfectionism (CBT-P) has been demonstrated as efficacious across guided and unguided internet delivered interventions in reducing perfectionism and psychopathology. The aim of this pilot study was to understand perceptions and acceptability of an artificial intelligence supplemented CBT-P intervention (AI-CBT-P) in young people with lived experience of anxiety and depression (n = 8; age range 19–29 years, M = 24 years, SD = 3.77; 50 % female, 38 % male, 12 % non-binary). Young people reported that they were frequent users of artificial intelligence for study, work and general information, were positive about the intervention and using artificial intelligence for guidance in a self-help intervention, but also noted several concerns. Young people perceived numerous benefits to AI-CBT-P, including ease of access, low cost, lack of stigma and benefits for individuals with social anxiety. Overall, young people appear to be interested in, and have a positive view of, AI-CBT-P. Further research is now required to examine the feasibility and acceptability of the intervention.
Nils Hentati Isacsson, Kirsten Zantvoort, Erik Forsell
et al.
Objective: Predicting who will not benefit enough from Internet-Based Cognitive Behavioral (ICBT) Therapy early on can assist in better allocation of limited mental health care resources. Repeated measures of symptoms during treatment is the strongest predictor of outcome, and we want to investigate if methods that explicitly account for time-dependency are superior to methods that do not, with data from (a) only two pre-treatment timepoints and (b) the pre-treatment timepoints and three timepoints during initial treatment. Methods: We use 1) commonly used time-independent methods (i.e., Linear Regression and Random Forest models) and 2) time-dependent methods (i.e., multilevel model regression, mixed-effects random forest, and a Long Short-Term Memory model) to predict symptoms during treatment, including the final outcome. This is done with symptom scores from 6436 ICBT patients from regular care, using robust multiple imputation and nested cross-validation methods. Results: The models had a 14 %–12 % root mean squared error (RMSE) in predicting the post-treatment outcome, corresponding to a balanced accuracy of 67–74 %. Time-dependent models did not have higher accuracies. Using data for the initial treatment period (b) instead of only from before treatment (a) increased prediction results by 1.3 % percentage points (12 % to 10.7 %) RMSE and 6 % percentage points BACC (69 % to 75 %). Conclusion: Training prediction models on only symptom scores of the first few weeks is a promising avenue for symptom predictions in treatment, regardless of which model is used. Further research is necessary to better understand the interaction between model complexity, dataset length and width, and the prediction tasks at hand.
Jean Marie Tshimula, D'Jeff K. Nkashama, Jean Tshibangu Muabila
et al.
The increasing sophistication of cyber threats necessitates innovative approaches to cybersecurity. In this paper, we explore the potential of psychological profiling techniques, particularly focusing on the utilization of Large Language Models (LLMs) and psycholinguistic features. We investigate the intersection of psychology and cybersecurity, discussing how LLMs can be employed to analyze textual data for identifying psychological traits of threat actors. We explore the incorporation of psycholinguistic features, such as linguistic patterns and emotional cues, into cybersecurity frameworks. Our research underscores the importance of integrating psychological perspectives into cybersecurity practices to bolster defense mechanisms against evolving threats.
Internet is becoming a part of our lifestyle; however, the usage rate and application of the Internet are disparate in different parts of the world. In many emerging countries, the Internet is yet to penetrate ordinary households. The present study focuses on how IoT adoption impacts the banks’ customer relationship management (CRM) in an emerging market context. Furthermore, the moderating roles of gender, age, and bank ownership type on the relationship between the adoption of IoT and CRM have also been tested. Cost, convenience, social context, and privacy were studied as the predicting variables of IoT adoption, while IoT adoption was investigated as the antecedent of CRM. The CRM variable has been operationalized as a second-order latent construct consisting of three first-order latent variables: responsiveness, satisfaction, and assurance. A cross-sectional, non-probability-based survey was conducted from 467 bank customers of three public and three private sector banks in Aligarh city of India. Two CFA models were run to ensure reliability, validity, and model fit. Hypotheses were tested using structural equation modeling (SEM) on AMOS software, while PROCESS Macro v4.0 by Hayes (2009) was used to test the moderating effect of gender on the relationship between IoT adoption and CRM. The results indicate that cost, convenience, social context, and privacy are positively influencing IoT adoption, which in turn positively affects CRM. Gender and age were found to have a negative moderation effect on the path between IoT adoption and CRM, while bank ownership type positively moderated this link.
Melkhianus Hendrik Pentury, Joan Herly Herwawan, Fandro Armando Tasijawa Faculty of Health, Universitas Kristen Indonesia Maluku, Maluku Province, IndonesiaCorrespondence: Fandro Armando Tasijawa, Faculty of Health, Universitas Kristen Indonesia Maluku, Maluku Province, Indonesia, Email fandrotasidjawa@gmail.com
Helena Moreira, Ana Carolina Góis, Ana Maria Pereira
et al.
Objectives: This study aims to (1) describe parents' knowledge and use of online resources to address children's mental health issues and the family's general internet and technology usage patterns; (2) examine parents' acceptance of blended interventions for children with emotional disorders (ED); and (3) analyse the predictors of parents' intention to use a blended intervention if their children experienced an ED. Method: The sample included 164 Portuguese parents (95.7 % mothers) of children between the ages of 6 and 13 years who completed an online survey. The study was disseminated through social networks, personal contacts of the researchers, and among parents participating in a randomized controlled trial investigating the efficacy of a psychological intervention for children with ED. Results: Only 4.3 % of parents knew about online psychological interventions for children, and only 1.2 % had used them before. Most parents (73.2 %) reported that they would choose face-to-face individual therapy as their first option if their child had any ED, followed by blended therapy (14.8 %). Regression analyses showed that higher levels of parents' intention to use a blended intervention were predicted by their perceptions of the utility or efficacy of this type of delivery format. Discussion/conclusion: These results suggest that although most parents show unfamiliarity with blended psychological interventions for children, they consider it a treatment modality to which they would resort if their children had emotional difficulties. Their intention to use such an intervention seems to be more likely if they perceive it as useful and effective.
Qin Xu,1,2,* Dan Li,1,2,* Yongning Dong,2,* Yi Wu,2 Hong Cao,1,2 Feng Zhang,1,2 Yanping Xia,1 Jing Chen,1,2 Xuesong Wang1,2 1Affiliated Hospital of Jiangnan University, Wuxi, People’s Republic of China; 2Wuxi School of Medicine, Jiangnan University, Wuxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jing Chen; Xuesong Wang, No. 1000 Hefeng Road, Binhu District, Wuxi, People’s Republic of China, Email cj02303@163.com; victorwxs@jiangnan.edu.cnPurpose: The aim of this study was to investigate whether the personality traits of intern nursing students could serve as valid predictors of their psychological status and clinical decision making. Additionally, we aimed to understand the psychological state of intern nursing students during the regular epidemic prevention and control stage of COVID-19.Participants and Methods: This study was designed as a cross-sectional survey. A total of 181 intern nursing students involved in clinical placements participated in this study. Participants provided relevant data by completing the Big Five Inventory-44, the Self-Rating Anxiety Scale, the Perceived Stress Scale 14, and the Clinical Decision-Making in Nursing Scale.Results: The results showed that neuroticism (β = -0.282, p
This paper investigates the interplay between cross-culture, organisational culture, path-goal leadership, and team effectiveness in Nigerian oil and gas projects. Employing a quantitative research approach with a philosophical assumption between positivism and relativism, the study examines path-goal leadership and organisational culture as mediating variables. A survey instrument was administered to 230 participants using judgmental recruitment, with a response of 91.3%. A partial least square structural equation modelling approach was implemented for data analysis. The findings reveal that high achievement and directive leadership styles in the Nigerian oil and gas industry lead workers to adopt a long-term orientation cross-culture to effectively adapt to the project working environments. Additionally, the dimensions of organizational culture exert a dominant influence on defining project environments in the industry. To enhance ownership and shared leadership, the study recommended the need to strike a balance between achievement-oriented and shared leadership throughout the project duration. Moreover, proactive occupational health measures can help manage the possible health effect of adaptive work behaviour. Furthermore, industry-wide project audits based on the study's variables can enhance leadership policies and promote a people-oriented leadership approach. The research presented in this study offers both theoretical and practical implications in the Nigerian oil and gas industry.
History of scholarship and learning. The humanities, Social sciences (General)
Background and purposeThe coronavirus disease 2019 (COVID-19) has been associated with olfactory dysfunction. The persistent symptoms of anosmia or hyposmia were associated in previous studies with the development of memory impairment and mood disturbances. We aimed to investigate the association between the chronicity of reported olfactory dysfunction and subjective and objective cognitive performance in long-COVID patients and to explore whether their emotional symptoms are related to their cognition.MethodsOne hundred twenty-eight long-COVID participants were recruited. Reported symptomatology, subjective memory complaints, anxiety and depression symptomatology, and trait-anxiety were assessed. Subjective memory complaints and mood disturbances were compared among groups of participants with olfactory dysfunction as an acute (AOD), persistent (POD), or nonexistent (NOD) symptom. Seventy-six of the volunteers also participated in a face-to-face session to assess their objective performance on tests of general cognitive function and verbal declarative memory. Objective cognitive performance and mood disturbances were compared among the AOD, POD, and NOD groups.ResultsThe subjective memory complaints and the anxiety and depression symptoms were similar among the groups, but the score in general cognitive function was lower in the participants with symptoms of acute olfactory dysfunction than in those with no olfactory symptoms at any time. Participants’ memory complaints were positively related to their emotional symptoms. The relationship between depressive symptomatology and memory complaints interacted with the olfactory dysfunction, as it only occurred in the participants without symptoms of olfactory dysfunction. Depressive symptomatology and acute olfactory symptoms were negatively associated with general cognitive function and delayed memory performance. The months elapsed from diagnosis to assessment also predicted delayed memory performance. Anxious symptomatology was negatively associated with the immediate ability to recall verbal information in participants who did not present olfactory dysfunction in the acute phase of the infection.ConclusionOlfactory dysfunction in the acute phase of the infection by COVID-19 is related to cognitive deficits in objective tests, and mood disturbances are associated with self-reported and objective memory. These findings may contribute to further understanding the neuropsychological and emotional aspects of long-COVID.
Delusional parasitosis (DP) is an infrequent psychotic illness, where the patient has a false but firm belief that his body is infested with parasites. It can be primary or secondary. Usually, these patients consult nonpsychiatric specialties from where they are referred to psychiatry. The presentation of DP varies among patients, although it typically manifests as a crawling and pinpricking sensation. Hallucinations are commonly seen. Antipsychotics show good remission of symptoms. A series of seven cases of DP have been described, and the condition is briefly discussed.
The complexity of psychological principles underscore a significant societal challenge, given the vast social implications of psychological problems. Bridging the gap between understanding these principles and their actual clinical and real-world applications demands rigorous exploration and adept implementation. In recent times, the swift advancement of highly adaptive and reusable artificial intelligence (AI) models has emerged as a promising way to unlock unprecedented capabilities in the realm of psychology. This paper emphasizes the importance of performance validation for these large-scale AI models, emphasizing the need to offer a comprehensive assessment of their verification from diverse perspectives. Moreover, we review the cutting-edge advancements and practical implementations of these expansive models in psychology, highlighting pivotal work spanning areas such as social media analytics, clinical nursing insights, vigilant community monitoring, and the nuanced exploration of psychological theories. Based on our review, we project an acceleration in the progress of psychological fields, driven by these large-scale AI models. These future generalist AI models harbor the potential to substantially curtail labor costs and alleviate social stress. However, this forward momentum will not be without its set of challenges, especially when considering the paradigm changes and upgrades required for medical instrumentation and related applications.
Roberto Figliè, Riccardo Amadio, Marios Tyrovolas
et al.
Today, one of the biggest challenges for digital transformation in the Industry 4.0 paradigm is the lack of mutual understanding between the academic and the industrial world. On the one hand, the industry fails to apply new technologies and innovations from scientific research. At the same time, academics struggle to find and focus on real-world applications for their developing technological solutions. Moreover, the increasing complexity of industrial challenges and technologies is widening this hiatus. To reduce this knowledge and communication gap, this article proposes a mixed approach of humanistic and engineering techniques applied to the technological and enterprise fields. The study's results are represented by a taxonomy in which industrial challenges and I4.0-focused technologies are categorized and connected through academic and grey literature analysis. This taxonomy also formed the basis for creating a public web platform where industrial practitioners can identify candidate solutions for an industrial challenge. At the same time, from the educational perspective, the learning procedure can be supported since, through this tool, academics can identify real-world scenarios to integrate digital technologies' teaching process.
Residual networks have shown great success and become indispensable in today's deep models. In this work, we aim to re-investigate the training process of residual networks from a novel social psychology perspective of loafing, and further propose a new training strategy to strengthen the performance of residual networks. As residual networks can be viewed as ensembles of relatively shallow networks (i.e., \textit{unraveled view}) in prior works, we also start from such view and consider that the final performance of a residual network is co-determined by a group of sub-networks. Inspired by the social loafing problem of social psychology, we find that residual networks invariably suffer from similar problem, where sub-networks in a residual network are prone to exert less effort when working as part of the group compared to working alone. We define this previously overlooked problem as \textit{network loafing}. As social loafing will ultimately cause the low individual productivity and the reduced overall performance, network loafing will also hinder the performance of a given residual network and its sub-networks. Referring to the solutions of social psychology, we propose \textit{stimulative training}, which randomly samples a residual sub-network and calculates the KL-divergence loss between the sampled sub-network and the given residual network, to act as extra supervision for sub-networks and make the overall goal consistent. Comprehensive empirical results and theoretical analyses verify that stimulative training can well handle the loafing problem, and improve the performance of a residual network by improving the performance of its sub-networks. The code is available at https://github.com/Sunshine-Ye/NIPS22-ST .
The popularity of Autonomous Sensory Meridian Response (ASMR) has skyrockted over the past decade, but scientific studies on what exactly triggered ASMR effect remain few and immature, one most commonly acknowledged trigger is that ASMR clips typically provide rich semantic information. With our attention caught by the common acoustic patterns in ASMR audios, we investigate the correlation between the cyclic features of audio signals and their effectiveness in triggering ASMR effects. A cyber-psychological approach that combines signal processing, artificial intelligence, and experimental psychology is taken, with which we are able to quantize ASMR-related acoustic features, and therewith synthesize ASMR clips with random cyclic patterns but not delivering identifiably scenarios to the audience, which were proven to be effective in triggering ASMR effects.