Children's Understanding of How Past Experience Shapes Future Expectations.

Focus to an attribute with verbal or numerical quantifiers affects the attribute framing effect

Propositional attitude generics such as “Experts think early humans ate grass” report an epistemic state (e.g., think, believe, say) that is generalised to a wider community (e.g., Experts, Scientists, Academics). These generics are often used in place of quantified claims (e.g., “Some experts think…”) but three pre-registered experiments (N = 4891) indicate that this lexical choice risks misrepresenting the true degree of scientific consensus. Relative to “Some experts think…” the generic “Experts think…” was more likely to be understood as “All Experts” or “Possibly all Experts” and less likely to invite the scalar inference “Not all Experts.” Consistent with this, the choice to use generic language became increasingly likely as expert consensus approached unanimity. Propositional attitude generics can imply a high degree of consensus and keep open the possibility of universal agreement. To avoid overgeneralisation, they should be used with caution where the objective degree of consensus is unknown.

Technology development has changed students' characteristics and learning needs. As facilitators of student learning, teachers should be able to use learning technology effectively in their classrooms. This study aimed to examine teacher preferences in using learning technology and explore key factors for using learning technology. This study used a survey design conducted on biology teachers in Yogyakarta, Indonesia. A total of 47 biology teachers participated in this study. The survey was conducted to gather information regarding demographic data, the frequency of technology usage, and teachers' preferences in utilizing technology. The preferences for technology use encompass two main aspects: teachers' experiences with technology and their future expectations. The results indicate that teachers are more interested in using learning technology on conceptual materials. Teachers suggest the development of application-based biology learning media on cell phones or laptops. The main factor for teachers to use technology is that they have sufficient basic knowledge of the learning technology they will use. Teaching experience and frequency of using technology do not affect the teacher’s factor in choosing technology. The government and universities can use the results of this study as a basis for further development of learning technology. Shifts in learning patterns and technological advances need to get attention from the government so that teacher training programs, especially those related to learning technology, can be carried out appropriately and effectively.

The study aims at analyzing and explaining the demographic characteristics and future expectations in bin Niz Palace by the use of the Geographic Information Systems by the aim of recognizing the development process of the population, their distribution, and their Growth rates during the period from 1973 to 2014 to study them in terms of the age structure, the specific aspect, family aspect in addition to studying their economic activities according to the professions and the educational status. The study depended on the Demographic data in the census for the years 1973, 1974, 1995, 2006, and in 2014. The study attained to the use of the demographic statistical methods or techniques and on the geographic information technology to get a group of results: The population has doubled in the Palace bin Ghacher continuously, but the increase was low if compared to the prior years of census by the rate of 1%, alike the Highness of the population in some places particularly in el awaneen and in Rodod el Zaweya and the raise of the general rate of the population to 36.01% from the majority of the population of bin Niz Palace that was explained in many figures by the use of the technology of the Geographic Information Systems هدفت الدراسة إلى تحليل وتفسير الخصائص السکانية والتوقعات المستقبلية بقصر بن غشير باستخدام نظم المعلومات الجغرافية وذلک بالتعرف على تطور عدد السکان وتوزرعهم ومعدلات نموهم فى الفترة من 1973-2014 ودراسة السکان من حيث الترکيب العمرى والنوعى والأسرى الى جانب دراسة الأنشطة الاقتصادية للسکان حسب المهن والحالة التعليمية وقد اعتمدت الدراسة على البيانات السکانية فى التعدادات السکانية لأعوام 1973 , 1984، 1995، 2006، 2014 وقد توصلت الدراسة الى استخدام الأساليب الاحصائية الديموجرافية المختلفة وتقنية نظم المعلومات الجغرافية الى مجموعة من النتائج أهمها : تضاعف عدد السکان فى قصر بن عنيز بشکل مستمر الا أن الزيادة تنخفض مقارنة بسنوات التعداد السابقة الى 1% وکذلک ارتفاع کثافة السکان فى بعض الحالات خصة فى محلتى العوانين وردود الزاوية وارتفاع نسبة السکان العاملين الى 36.1% من جملة سکان قصر بن عنيز التى يتم توضحيها بأشکال مختلفة باستخدام تقنية نظم المعلومات الجغرافية

"A Causal Model to Explain Sources of Errors in Demographic Data"

The number of research papers in the area of Brain Computer Interface (BCI) assistive technologies is increasing rapidly. In addition, there is a possibility that some prosthetic models based on BCI will soon be available on the market. However, the acceptance and the degree of information that lay people have about this kind of technology is still unclear. The objective of this study was to investigate the diffusion of this top-of-the-edge technology and its acceptance by society. We developed a structured questionnaire and we applied it, in a single day, to passersby in downtown of an urban city with over one million people in Southeastern Brazil. The results showed that almost a third of 336 interviewees (mean age of 37 year-old, range from 15 to 89 years) had never heard about such technology. Most (89 %) of the other two thirds that had already heard of it affirmed that BCI based technologies would help in cases of incapacitating physical disabilities. No association was found when confronting the pattern of the answers with demographical data (p>O.05). We conclude that there is a positive attitude towards assistive technology by the society. The majority of the interviewees claimed to know about it, but it seems that the knowledge is superficial and based on broad science diffusion media. The literature on BCI acceptance is still very limited. Approximation between the scientific community and the end users is advised to bring adequate information and to decrease the degree of fantasy naturally related to this area, avoiding future false expectations.

In this study, the interaction between cyberloafing and psychological capital was examined by considering demographic characteristics. Scanning, relational and causal comparison models are used within the scope of the quantitative paradigm. The data of a total of 196 participants were analyzed. A series of correlation, variance and regression analysis was performed. According to the results of the analysis, cyberloafing and psychological capital averages differ significantly depending on demographic data. Cyberloafing scores of males and those with a lower age group are significantly higher. Women, those with low working years and younger age, and psychology proffesionals have lower psychological capital. Psychological capital is negatively related to the outcome and punishment sub-dimension and beliefs about the outcome, while the behavior, attitude and facilitator dimension is positively related. Regression analysis indicated that cyberloafing subfactors (consequences and punishment, beliefs about outcomes, and facilitating) accounted for 16.3% of PsyCap variance, while work experience contributed an extra 4.3%. Consequence and punishment, and beliefs about outcome are associated with decreased PS, facilitator dimension, and working year associated with increased PS. The study suggests that lenient policies on non-work-related internet use enhance employees' PsyCap through cyberloafing, whereas strict cautionary measures decrease it.

Technostress is a new form of stress that affects several people, including healthcare workers. Technostress may increase because of the increasing responsibilities and demands that this digitization places on health care workers (HCWs).Technostress has been defined as the negative aspect of technology use. Both individuals and organizations suffer from technostress, which has been linked to negative health outcomes, reduced job performance, increased job discontent, and disruptions in work settings. Addressing technostress among healthcare professionals has received little attention, despite the increasing adoption of technological advances in healthcare facilities. The aim of this study was to investigate the technostress creators among healthcare workers in Family Medicine Centers (FMCs).Methods: A cross-sectional study was conducted in one of the family medicine centres in Saudi Arabia and included healthcare workers working there. The data were collected through an online questionnaire sent through email from February to March 2024. All the participants took a two-part questionnaire that asked about demographic data and ‎technostress creators (complexity, overload, invasion, uncertainty, multitasking, and work interruption). Informed consent was obtained from all healthcare staff who agreed to participate in the study. The study was approved by the Institutional Research Board (IRB) of the Royal Commission health service program in ‎Jubail.‎Results: In total, 101 participants were enrolled in this study, with a response rate of 79.2%, the result calculated the mean and standard deviation of participants, agreement for ‎technostress. Among all the technostress creators, the highest mean of participants, agreement recorded for techno-complexity (There are always new developments in the digital technologies we use in our organization), showed 4.34±3.23. However, work interruption had a low level in the total mean (2.01± 1.18); the total mean was (3.04±0.70) at level (neutral). Correlations with demographic factors were not discovered in this investigation, which indicates that technostress is a widespread problem that affects practitioners from every category. This generality emphasizes how vital it is to investigate the root origins of this occurrence.Conclusion: This study showed a significant level of technostress among HCWs, especially in techno-complexity, which concurs with other studies. Other creators are still favourable regarding technostress. To achieve a useful and long-lasting level of utilization, decision-makers should take into account the cognitive and social components of digitalization. Additional investigation is required to develop causal and practical models for workable action plans.

Population numbers at local levels are fundamental data for many applications, including the delivery and planning of services, election preparation, and response to disasters. In resource-poor settings, recent and reliable demographic data at subnational scales can often be lacking. National population and housing census data can be outdated, inaccurate, or missing key groups or areas, while registry data are generally lacking or incomplete. Moreover, at local scales accurate boundary data are often limited, and high rates of migration and urban growth make existing data quickly outdated. Here we review past and ongoing work aimed at producing spatially disaggregated local-scale population estimates, and discuss how new technologies are now enabling robust and cost-effective solutions. Recent advances in the availability of detailed satellite imagery, geopositioning tools for field surveys, statistical methods, and computational power are enabling the development and application of approaches that can estimate population distributions at fine spatial scales across entire countries in the absence of census data. We outline the potential of such approaches as well as their limitations, emphasizing the political and operational hurdles for acceptance and sustainable implementation of new approaches, and the continued importance of traditional sources of national statistical data.

In this article, the results of a pilot study investigating the relative importance of various learner variables on L2 performance are presented. The study was conducted with the participation of forty students enrolled in a beginning Spanish class at a large midwestern university. The CANAL S test, FLCAS and SILL surveys were administered to evaluate language learning aptitude, anxiety and learning strategy use. Demographic data and exam grades were also recorded. The data was first analyzed within the framework of a causal model with correlational and multiple regression analyses. A second step involved the evaluation of the relative importance of the learner variables. Third, the validity of the causality postulated between foreign language anxiety and L2 performance was examined. Overall, this paper confirmed the prevailing role played by language learning aptitude and anxiety, and demonstrated the applicability of a multivariate model to analyze the impact of individual differences on L2 performance.

Background: Adherence to scheduled radiation therapy (RT) is a key determinant of cancer treatment quality and outcomes. For this study, we developed an interpretable AI model to identify 1) patients at risk for multiple unplanned RT interruptions and 2) modifiable factors contributing to an elevated risk of RT interruption. Methods: We retrospectively analyzed clinical, socioeconomic, demographic, and behavioral data from 2,525 RT patients treated at the University of Tennessee Medical Center (UTMC) in Knoxville. The study cohort was dichotomized into patients with 0-1 unplanned RT interruptions (Class 0; n≈2000) and those missing >2 sessions (Class 1; n≈500). The dataset was partitioned into training, validation, and test sets (70:15:15 ratio), with class imbalance addressed in the training set by synthetic data generation via Tabular Variational Autoencoder. Twenty-seven candidate features were initially evaluated for multicollinearity using correlation matrices, heatmap visualization, and Variance Inflation Factor analysis. We applied feature selection methods (correlation-based techniques and causality-based approaches) to limit further modeling to the most predictive 15 core features. We compared XGBoost and Neural Networks-based classifiers, with each model undergoing hyperparameter optimization using Bayesian optimization methods. SHapley Additive exPlanations (SHAP) analysis was used to identify influential predictors. Results: The final optimized XGBoost model provided an overall accuracy of 82% and AUC-ROC of 63% on the independent test set. All tested models yielded similar performance, confirming the consistent predictive value of our selected features despite class imbalance. SHAP analyses identified dominant predictive contributions from treatment factors (prescribed radiation dose per session), patient resources (insurance coverage, marital status, social vulnerability indices), and travel distance to the radiotherapy facility. Supplementary causal analysis employing total causal effect methods further corroborated the direct influence of all these features. Conclusions: Our results suggest that causal inference and explainable AI modeling can provide useful interrogative strategies to identify modifiable predictors of RT adherence. Further refinement of predictive decision-support tools may lead to automated approaches to match high-risk patients with personalized interventions (e.g. community-based care navigation and/or patient psychosocial support) in real-world clinical settings to overcome social barriers to RT access. Citation Format: Rezaur Rashid, Soheil Hashtarkhani, Parnian K. Rahimabad, Brianna M. White, Fekede A. Kumsa, Lokesh Chinthala, Janet A. Zink, Christopher L. Brett, Robert L. Davis, David L. Schwartz, Arash Shaban-Nejad. Machine Learning and Causal Inference-Based Predictive Risk Modeling of Unplanned Radiation Treatment Interruption [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A061.

ObjectivesA feasibility study has shown that a scientifically rigorous and comprehensive epidemiological study of workers involved in the manufacture or production of tungsten carbide with a cobalt binder is feasible,...

Safety observations during the clinical development of Mentane (velnacrine maleate) have included the occurrence of generally asymptomatic liver enzyme elevations confined to patients with Alzheimer's disease (AD). The clinical presentation of this reversible hepatocellular injury is analogous to that reported for tetrahydroaminoacridine (THA). Direct liver injury, possibly associated with the production of a toxic metabolite, would be consistent with reports of aberrant xenobiotic metabolism in Alzheimer's disease patients. Since a patient related aberration in drug metabolism was suspected, a biostatistical strategy was developed with the objective of predicting hepatotoxicity in individual patients prior to exposure to velnacrine maleate. The method used logistic regression techniques with variable selection restricted to those items which could be routinely and inexpensively accessed at screen evaluation for potential candidates for treatment. The model was to be predictive (a marker for eventual hepatotoxicity) rather than a causative model, and techniques employed "goodness of fit", percentage correct, and positive and negative predictive values. On the basis of demographic and baseline laboratory data from 942 patients, the PROPP statistic was developed (the Physician Reference Of Predicted Probabilities). Main effect variables included age, gender, and nine hematological and serum chemistry variables. The sensitivity of the current model is approximately 49%, specificity approximately 88%. Using prior probability estimates, however, in which the patient's likelihood of liver toxicity is presumed to be at least 30%, the positive predictive value ranged between 64-77%. Although the clinical utility of this statistic will require refinements and additional prospective confirmation, its potential existence speaks to the possibility of markers for idiosyncratic drug metabolism in patients with Alzheimer's disease.

We are interested in all sorts of tool-support, which help the designer of a pervasive application in different stages of the development process, such as task and requirements analysis, conceptual design, prototyping and evaluation. We are looking for contributions that will help to address the following questions: What are the past experiences and future expectations of designers and developers that use tools for support?; What exactly are the benefits and the shortcomings of available tools?; What are the open issues and challenges for the next few years?The workshop will feature presentations of research results, experiences of past and ongoing work, and a forum for participants to address a predefined set of focus questions.

Psychosocial rehabilitation for people with chronic-severe mental illness mainly aims to social integration by restoring independent functioning in the community, improving quality of life, and addressing risk factors that lead to social disability. Support groups (SG) are usually part of this multilevel mental health process. Given that non-adherence to treatment is a common phenomenon in people with chronic- severe mental illness, the aim of the current study was to identify which factors influence members' attendance in a support group in a vocational training Program of the Psychosocial Rehabilitation Unit of Byron-Kaissariani Community Mental Health Centre. The SG sessions were weekly, with 45-minute duration, opened to any new member of the Program and coordinated by two therapists. Members' demographic and psychiatric data were gathered from the medical records of the Center. Information about SG was obtained from the reports of the sessions. The sample consisted of 18 women, with mean age 38.56 (±6.92) years. Most of them were high school graduates (61.1%), unmarried (83.3%), with low socioeconomic status (55.5%), suffering from a schizophrenic spectrum disorder (61.1%) with a mean duration 15.22 (±8.44) years. Out of 83 sessions in total, twenty-two (26.5%) were in absence of a co-therapist, 11 (13.3%) after a member's entrance or withdrawal and 11 (13.3%) after a session cancellation. Furthermore, an average of four issues was discussed per session, with mental illness (62.7%) and interpersonal relationships (73.5%) being the most popular topics during the sessions. The statistical analysis demonstrated that members' demographic (age, education, marital status, residence, socioeconomic status, working experience) and psychiatric characteristics (diagnosis, illness duration, rehabilitation program experience) were not associated with the attendance rate in the SG. Similarly, the proportion of participants attending the sessions did not seem to be significantly related to the absence of a co-therapist, to a member's entrance or withdrawal and to a session cancellation. In contrast, attendance seemed to be significantly reduced when the topic of a session focused on members' future expectations/goals (having a family, further education, finding a job) (Beta=-0.32, p=0.006). This finding highlights the need for future research in order to incorporate interventions that promote and address future goals and expectations of people with chronic-severe mental illness in psychosocial rehabilitation services.