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Multitasking has been demonstrated to negatively impact performance across a wide range of tasks, including in the classroom, yet students continue to multitask. This study examined the relationship between college students’ perceptions and performance of technology-based multitasking. Technology-based multitasking and self-efficacy data were collected and analyzed from 265 undergraduate students. Students engaged in a technology-based multitasking perceptions survey, a video + survey multitasking task or a video-only non-multitasking task, and a technology-based self-efficacy survey. An analysis of student perceptions indicated that students understood that different tasks required different levels of mental effort to complete successfully and that multitasking across high-mental effort tasks required greater effort than multitasking across low-mental effort tasks. In addition, students in the video + survey multitasking group significantly underperformed students in the video-only non-multitasking group. Finally, the relationship between technology-based multitasking and self-efficacy was addressed in a correlational analysis between student technology-based multitasking scores and technology-based self-efficacy scores, yielding no significant relationship. The study findings indicate that most students have an understanding and awareness of multitasking, but ultimately, whether they believed they could multitask or not, multitasking significantly impeded performance.

Prior to the COVID-19 pandemic, remote and hybrid work options were scarcely available. Where applicable, workers experienced remote and flexible hybrid work arrangements, during the pandemic. Post-pandemic, many companies are pivoting and rescinding remote and hybrid options and requiring in-office attendance. The impetus of this research was to ascertain future workers’ preferences for remote, hybrid, and live work situations. The challenge is that current students have become accustomed to flexible work and school standards. This mixed method research was conducted with 289 undergraduate business students studying online and on-campus in an urban University in at an urban University, Kean University in Union, New Jersey, in May and September of 2023. Open-ended and Likert scale survey questions were given anonymously to undergraduate business students, with a 72.3% response rate. The following quantitative hypotheses were researched and should be significant: H1: Gender (Women vs. Men) - female students may perceive workplace flexibility more importantly than male students. H2: Racial/Ethnic Minority - racial/ethnic minority students may value workplace flexibility more than white students. H3: Intersectionality - racial/ethnic minority female students may perceive workplace flexibility as more important than other students. In terms of the qualitative findings, the results uncovered that female and racial/ethnic minority students regard flexible work arrangements, such as hybrid work, more importantly than others. Females and racially diverse employees desire greater flexibility to maintain work-life balance. Lastly, female students perceive work-life balance very differently from male students. The benefits to workers include decreased commuting costs, increased commitment, and decreased stress, while employers benefit by keeping attrition levels low, increasing employee engagement, and decreasing office space requirements and associated overhead costs while maintaining productivity. The challenge for employers is to continue to offer flexible work options instead of eliminating them, particularly if a corporation desires to increase the number of females and racially diverse employees at their companies.

Female minorities are represented scarcely in leadership positions within STEM. Previous research focused on increasing STEM education of minorities, not exploring the underlying issues affecting these groups in the workforce. It has driven the relevance of this study, which focuses on examining the intersectional experiences of cisgender, female minority leaders on their career paths, and examines the coping mechanisms which created resilience allowing these women to rise to high-level positions in STEM. The theoretical construct used is the 360-Degree Gender Sphere, which describes the barriers that prevent women from moving up the corporate ladder. The results of the study indicate that minority women who are leaders in STEM fields did indeed face a 360-Degree Gender Sphere, in particular challenges and obstacles from direct leaders, family, gender structures, themselves, peers and social norms. Further, they were encapsulated by a “360-degree intersectionality sphere” a peripheral and tangentially related outer layer of challenges and obstacles, including cultural stereotypes, cultural micro-attacks, cultural macro-aggressions, microaggressions from the H/R environment, cultural isolation, cultural doubts, cultural naivety, cultural micro-attacks, cultural stereotypical threats and geographical cultural macroaggression. The results also showed that women overcame these difficulties by creating resilience to these challenges that influenced their practice, education and leadership. The impact of this research can be useful in the process of initiating and shaping impulses for changes in society, women's communities, business, geopolitical formations and political systems in the world. The practical significance of this study is that it deepens the knowledge and equips minority females with information and skills for self-realization and successful career building in STEM fields.

Background/Objectives: Autistic adults are more likely to be unemployed compared to neurotypical adults and those with disability. To address these poorer employment outcomes, it is important to consider factors that may be impacting on autistic adults’ employment outcomes. Anxiety is a common co-occurring condition for autistic adults; however, there is little research on how anxiety affects or influences autistic people’s experience across the employment-seeking process. The aim of this study was to explore whether anxiety is perceived to affect autistic adults’ ability to engage in employment-seeking tasks. Methods: Online, semi-structured interviews were conducted with 12 autistic adults (22–52 years) who were actively seeking employment or had sought employment in the last 18 months. Interview transcripts were analysed using thematic analysis. Results: Three themes were generated from the data analysis. Theme 1, Finding the “sweet spot”, identified some level of anxiety—but not too much—was helpful in performing the employment-seeking tasks and this “spot” could vary depending on the person and the task. Theme 2, Anxiety affects the ability to perform and function, encapsulates the autistic person’s experience when anxiety is too high. The third theme, The “vicious cycle” of anxiety and employment-seeking behaviours, explores participants’ ongoing experience of anxiety on their employment-seeking behaviours. Conclusions: The findings suggest that autistic job candidates would benefit from tailored accommodations and adjustments offered throughout the recruitment process, to reduce anxiety and improve employment-seeking outcomes for autistic candidates.

Abstract Preterm birth is a global epidemic affecting millions of mothers across different ethnicities. The cause of the condition remains unknown but has recognised health‐based implications, in addition to financial and economic ones. Machine Learning methods have enabled researchers to combine datasets using uterine contraction signals with various forms of prediction machines to improve awareness of the likelihood of premature births. This work investigates the feasibility of enhancing these prediction methods using physiological signals including uterine contractions, and foetal and maternal heart rate signals, for a population of south American women in active labour. As part of this work, the use of the Linear Series Decomposition Learner (LSDL) was seen to lead to an improvement in the prediction accuracies of all models, which included supervised and unsupervised learning models. The results from the supervised learning models showed high prediction metrics upon the physiological signals being pre‐processed by the LSDL for all variations of the physiological signals. The unsupervised learning models showed good metrics for the partitioning of Preterm/Term labour patients from their uterine contraction signals but produced a comparatively lower set of results for the various kinds of heart rate signals investigated.

Abstract Background Phenotypic studies have identified distinct patterns of autistic characteristics in genetic syndromes associated with intellectual disability (ID), leading to diagnostic uncertainty and compromised access to autism-related support. Previous research has tended to include small samples and diverse measures, which limits the generalisability of findings. In this study, we generated detailed profiles of autistic characteristics in a large sample of > 1500 individuals with rare genetic syndromes. Methods Profiles of autistic characteristics based on the Social Communication Questionnaire (SCQ) scores were generated for thirteen genetic syndrome groups (Angelman n = 154, Cri du Chat n = 75, Cornelia de Lange n = 199, fragile X n = 297, Prader–Willi n = 278, Lowe n = 89, Smith–Magenis n = 54, Down n = 135, Sotos n = 40, Rubinstein–Taybi n = 102, 1p36 deletion n = 41, tuberous sclerosis complex n = 83 and Phelan–McDermid n = 35 syndromes). It was hypothesised that each syndrome group would evidence a degree of specificity in autistic characteristics. To test this hypothesis, a classification algorithm via support vector machine (SVM) learning was applied to scores from over 1500 individuals diagnosed with one of the thirteen genetic syndromes and autistic individuals who did not have a known genetic syndrome (ASD; n = 254). Self-help skills were included as an additional predictor. Results Genetic syndromes were associated with different but overlapping autism-related profiles, indicated by the substantial accuracy of the entire, multiclass SVM model (55% correctly classified individuals). Syndrome groups such as Angelman, fragile X, Prader–Willi, Rubinstein–Taybi and Cornelia de Lange showed greater phenotypic specificity than groups such as Cri du Chat, Lowe, Smith–Magenis, tuberous sclerosis complex, Sotos and Phelan-McDermid. The inclusion of the ASD reference group and self-help skills did not change the model accuracy. Limitations The key limitations of our study include a cross-sectional design, reliance on a screening tool which focuses primarily on social communication skills and imbalanced sample size across syndrome groups. Conclusions These findings replicate and extend previous work, demonstrating syndrome-specific profiles of autistic characteristics in people with genetic syndromes compared to autistic individuals without a genetic syndrome. This work calls for greater precision of assessment of autistic characteristics in individuals with genetic syndromes associated with ID.

Identifying ways to improve transition support for autistic students exiting secondary school is an important research priority. However, few studies have consulted with autistic/autism community members to identify how to make this support effective. This co-produced study asked autistic adults to reflect on their own transition, with the aim of identifying what was helpful and effective in supporting their transitioning from secondary school. It also aimed to identify whether there were any differences for students from non-metropolitan areas. Ten autistic adults participated in semi-structured interviews conducted online or via telephone. Interview data were analysed using thematic analysis, which generated two themes: (1) accessing opportunities that supported decision making and (2) being able to develop the “skills of adulthood”. The findings suggest the importance of supporting all autistic students to explore multiple pathways to make well-informed post-school decisions. A holistic approach is needed to support autistic students in developing the logistical skills and independence needed to succeed in post-school life. This approach should include guidance from autistic role models. For autistic young people attending non-metropolitan schools, the impact of local cultures and additional logistical barriers indicate that they may need further tailored support to aid their transition to adulthood.

The effective prediction of preterm labor continues to be a topic of interest for research within pregnancy medicine, where uterine muscle contraction signals have shown to be insightful to predict a potential preterm birth. Magnetomyography (MMG) is a physiological-measurement-based tool which measures the orthogonal offset of bioelectrical manifestations from uterine contractions and may serve to predict potential premature deliveries with an enhanced accuracy. The decoding of the physiological signal is an area of substantial research where classical signal processing approaches and metaheuristics optimization routines have been utilized in the postprocessing and decomposition of MMG signals. This work requires a degree of expert knowledge and an understanding of tuning and parameter initialization. As a stride towards creating a more automated clinical decision support platform for predictions of preterm labor, we employ the use of a deep wavelet scattering (DWS) model. This methodology allows for a deep multiresolution analysis alongside unsupervised feature learning for the postprocessing of candidate MMG signals. DWS is combined with select pattern-recognition-based prediction machines in order to assemble a clinical decision pipeline for the prediction of the states of various pregnancies, with a greater degree of machine intelligence. The patient cohort consisted of a multi-ethnic demographic population composed of preterm and term pregnancies, where births occurred both under and over 48 h after labor commenced. Contrasting results were found between the various methods from the literature and DWS using the logistic regression algorithm. It was seen that DWS produced a slightly lower accuracy in comparison, as a trade-off for its streamlined unsupervised feature extraction process. Further work will now involve the application of various other machine learning methods in an attempt to assess and identify the most appropriate machine learning method with DWS that proves to be the most accurate.

Cancers are prevalent worldwide, affecting a substantial amount of the global population, while early and proactive diagnosis of the disease continues to be a global medical challenge. Endometrial cancer represents a gynecological variant which is not only difficult to diagnose but also produces symptoms that are not distinct or exclusive to just the cancer itself. Blood spectroscopy has recently prevailed as a means towards a high-throughput and largely inexpensive method of diagnosing endometrial cancer. Using this method, and with the postprocessing of the accompanying spectra alongside the use of multivariate statistics, an inference can be formed which gives an indication of the presence and extent of the cancer. Previous work in this area has shown that the prediction results for this cancer could be improved with the use of signal decomposition models alongside machine learning prediction models, thus demonstrating the potential appeal of decomposition models in the processing pipeline of the spectroscopy data. As part of this exploratory study, we employ for the first time the use of deep learning, in the form of deep wavelet scattering, for the processing of acquired Fourier transform infrared (FTIR) spectra, which allows for a fully unsupervised decomposition and feature extraction of the resulting spectra, coupled with prediction machines capable of predicting the presence of cancer. The obtained results show that the use of deep learning allows for enhanced predictions of endometrial cancer, whilst allowing for a clinical decision-support platform which carries a greater degree of autonomy and, therein, diagnosis throughput.

Menopause is an under-reported and under-researched life stage for women living with type 1 diabetes (T1D) despite its lasting approximately 20 years. Menopause is associated with metabolic dysfunction leading to weight gain, impaired insulin sensitivity, hypertension, and hypercholesterolemia, each of which diminishes longevity for women living with diabetes. Its symptoms, affecting cognition, sleep patterns, mood, cardiac and vascular health, and physical health, are known to impact glucose variability dynamics. Associated vasomotor symptoms of hot-flushes/night-sweats, mood swings, anxiety, depression, and sexual dysfunction make it difficult to differentiate between symptoms of menopause and hypoglycemia. While it is recognized that transitioning through menopause increases the potential for developing diabetes, there are no recommendations on managing glucose variability or insulin resistance for women with pre-existing diabetes. Increasingly, women using wearable glucose sensor technologies, insulin pumps, and artificial pancreas systems have self-identified increased glucose variability in the data sets provided by wearable digital health technologies. These datasets, collected in real-time from women outside of traditional research settings, would have been unimaginable 20 years ago. Women highlighting the knowledge deficit are driven to learn and share more about menopause. Their quest for clinician support about the impact of hormone replacement therapies (HRT) on glycemic variability, and the associated risk of developing additional comorbidities, further illustrates the importance of this subject. This work uses datasets from wearable sensors, contributed by women with T1D to inform an understanding of their collective perimenopause and menopause journeys. Glucose readings across a period of weeks, leading up to and immediately following the initiation of prescribed medications, have been analyzed to investigate the physiological effects of HRT on the endocrine system of this sample of menopausal T1D women. Self-management and peer support is bridging the research void, which is why there should be more research into this topic.

Preterm pregnancies are one of the leading causes of morbidity and mortality amongst children under the age of five. This is a global issue and has been identified as an area requiring active research. The emphasis now is to identify and develop methods of predicting the likelihood of preterm birth. This paper uses physiological data from a group of patients in active labor. The dataset contains information about fetal heart rate (FHR) and maternal heart rate (MHR) for all patients and electrohysterogram (EHG) recordings for the measurement of uterine contractions. For the physiological data analysis and associated signal processing, we utilize deep wavelet scattering (DWS). This is an unsupervised decomposition and feature extraction method combining characteristics from deep learning convolutions, as well as the classical wavelet transform, to observe and investigate the extent to which active preterm labor can be accurately identified from an acquired physiological signal, the results of which were compared with the metaheuristic linear series decomposition learner (LSDL). Additional machine learning algorithms are tested on the acquired physiological data to allow for the identification of optimal model architecture for this specific physiological data.

Angelman syndrome (AS) is caused by loss of information from the 15q11.2-13 region on the maternal chromosome with striking phenotypic difference from Prader–Willi syndrome in which information is lost from the same region on the paternal chromosome. Motivation for social contact and sensory seeking behaviors are often noted as characteristics of the phenotype of AS and it has been argued that the strong drive for social contact supports a kinship theory interpretation of genomic imprinting. In this study we developed an experimental paradigm for quantifying the motivation for social contact in AS and examined differences across the genetic subtypes that cause AS [deletion, imprinting centre defect (ICD), uniparental disomy and UBE3A mutation]. Using single case experimental designs we examined the rate of acquisition of behavioral responses using operant learning paradigms for 21 children with AS whilst systematically varying the nature of social and sensory reinforcement. Variability in rates of acquisition was influenced by the nature of rewarding stimuli. Across the total sample both sensory stimuli and social contact could increase the rate of rewarded behavior with difference between children in the most effective reward. A striking difference in the rewarding properties of social contact across genetic subtypes was evidenced by non-deletion genetic causes of AS showing significantly higher rates of responding than the deletion cause in the social reinforcement paradigm. The results indicate that reinforcer assessment can beneficially inform behavioral interventions and that within syndrome variability in the behavioral phenotype of AS is likely driven by genetic difference. The non-deletion cause of AS, and particularly the ICD group, may be the optimal group for further study of genomic imprinting.

BackgroundChildren on the autism spectrum are consistently reported to underachieve compared to ability. In typically developing children, anxiety is a strong predictor of poor school performance. Despite the high prevalence of anxiety disorders among children on the spectrum, the impact of their anxiety on academic achievement is under-researched. The main aim of this project is to determine the moderating role anxiety may have in the development of academic learning behaviours (academic enablers) in children on the spectrum. This project addresses a gap in knowledge about the possible associations between anxiety and academic achievement in children on the spectrum. Understanding these associations opens up the possibility of new intervention pathways to enhance academic outcomes through anxiety reduction/prevention.MethodsThis longitudinal study will aim to recruit 64 children on the spectrum aged 4-5 years and their parents. Information will be gathered from children, parents and teachers. Children will be randomly assigned to one of two conditions in order to experimentally manipulate anxiety levels in the sample: experimental (to receive an anxiety reduction/prevention program, N = 32) or control (no intervention/treatment as usual, N = 32). The primary outcome measures are child academic skills and enabling behaviours assessed using the Academic Competence Evaluation Scales and the WIAT-II. Anxiety will be assessed through parent and teacher report. Assessments will be conducted at baseline, post-experimental manipulation of anxiety, and within the first year of formal schooling. It is hypothesised that anxiety will moderate the relationship between autism characteristics and academic enablers.DisseminationResults will be disseminated through peer-reviewed manuscripts and conference presentations. Lay summaries will be provided to all participants and available on the research centre website.

Abstract Background It is well documented that mothers of children with intellectual disabilities or autism experience elevated stress, with mental health compromised. However, comparatively little is known about mothers of children with rare genetic syndromes. This study describes mental health and well-being in mothers of children with 13 rare genetic syndromes and contrasts the results with mothers of children with autism. Methods Mothers of children with 13 genetic syndromes (n = 646; Angelman, Cornelia de Lange, Down, Fragile-X, Phelan McDermid, Prader-Willi, Rett, Rubenstein Taybi, Smith Magenis, Soto, Tuberous Sclerosis Complex, 1p36 deletion and 8p23 deletion syndromes) and mothers of children with autism (n = 66) completed measures of positive mental health, stress and depression. Using Bayesian methodology, the influence of syndrome, child ability, and mother and child age were explored in relation to each outcome. Bayesian Model Averaging was used to explore maternal depression, positive gain and positive affect, and maternal stress was tested using an ordinal probit regression model. Results Different child and mother factors influenced different aspects of mental well-being, and critically, the importance of these factors differed between syndromes. Maternal depression was influenced by child ability in only four syndromes, with the other syndromes reporting elevated or lower levels of maternal depression regardless of child factors. Maternal stress showed a more complex pattern of interaction with child ability, and for some groups, child age. Within positive mental health, mother and child age were more influential than child ability. Some syndromes reported comparable levels of depression (SMS, 1p36, CdLS) and stress (SMS, AS) to mothers of children with autism. Conclusions Bayesian methodology was used in a novel manner to explore factors that explain variability in mental health amongst mothers of children with rare genetic disorders. Significant proportions of mothers of children with specific genetic syndromes experienced levels of depression and stress similar to those reported by mothers of children with autism. Identifying such high-risk mothers allows for potential early intervention and the implementation of support structures.

Involving the autism community in research increases the real-world translation and impact of findings. The current study explored the research priorities of parents of school-aged children on the autism spectrum across the home, school, and community settings. A combination of content analysis of an online questionnaire (n = 134) and Q-sort methodology (n = 9) was used. The most commonly identified research priorities in the online questionnaire were child health and well-being (home setting), socialisation and social support (school), and community awareness and understanding of autism (community). The Q-sort method highlighted different top priorities, with understanding the parent, sibling, child and family impact and stress the highest ranked priority for home, teacher/staff education and support for the school, and recognizing and supporting anxiety for the community. The implications of the findings are discussed in relation to shifting the framework of autism research to align research agendas with parental priorities.