ABSTRACT: Current automated pain assessment methods only focus on infants or youth. They are less practical because the children who suffer from postoperative pain in clinical scenarios are in a wider range of ages. In this article, we present a large-scale Clinical Pain Expression of Children (CPEC) dataset for postoperative pain assessment in children. It contains 4104 preoperative videos and 4865 postoperative videos of 4104 children (from 0 to 14 years of age), which are collected from January 2020 to December 2020 in Anhui Provincial Children's Hospital. Moreover, inspired by the dramatic successful applications of deep learning in medical image analysis and emotion recognition, we develop a novel deep learning-based framework to automatically assess postoperative pain according to the facial expression of children, namely Children Pain Assessment Neural Network (CPANN). We train and evaluate the CPANN with the CPEC dataset. The performance of the framework is measured by accuracy and macro-F1 score metrics. The CPANN achieves 82.1% accuracy and 73.9% macro-F1 score on the testing set of CPEC. The CPANN is faster, more convenient, and more objective compared with using pain scales according to the specific type of pain or children's condition. This study demonstrates the effectiveness of deep learning-based method for automated pain assessment in children.
Abstract Throughout the pandemic Chile implemented a series of public health mandates restricting mobility and high social-contact activities with a goal of reducing disease spread. In this paper, we study the impact of one of these policies-central planner variation in school reopenings on labor market outcomes. We examine how access to supervised care for children in school affected mothers’ labor supply and show that mothers increased labor force participation anywhere from 2.6 to 21.0 percentage points (ppts) as schools reopened. As mothers came back to the labor force, however, unplanned disruptions decreased their ability to stay actively engaged in work and increased their take up of leave from work, an artifact of unanticipated sick children and quarantine policies that particularly affected mothers who were secondary earners in the household. Conditional on being in the labor force, having a teenager buffered both mothers and fathers from work disruptions; parents were more likely to be actively working and less likely to be on leave. Our findings support a theory that parental labor supply is sensitivity to disruptions in the care of children but also depends on household composition and each parent’s role beyond gender. Policies that encourage consistent formalized systems of care and learning for children will not only benefit children, but also a second-earner’s ability to re-enter the labor force and advance at work. JEL: J13, J16, J22
BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a considerably common childhood-onset neurodevelopmental disorder, often associated with socio-behavioral and academic difficulties. There is an increased risk for development of a range of future problems such as psychiatric disorders, difficulties in employment, and relationships. The aim of this study was to know the prevalence and determinants of ADHD among primary school children in district Dehradun and to assess the learning difficulties and classroom behavior in these students. MATERIALS AND METHODS: In this cross-sectional study, overall, 228 students (aged 6–12 years) studying in a primary school were assessed for ADHD after seeking the written informed consent from their parents. The data were analyzed using Excel sheet and SPSS software (22.0 version). For all qualitative measures, frequency and percentages were calculated, and for quantitative measures, mean and standard deviation were calculated. For analysis of categorical values, Chi-square test was used. P < 0.05 was considered statistically significant. RESULTS: The prevalence of ADHD was found to be 11.8% based on the teacher tool only and 1.75% based on the parent and teacher tools combined. ADHD was found to be significantly more in males. Inattention was the most prevalent subtype of ADHD, and children screened positive for ADHD had significant learning difficulties at various levels. CONCLUSION: The possibility of ADHD in students with academic difficulties should not be ignored as children with ADHD usually face significant problems at school such as learning difficulties and have been shown to be at increased risk for a broad range of negative outcomes. Early identification and treatment of ADHD can significantly reduce the rates of some of these poor outcomes. Screening these children in the early years will help the parents, children, teachers, and community at large.
Gurpreet Kaur Chawla, Ruchi Juyal, Deep Shikha et al.
Purpose Intervention provided to school-age children with developmental language disorder often relies on the provision of performance feedback, yet it is unclear whether children with this disorder benefit from feedback-based learning. The study evaluates the effect of performance feedback on learning in children with developmental language disorder. Method Thirteen 8- to 12-year-old children with developmental language disorder and 14 age- and gender-matched children with typical language development completed two learning tasks whose objective was to pair nonword novel names with novel objects. The two tasks differed in the presence of performance feedback to guide learning. Learning outcomes on immediate and follow-up tests were compared between the feedback-based and feedback-free tasks. Additionally, an electrophysiological marker of feedback processing was compared between children with and without developmental language disorder. Results Children with developmental language disorder demonstrated poorer learning outcomes on both tasks when compared with their peers, but both groups achieved better accuracy on the feedback-free task when compared with the feedback-based task. Within the feedback-based task, children were more likely to repeat a correct response than to change it after positive feedback but were as likely to repeat an error as they were to correct it after receiving negative feedback. While children with typical language elicited a feedback-related negativity with greater amplitude to negative feedback, this event-related potential had no amplitude differences between positive and negative feedback in children with developmental language disorder. Conclusions Findings indicate that 8- to 12-year-old children benefit more from a feedback-free learning environment and that negative feedback is not as effective as positive feedback in facilitating learning in children. The behavioral and electrophysiological data provide evidence that feedback processing is impaired in children with developmental language disorders. Future research should evaluate feedback-based learning in children with this disorder using other learning paradigms.
Abstract Nature-based learning within the primary school curriculum offers numerous potential benefits. However, there is a lack of clarity about how school grounds can be designed to enable effective nature-based learning. There is also little knowledge of how specific features within green school grounds contribute to specific desirable outcomes, such as improved academic performance or health. To address this gap, a systematised review of peer-reviewed academic literature was undertaken, with 173 databases searched from January to December 2021. The search included studies of nature-based learning on school grounds and literature concerned with the design of green school grounds for fostering nature connectedness and broader educational outcomes for primary school children aged 5 to 11. No date or geographical restrictions were applied. Of a total of 285 articles initially identified, 11 matched the inclusion criteria. Results from these indicate significant research gaps on the design of green learning spaces in schools. While studies note apparent positive links between nature-based learning in school grounds and improved subject-specific learning, wellbeing, and nature connectedness, there is very little empirical evidence of how specific design features are linked to specific outcomes. Furthermore, the current evidence base is poorly representative of different social, cultural, and geographical contexts and not fully reflective of all primary schooling ages. The challenging contexts of urban schools and schools with small footprints are also inadequately addressed. These findings indicate an urgent need for increased research to guide the design of school grounds for the implementation of nature-based learning programmes for primary school learners.
BACKGROUND: Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children. METHODS AND FINDINGS: Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ -6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered. CONCLUSIONS: To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia.
Web-based learning, defined as the purposeful use of internet technology to impart knowledge, has gained attention as an educational tool in developing countries. However, limited evidence exists on how children with learning disabilities in impoverished regions adopt such methods. This study aims to identify the variables influencing the adoption of web-based learning among children with learning disabilities, focusing on performance expectancy, effort expectancy, social influence, and training. The research utilized survey questionnaires to assess these factors among 196 children with learning disabilities enrolled in mathematics and Arabic classes in Jordan's Balqa governorate. Results revealed a positive relationship between behavioral intention and the acceptance of web-based learning systems, highlighting the critical role of the identified variables. The findings underscore the importance of tailoring web-based learning systems to the needs of children with learning disabilities, offering actionable insights for system developers and educators in similar contexts. Recommendations include prioritizing user-friendly designs, targeted training programs, and strategies to enhance social and performance-related perceptions to improve learning outcomes. Keywords: Effort expectancy; learning disabilities; performance expectancy; social influence; training; web-based learning
Suleiman D. Al-Ahmad, Ali Alawammreh, Abdallah Mishael Obeidat et al.
The importance of visual perception, motor coordination and visual motor integration (VMI) skills for school achievements has been shown in various studies. Cognitive orientation to daily occupational performance or CO-OP is an evidence-based approach that has been effective in children with developmental coordination disorder and autism spectrum. The present study evaluated the effect of CO-OP approach in improving VMI skills in children with learning disability. Sample of 58 children were included in the study. National Centre for Learning Disabilities (NCLD) Checklist was used as a screen tool and pre and post intervention Beery VMI and Canadian Occupational Performance Measures (COPM) was used as outcome measures.Occupational therapy program using CO-OP approach, based on each childs individualized goals. children were thought global strategy of GO-PLAN-DO-CHECK the sessions were conducted for 12 weeks one hour a day three times a week. After 12 weeks post assessment was done using t test. There significant difference in pre and post CO-OP intervention. p-value was found to be less than 0.0001 for VMI, COPM performance and satisfaction whereas, there was significant difference in motor coordination and visual perception component of Beery VMI. The result shows that CO-OP approach is effective in improving visual motor integration skills of children with learning disability.\n\n
This study was motivated by the decline in student learning outcomes in the odd semester of the 2022/2023 school year which was caused by students paying less attention to the explanation and material from the teacher during the learning process. Such as not doing assignments on time, often chatting alone when the teacher explains, lack of interest in learning, and there are some children who have low learning outcomes. Therefore, it is necessary to instill discipline in learning since childhood, because it will help children control themselves in the formation of self-awareness in learning without an element of compulsion. This study aims to determine the relationship between learning discipline and students' interest in learning. The research method used is a quantitative method with a correlational approach that uses ex post facto research. The sample in the study was class III students with a total of 14 people, whose sampling used the saturated sample method because all students in class III were sampled in the study. Data collection was carried out using questionnaire and documentation techniques. The results showed that: 1) There is a significant relationship between learning discipline and student learning outcomes with a Thitung value of 8.131. 2) There is a significant relationship between learning discipline and student learning outcomes with a Thitung of 3.459. 3). There is a significant relationship simultaneously between learning discipline and learning interest on student learning outcomes with a T-count of 6.698.
