A Scale of Parental Anxiety about Pediatric Emergency Medical Care Services of Japan: Development, Reliability, Validity, Generalizability and Usefulness

Aim: The purpose of this study was to develop a scale, “parental anxiety about pediatric emergency medical care services” (PAPEMCS), and to evaluate its psychometric properties. Methods: Participants were 14,510 parents with children 6 years old or younger in Kagawa Prefecture. Using each half of the participants, exploratory factor analysis was performed to generate items and factors for the PAPEMCS, and confirmatory factor analysis (CFA) was used to establish the construct validity. The generalizability of the PAPEMCS was evaluated by congruence tests and multigroup CFA. The usefulness of the PAPEMCS was established by the relationship between the PAPEMCS and non-urgent usage of pediatric emergency medical care services (PEMCS). Results: The PAPEMCS compromised 4 factors: “anxiety about quality of PEMCS”, “anxiety about PEMCS system”, “anxiety about public support”, and “anxiety about private support”. All reliability estimates (polychoric ordinal alpha coefficients, item-rest correlations), the item discrimination, 5 fit indices for CFA, the convergent validity (indicator reliabilities, composite reliabilities, average variance extracteds), and the discriminant validity fulfilled the acceptability thresholds. All generalizability estimates fulfilled the predetermined levels of acceptability (Tucker’s congruence coefficients, congruence tests, strict factorial invariance). The usefulness of the PAPEMCS was established by the higher scores of the PAPEMCS being related to non-urgent usage of PEMCS. Conclusions: The PAPEMCS demonstrated satisfactory reliability, validity, generalizability and usefulness. The PAPEMCS is useful to quantify the contents and extent of parental anxiety about PEMCS, and to clarify the mechanisms of non-urgent PEMCS usage.

Analysis of the Invariance and Generalizability of Multiple Linear Regression Model Results Obtained from Maslach Burnout Scale through Jackknife Method

The purpose of this study was to examine the burnout levels of research assistants in Ondokuz Mayis University and to examine the results of multiple linear regression model based on the results obtained from Maslach Burnout Scale with Jackknife Method in terms of validity and generalizability. To do this, a questionnaire was given to 11 research assistants working at Ondokuz Mayis University and the burnout scores of this questionnaire were taken as the dependent variable of the multiple linear regression model. The variable of burnout was explained with the variables of age, weekly hours of classes taught, monthly average credit card debt, numbers of published articles and reports, gender, marital status, number of children and the departments of the research assistants. Dummy variables were assigned to the variables of gender, marital status, number of children and the departments of the research assistants and thus, they were made quantitative. The significance of the model as a result of multiple linear regressions was examined through backward elimination method. After this, for the five explanatory variables which influenced the variable of burnout, standardized model coefficients and coefficients of determination, and 95% confidence intervals of these values were estimated through Jackknife Method and the generalizability of the parameter estimation results of these variables on population was researched.

Double Deep Q-Network Decoder Based on EEG Brain-Computer Interface

Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through electroencephalogram(EEG)and translated into neural intentions reflecting the user’s behavior.Correct decoding of the neural intentions then facilitates the control of external devices.Reinforcement learning-based BCIs enhance decoders to complete tasks based only on feedback signals(rewards)from the environment,building a general framework for dynamic mapping from neural intentions to actions that adapt to changing environments.However,using traditional reinforcement learning methods can have challenges such as the curse of dimensionality and poor generalization.Therefore,in this paper,we use deep reinforcement learning to construct decoders for the correct decoding of EEG signals,demonstrate its feasibility through experiments,and demonstrate its stronger generalization on motion imaging(MI)EEG data signals with high dynamic characteristics.

Toward Artificial General Intelligence: Deep Reinforcement Learning Method to AI in Medicine

Artificial general intelligence (AGI) is the ability of an artificial intelligence (AI) agent to solve somewhat-arbitrary tasks in somewhat-arbitrary environments. Despite being a long-standing goal in the field of AI, achieving AGI remains elusive. In this study, we empirically assessed the generalizability of AI agents by applying a deep reinforcement learning (DRL) approach to the medical domain. Our investigation involved examining how modifying the agent’s structure, task, and environment impacts its generality. Sample: An NIH chest X-ray dataset with 112,120 images and 15 medical conditions. We evaluated the agent’s performance on binary and multiclass classification tasks through a baseline model, a convolutional neural network model, a deep Q network model, and a proximal policy optimization model. Results: Our results suggest that DRL agents with the algorithmic flexibility to autonomously vary their macro/microstructures can generalize better across given tasks and environments.

Kernel-blending connection approximated by a neural network for image classification

This paper proposes a kernel-blending connection approximated by a neural network(KBNN)for image classification.A kernel mapping connection structure,guaranteed by the function approximation theorem,is devised to blend feature extraction and feature classification through neural network learning.First,a feature extractor learns features from the raw images.Next,an automatically constructed kernel mapping connection maps the feature vectors into a feature space.Finally,a linear classifier is used as an output layer of the neural network to provide classification results.Furthermore,a novel loss function involving a cross-entropy loss and a hinge loss is proposed to improve the generalizability of the neural network.Experimental results on three well-known image datasets illustrate that the proposed method has good classification accuracy and generalizability.

Methodological Considerations Which Could Improve Spinal Cord Injury Research

Advances in spinal cord injury-based research in the last 50 years have resulted in significant improvements to therapy options.However,the efficacy of such research could be further enhanced if threats to internal and external validity were addressed.To provide perspective,a sample topic was identified:the effects of acute and chronic exercise on clinical and sub-clinical markers of cardiovascular health.The intention was not a systematic review,nor a critique of exercise-based research,but rather a means to generate further discussion.Thirty-one articles were identified,and four common issues were found relating to:(1)sampling;(2)study design;(3)control group;and(4)clinical inference.These concerns were largely attributed to insufficient resources,and challenges associated with recruiting individuals with spinal cord injury.Overcoming these challenges will be difficult,but some opportunities include:(1)implementing multi-center trials;(2)sampling from subject groups appropriate to the research question;(3)including an appropriate control group;and(4)clearly defining clini-cal inference.These opportunities are not always feasible,and some easier to implement than others.However,addressing these concerns may assist in progressing spinal cord injury-based research,thereby helping to ensure steady advancement of therapy options for persons with spinal cord injury.