Generative AI with WGAN-GP for boosting seizure detection accuracy.

Background: Imbalanced datasets pose challenges for developing accurate seizure detection systems based on electroencephalogram (EEG) data. Generative AI techniques may help augment minority class data to facilitate automatic epileptic seizure detection.

New Method: This study investigates the impact of various data augmentation (DA) approaches, including Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), Vanilla GAN, Conditional GAN (CGAN), and Cramer GAN, on classification performance with Random Forest models.

Exploring the Associations of Personality and Sexual Health Using the DSM-5 Alternative Model for Personality Disorders.

This study explored the relationships between sexual health indicators (i.e., sexual satisfaction, distress, and function) and the DSM-5 Alternative Model for Personality Disorders, a promising dimensional framework for assessing personality pathology.

Unveiling distinct kinematic profiles among total knee arthroplasty candidates through clustering technique.

Background: Characterizing the condition of patients suffering from knee osteoarthritis is complex due to multiple associations between clinical, functional, and structural parameters. While significant variability exists within this population, especially in candidates for total knee arthroplasty, there is increasing interest in knee kinematics among orthopedic surgeons aiming for more personalized approaches to achieve better outcomes and satisfaction. The primary objective of this study was to identify distinct kinematic phenotypes in total knee arthroplasty candidates and to compare different methods for the identification of these phenotypes.

Multi-modal recommender system for predicting project manager performance within a competency-based framework.

The evaluation of performance using competencies within a structured framework holds significant importance across various professional domains, particularly in roles like project manager. Typically, this assessment process, overseen by senior evaluators, involves scoring competencies based on data gathered from interviews, completed forms, and evaluation programs. However, this task is tedious and time-consuming, and requires the expertise of qualified professionals.

Is there room for mothers' agency in the choice to breastfeed? A qualitative analysis of mothers' views on messages promoting breastfeeding in Quebec.

Introduction: This exploratory cross-sectional study focuses on the experiences of mothers regarding health messages promoting breastfeeding. The objective is to describe the content and context in which messages are conveyed.

Methods: A total of 944 new mothers responded to a questionnaire (15-31 January 2021) on their perception of health messages promoting breastfeeding and their feeling of agreement towards these messages, their intention to breastfeed, incentives received, and their relationship with the professionals.

Towards a greater engagement of universities in addressing climate change challenges.

Many higher education institutions around the world are engaged in efforts to tackle climate change. This takes place by not only reducing their own carbon footprint but also by educating future leaders and contributing valuable research and expertise to the global effort to combat climate change. However, there is a need for studies that identify the nature of their engagement on the topic, and the extent to which they are contributing towards addressing the many problems associated with climate change.

Measuring environmental awareness: An analysis using google search data.

Environmental awareness is usually measured using surveys. This paper aims to offer an alternative measure: an Environmental Awareness Index (EAI) constructed using Google search data provided by Google Trends. The benefits of using Google search data over surveys are that (i) they are less costly to obtain, (ii) they are available at high frequency, and (iii) they cover countries where no surveys are available.

Nurses' work experiences 5 years after hospital merger in the province of Quebec/Canada-An exploratory qualitative study.

In recent years, healthcare organisations in North America have undergone major structural changes. In particular, the province of Quebec in Canada adopted a reform in 2015 which led to the merging of healthcare organisations into centralised regional administrations (the 'CISSS'). As research indicates negative impacts of mergers on patient outcomes and difficulties for the nursing work group in particular, the present paper aims to answer calls for more research about the long-term effects of major organisational changes on nurses' professional practice and well-being.

How do Informal Caregivers of Seniors' Tasks Lead to Presenteeism and Absenteeism Behaviors? A Canadian Quantitative Study.

This study extends our knowledge on the role of informal caregivers of seniors and the impact of this role on presenteeism and absenteeism at work. Based on the conservation of resources theory, this article seeks to gain insights into the mechanisms and antecedents of presenteeism and absenteeism among employees who are also informal caregivers of seniors. Specifically, this article argues that family-work conflict and emotional exhaustion mediate the relationship between the informal caregiver's role, presenteeism, and absenteeism.

How Psychosocial Safety Climate Helped Alleviate Work Intensification Effects on Presenteeism during the COVID-19 Crisis? A Moderated Mediation Model.

Healthcare sector organizations have long been facing the issue of productivity loss due to presenteeism which is affected by psychosocial safety climate (PSC) and work intensification. Presenteeism has visibly increased among nurses during COVID-19 pandemic period. Grounded in COR theory and sensemaking theory, the current study aimed to examine the role PSC plays as driver or moderator to reduce presenteeism by lessening work intensification over time and the impact of work intensification over time on presenteeism during the COVID-19 pandemic.

Musculoskeletal disorder (MSD) prevention practices by nurses working in health care settings: Facilitators and barriers to implementation.

The incidence of musculoskeletal disorders (MSD) in the healthcare professional population is concerning. In particular, the high incidence of back injuries is an issue for nurses. Although many MSD prevention initiatives are being implemented, these practices are not succeeding in reducing MSDs.

Advantages of and Barriers to Crafting New Technology in Healthcare Organizations: A Qualitative Study in the COVID-19 Context.

Nursing professionals are constantly required to adapt to technological changes, and especially so in the wake of COVID-19, which has prompted the development of new digital tools. A new and specific form of job crafting in relation to new technology has recently emerged in the literature; that is, adoption job crafting. However, little is known about this specific form of job crafting, especially within the pandemic context.

Integration of sex and gender in interventions by students in ergonomics.

This article aims to analyse the integration of sex and gender (s/g) by ergonomics students during their internship at the master's degree level, following training sessions on s/g issues in the workplace. This exploratory research used a descriptive mixed-methods design, encompassing evaluation of students' intention to use the content from the training ( = 13 students), and a multiple case study ( = 5 ergonomics interventions). The results show that while students found the training relevant, they only minimally integrated s/g in their interventions and when they did, it was primarily from an anthropometric and physiological perspective.

Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers.

Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification to provide healthcare of higher standards. The purpose of this study is to investigate a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare. This method classifies wearable sensor acceleration time series data of human movement using an efficient classifier combination of feature engineering-based and feature learning-based data representation.

A meta-analysis of mesophyll conductance to CO2 in relation to major abiotic stresses in poplar species.

Mesophyll conductance (gm) determines the diffusion of CO2 from the substomatal cavities to the site of carboxylation in the chloroplasts and represents a critical component of the diffusive limitation of photosynthesis. In this study, we evaluated the average effect sizes of different environmental constraints on gm in Populus spp., a forest tree model.

A Spectral-Based Approach for BCG Signal Content Classification.

This paper has two objectives: the first is to generate two binary flags to indicate useful frames permitting the measurement of cardiac and respiratory rates from Ballistocardiogram (BCG) signals-in fact, human body activities during measurements can disturb the BCG signal content, leading to difficulties in vital sign measurement; the second objective is to achieve refined BCG signal segmentation according to these activities. The proposed framework makes use of two approaches: an unsupervised classification based on the Gaussian Mixture Model (GMM) and a supervised classification based on K-Nearest Neighbors (KNN). Both of these approaches consider two spectral features, namely the Spectral Flatness Measure (SFM) and Spectral Centroid (SC), determined during the feature extraction step.

Head CT overuse in children with a mild traumatic brain injury within two Canadian emergency departments.

Background: The validated Pediatric Emergency Care Applied Network (PECARN) rule helps determine the relevance of a head computerized tomography (CT) for children with mild traumatic brain injury (mTBI). We sought to estimate the potential overuse of head CT within two Canadian emergency departments (EDs).

Methods: We conducted a retrospective chart review of children seen in 2016 in a paediatric Level I (site 1) and a general Level II (site 2) trauma centre.

Inferring Destinations and Activity Types of Older Adults From GPS Data: Algorithm Development and Validation.

Background: Outdoor mobility is an important aspect of older adults' functional status. GPS has been used to create indicators reflecting the spatiotemporal dimensions of outdoor mobility for applications in health and aging. However, outdoor mobility is a multidimensional construct.

Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models.

The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal behaviors observed during activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we investigate a variety of deep learning models such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM and Autoencoder-CNN-LSTM for identifying and accurately predicting the abnormal behaviors of elderly people.

Knee Joint Biomechanical Gait Data Classification for Knee Pathology Assessment: A Literature Review.

Background: The purpose of this study is to review the current literature on knee joint biomechanical gait data analysis for knee pathology classification. The review is prefaced by a presentation of the prerequisite knee joint biomechanics background and a description of biomechanical gait pattern recognition as a diagnostic tool. It is postfaced by discussions that highlight the current research findings and future directions.

Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: A single center pilot study.

Background: In an intensive care units, experts in mechanical ventilation are not continuously at patient's bedside to adjust ventilation settings and to analyze the impact of these adjustments on gas exchange. The development of clinical decision support systems analyzing patients' data in real time offers an opportunity to fill this gap.

Objective: The objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict transcutaneous hemoglobin oxygen saturation 5 min (5min SpO2) after a ventilator setting change.

Challenges in Collecting Big Data in A Clinical Environment with Vulnerable Population: Lessons Learned from A Study Using A Multi-modal Sensors Platform.

Agitation is one of the most common behavioural and psychological symptoms in people living with dementia (PLwD). This behaviour can cause tremendous stress and anxiety on family caregivers and healthcare providers. Direct observation of PLwD is the traditional way to measure episodes of agitation.

3D imaging system for respiratory monitoring in pediatric intensive care environment.

Assessment of respiratory activity in pediatric intensive care unit allows a comprehensive view of the patient's condition. This allows the identification of high-risk cases for prompt and appropriate medical treatment. Numerous research works on respiration monitoring have been conducted in recent years.

An analysis of 3D knee kinematic data complexity in knee osteoarthritis and asymptomatic controls.

Three-dimensional (3D) knee kinematic data, measuring flexion/extension, abduction/adduction, and internal/external rotation angle variations during locomotion, provide essential information to diagnose, classify, and treat musculoskeletal knee pathologies. However, and so across genders, the curse of dimensionality, intra-class high variability, and inter-class proximity make this data usually difficult to interpret, particularly in tasks such as knee pathology classification. The purpose of this study is to use data complexity analysis to get some insight into this difficulty.