Postpandemic Evaluation of the Eco-Efficiency of Personal Protective Equipment Against COVID-19 in Emergency Departments: Proposal for a Mixed Methods Study.

Background: The COVID-19 pandemic has had a profound impact on emergency department (ED) care in Canada and around the world. To prevent transmission of COVID-19, personal protective equipment (PPE) was required for all ED care providers in contact with suspected cases. With mass vaccination and improvements in several infection prevention components, our hypothesis is that the risks of transmission of COVID-19 will be significantly reduced and that current PPE use will have economic and ecological consequences that exceed its anticipated benefits.

Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis.

Computer vision and deep learning have the potential to improve medical artificial intelligence (AI) by assisting in diagnosis, prediction, and prognosis. However, the application of deep learning to medical image analysis is challenging due to limited data availability and imbalanced data. While model performance is undoubtedly essential for medical image analysis, model trust is equally important.

COVID-Net USPro: An Explainable Few-Shot Deep Prototypical Network for COVID-19 Screening Using Point-of-Care Ultrasound.

As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent the further spread of the virus and lessen the burden on healthcare providers is a necessity. As a cheap and widely accessible medical image modality, point-of-care ultrasound (POCUS) imaging allows radiologists to identify symptoms and assess severity through visual inspection of the chest ultrasound images. Combined with the recent advancements in computer science, applications of deep learning techniques in medical image analysis have shown promising results, demonstrating that artificial intelligence-based solutions can accelerate the diagnosis of COVID-19 and lower the burden on healthcare professionals.

COVIDx-US: An Open-Access Benchmark Dataset of Ultrasound Imaging Data for AI-Driven COVID-19 Analytics.

Background: The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. Apart from the global health crises, the pandemic has also caused significant economic and financial difficulties and socio-physiological implications. Effective screening, triage, treatment planning, and prognostication of outcome play a key role in controlling the pandemic.

Direct-access physiotherapy to help manage patients with musculoskeletal disorders in an emergency department: Results of a randomized controlled trial.

Objectives: The objective was to evaluate the effects of direct-access physiotherapy on patients presenting with a musculoskeletal disorder (MSKD) to the emergency department (ED) on clinical outcomes and use of health care resources.

Methods: We conducted a randomized controlled trial in an academic ED in Québec City, Canada. We included patients aged 18 to 80 years with minor MSKD.

Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing.

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve.

Effects of physical activity on pediatric reference data for obesity.

Purpose: To examine the influence of physical activity on pediatric obesity reference data for Canada.

Methods: The sample included 3527 boys and 3554 girls, 6 to 18 years of age, from the 2004 Canadian Community Health Survey: Nutrition component. The heights and weights of the participants were directly measured, and the body mass index was calculated (BMI: kg/m(2)).

Body mass index in urban Canada: neighborhood and metropolitan area effects.

Objectives: We investigated the influence of neighborhood and metropolitan area characteristics on body mass index (BMI) in urban Canada in 2001.

Methods: We conducted a multilevel analysis with data collected from a cross-sectional survey of men and women nested in neighborhoods and metropolitan areas in urban Canada during 2001.

Results: After we controlled for individual sociodemographic characteristics and behaviors, the average BMIs of residents of neighborhoods in which a large proportion of individuals had less than a high school education were higher than those BMIs of residents in neighborhoods with small proportions of such individuals (P< .

Waiting for care in Canada: findings from the health services access survey.

Waiting for care has been and continues to be a major issue for the healthcare sector in Canada. While considerable gains have been made regarding valid and reliable information on waiting times, gaps remain. Statistics Canada continues to provide information regarding patients' experiences in accessing care at the national and provincial levels, including how long individuals waited for specialized services, through the Health Services Access Survey.

Impact of the Ottawa Decision Support Framework on the agreement and the difference between patients' and physicians' decisional conflict.

Background: The Ottawa Decision Support Framework (ODSF) provides a process that facilitates shared decision making.

Objective: To assess the impact of implementing the ODSF on the agreement and the difference between patients' and physicians' decisional conflict scores.

Design: In total, 120 physicians and 903 patients enrolled in this before-and-after study.

Neighbourhood influences on health in Montréal, Canada.

This paper examines neighbourhood effects on health within a large Canadian city--Montréal. Our approach is to consider that individual health outcomes are determined both by individual and neighbourhood characteristics and we consciously take on the problem of neighbourhood definition by developing 'natural' neighbourhoods. Our data come from the Montréal health region sample of the 2000/1 Canadian Community Health Survey, a comprehensive national survey that contains information on health outcomes as well as behavioural and socio-demographic information.

Factors related to adolescents' self-perceived health.

Objectives: This analysis examines self-perceived health among Canadian adolescents aged 12 to 17, and factors associated with ratings of very good/excellent health.

Data Source: The data are from cycle 1.1 of the 2000/01 Canadian Community Health Survey (CCHS), conducted by Statistics Canada.

Factors associated with the difference in score between women's and doctors' decisional conflict about hormone therapy: a multilevel regression analysis.

Objective: To explore factors associated with the difference in score between women's and doctors' decisional conflict about hormone therapy (HT).

Design: Secondary analysis.

Setting And Participants: family doctors were randomized to prepare women for counselling about HT using either a decision aid or a pamphlet.

Changes in unmet health care needs.

Objectives: This article examines recent trends in self-reported unmet health care needs among the household population aged 12 or older, and explores various explanations for the increase observed.

Data Sources: The data are from the first half (September 2000 through February 2001) of data collection for cycle 1.1 of the Canadian Community Health Survey and from cross-sectional (1994/95 through 1998/99) household components of the National Population Health Survey.