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Managers of small commercial building (SCB) portfolios need to understand occupant interactions with heating, ventilation, and air-conditioning (HVAC) systems to reduce energy use and greenhouse gas (GHG) emissions. In Canada, SCBs are currently underserved by energy conservation and thermal analysis tools because of their dispersion and lower payback potential. However, the emergence of smart thermostats (STs) and their central data collection platform provide a cost-effective solution to gather data from portfolios of SCBs and improve our understanding of occupant-HVAC interactions. This article analyzes the relationship between HVAC schedules (temperature set-points), indoor thermal conditions (dry-bulb temperature and relative humidity), and occupant behavior (thermostat overrides) in a portfolio of 30 SCBs in Ontario, Canada. The results reveal that temperature set-points were not properly selected in the portfolio of SCBs, leading to a large range of indoor thermal conditions and increased thermostat overrides. Specifically, the study demonstrates that building- and zone-specific HVAC schedules are necessary to minimize discomfort and reduce energy consumption in the portfolio of SCBs. The findings of this study can provide valuable insights for portfolio managers to improve HVAC schedules in a manner that reduces energy consumption and GHG emissions while accommodating occupants' thermal comfort and productivity. [ABSTRACT FROM AUTHOR]

The past decade has witnessed a rapid evolution in rare disease (RD) research, fueled by the availability of genome-wide (exome and genome) sequencing. In 2011, as this transformative technology was introduced to the research community, the Care4Rare Canada Consortium was launched: initially as FORGE, followed by Care4Rare, and Care4Rare SOLVE. Over what amounted to three eras of diagnosis and discovery, the Care4Rare Consortium used exome sequencing and, more recently, genome and other 'omic technologies to identify the molecular cause of unsolved RDs. We achieved a diagnostic yield of 34% (623/1,806 of participating families), including the discovery of deleterious variants in 121 genes not previously associated with disease, and we continue to study candidate variants in novel genes for 145 families. The Consortium has made significant contributions to RD research, including development of platforms for data collection and sharing and instigating a Canadian network to catalyze functional characterization research of novel genes. The Consortium was instrumental to implementing genome-wide sequencing as a publicly funded test for RD diagnosis in Canada. Despite the successes of the past decade, the challenge of solving all RDs remains enormous, and the work is far from over. We must leverage clinical and 'omic data for secondary use, develop tools and policies to support safe data sharing, continue to explore the utility of new and emerging technologies, and optimize research protocols to delineate complex disease mechanisms. Successful approaches in each of these realms is required to offer diagnostic clarity to all families with RDs. After a decade of collaborative network science in Canada and three eras of RD gene discovery, the Care4Rare Canada Consortium recognized it was time to reflect on the lessons learned from our successes to best meet the challenges of RD diagnosis and discovery in the decade to come. [ABSTRACT FROM AUTHOR]

Introduction: Stigmatisation of alcohol and other drug (AOD) use disorders poses a significant barrier to treatment access. A review by the World Health Organization concluded that addictive disorders were the most stigmatised health condition. Few studies have examined whether different etiological models of addiction (MOA) have implications for public stigma toward AOD disorders. The current study examined whether beliefs representative of five MOA predict public stigma levels and whether stigma differs for AOD use disorders relative to other health conditions. Methods: Survey data were collected from Canada, the USA and Australia using an online data collection platform. Participants were randomised to one of four vignette manipulations describing an individual with an alcohol use disorder and/or other disorder. Participants' stigma toward the vignette character and beliefs related to five MOA (disease, moral, psychological, sociological, nature) were measured. Results: Stigma ratings were significantly higher in the alcohol use disorder condition compared to other conditions. Two MOA accounted for significant variance in stigma ratings, where greater beliefs in the nature and psychological MOA predicted significantly lower levels of stigma toward alcohol use disorder. Contrary to predictions, beliefs in the disease MOA did not relate to lower stigma. Lastly, beliefs in the moral MOA partly accounted for geographical region differences (the USA vs. Canada) in public stigma. Discussion and Conclusions: The current study provides further experimental support that AOD disorders are more stigmatised than others. Additionally, the findings suggest that MOA may relate differentially to perceived stigma, and that regional variability in such beliefs exists. [ABSTRACT FROM AUTHOR]

Video-based traffic analysis is a leading technology for streamlining transportation data collection. With traffic records from video cameras, unsupervised automated video analysis can detect various vehicle measures such as vehicle spatial coordinates and subsequently lane positions, speed, and other dynamic measures without the need of any physical interconnections to the road infrastructure. This paper contributes to the unsupervised automated video analysis by addressing two main shortcomings of the approach. The first objective is to alleviate tracking problems of over-segmentation and over-grouping by integrating region-based detection with feature-based tracking. This information, when combined with spatiotemporal constraints of grouping, can reduce the effects of these problems. This fusion approach offers a superior decision procedure for grouping objects and discriminating between trajectories of objects. The second objective is to model three-dimensional bounding boxes for the vehicles, leading to a better estimate of their geometry and consequently accurate measures of their position and travel information. This improvement leads to more precise measurement of traffic parameters such as average speed, gap time, and headway. The paper describes the various steps of the proposed improvements. It evaluates the effectiveness of the refinement process on data collected from traffic cameras in three different locations in Canada and validates the results with ground truth data. It illustrates the effectiveness of the improved unsupervised automated video analysis with a case study on 10 h of traffic data collection such as volume and headway measurements. [ABSTRACT FROM AUTHOR]

This research note discusses opportunities and challenges of using online survey engines for social network analysis (SNA) data collection and assessing sample size and representativeness. The discussion is based on a case study of SNA data collection for a pilot research on health-relevant policy networks in Canada. Our approach demonstrates how to use generic online survey tools in long-term snowball sampling campaigns and how to compute population size estimates through capture–mark–recapture formulas, thus solving a significant methodological challenge of collecting snowball data for unbounded networks. [ABSTRACT FROM AUTHOR]

Context: Patients with chronic conditions may face fragmented, uncoordinated and, therefore, substandard healthcare. Standardized indicators of quality of care for these patients will help identify areas of excellence and challenge. Canada is participating in an international survey using patient reported outcome measures and patient reported experience measures to provide internationally comparable standardized indicators. Unlike other countries, primary healthcare delivery is different across Canada and the impact this has on research remains unclear. Objective: The aim of this study was to describe the characteristics of primary healthcare delivery to capture the complexity of health systems across Canada. Methods: A pan-Canadian working group formed in 2020 from members of the Strategy for Patient- Oriented research Primary and Integrated Health Care Innovation Network. The advisory group conducted a series of virtual conferences and national meetings to help define approaches for the methods of recruitment, data collection, and data dissemination for the international survey. The different site approaches were qualitatively synthesized to form a description of the modalities of primary care delivery across the nation. Results: Nine out of 13 possible distinct sites have contributed to the study. Initial findings have determined that only a few sites that are currently using electronic medical records (EMR) in primary care are able to consider an automated data collection using EMRs. Other sites will need to use different data collection platforms, online surveys, or multiple methods of contact, such as emails, mail, or phone calls. None of the sites have a complete registry of eligible practices or patients thus they will need to use different methods of engagement and dissemination, such as newsletters, articles, social media, local news, provider learning/education sessions, or podcasting. Conclusions: Canadian primary care is delivered through a highly heterogenous health systems thus requiring a diversity of methods for participants' recruitment, and data collection and dissemination. This work provides an opportunity to determine how this diversity can impact national and international primary care research projects. This has important implications for research capacity building with a practice-based data source to evaluate and optimize the health care utilization by primary care patients. [ABSTRACT FROM AUTHOR]