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Writing involves more than attention to form (e.g., orthography, grammar), since it requires attention to text type, content, and genre. However, most students of English as a second language (L2) tend to prioritize linguistic accuracy in their writing, to the detriment of the content of their texts. Automatic speech recognition (ASR) has the potential to mitigate this, as it reduces the cognitive burden of writing by facilitating the text input process (using a skill most humans possess--speaking), offering assistance in spelling, and allowing a focus on other aspects of the task (e.g., cohesion, content). Automatic speech recognition is not only accessible and free, but it also fulfills Chapelle's (2001) criteria of an effective computer-assisted language learning tool (e.g., authenticity, learner fit). Despite these affordances, there is a dearth of studies examining the possible affordances of ASR for writing. This mixed methods, one-shot study examines L2 writers' perceptions of using ASR to write using the technology acceptance model (TAM). Seventeen (N = 17) undergraduate students at a Canadian university were provided with training on Google Voice Typing (Google Docs) and carried out a series of ASR-based writing tasks over a two-hour period. In order to measure their perceptions of the target criteria, participants filled in a TAM-informed survey consisting of statements about their experience with ASR scored on a 7-point Likert scale. To further explore the participants' perceptions, semi-structured interviews followed. Findings indicate positive perceptions of ASR's usefulness in terms of language learning and its ease of use due to the user-friendly voice commands. This suggests that ASR has pedagogical potential, thus requiring further examination to determine its optimal use for L2 writing.

Objectives: Automatic assessment systems are widely used to provide rapid feedback for students and reduce grading time. Despite the benefits of increased efficiency and improved pedagogical outcomes, an ongoing challenge is mitigating poor student behaviors when interacting with automatic assessment systems including numerous submissions, trial-and-error, and relying on marking feedback for problem solving. These behaviors negatively affect student learning as well as have significant impact on system resources. This research quantitatively examines how utilizing submission policies such as limiting the number of submissions and applying regression penalties can reduce negative student behaviors. The hypothesis is that both submission policies will have a significant impact on student behavior and reduce both the number of submissions and regressions in student performance. The research questions evaluate the impact on student behavior, determine which submission policy is the most effective, and what submission policy is preferred by students. Participants: The study involved two course sections in two different semesters consisting of a total of 224 students at the University of British Columbia, a research-intensive university. The students were evaluated using an automated assessment system in a large third year database course. Study Methods: The two course sections used an automated assessment system for constructing database design diagrams for assignments and exams. The first section had no limits on the number of submissions for both assignments and exams. The second section had limits for the exams but no limits on assignments. On the midterm, participants were randomly assigned to have either a restriction on the total number of submissions or unlimited submissions but with regression penalties if a graded answer was lower than a previous submission. On the final exam, students were given the option of selecting their submission policy. Student academic performance and submission profiles were compared between the course sections and the different submission policies. Findings: Unrestricted use of automatic grading systems results in high occurrence of undesirable student behavior including trial-and-error guessing and reduced time between submissions without sufficient independent thought. Both submission policies of limiting maximum submissions and utilizing regression penalties significantly reduce these behaviors by up to 85%. Overall, students prefer maximum submission limits, and demonstrate improved behavior and educational outcomes. Conclusions: Automated assessment systems when used for larger problems related to design and programming have benefits when deployed with submission restrictions (maximum attempts or regression penalty) for both improved student learning behaviors and to reduce the computational costs for the system. This is especially important for summative assessment but reasonable limits for formative assessments are also valuable.

Our study examines written corrective feedback generated by two online grammar checkers (GCs), Grammarly and Virtual Writing Tutor, and by the grammar checking function of Microsoft Word. We tested the technology on a wide range of grammatical error types from two sources: a set of authentic ESL compositions and a series of simple sentences we generated ourselves. The GCs were evaluated in terms of (1) coverage (number of errors flagged), (2) appropriacy of proposed replacement forms, and (3) rates of "false alarms" (forms mistakenly flagged as incorrect). Although Grammarly and Virtual Writing Tutor outperformed Microsoft Word, neither of the online GCs had high rates of overall coverage (<50%). Consequently, they cannot be relied on to supply comprehensive feedback on student compositions. The finding of higher identification rates for errors from simple rather than authentic sentences reinforces this conclusion. Nonetheless, since few inaccurate replacement forms and false alarms were observed, only rarely is the feedback actively misleading. In addition, the GCs were better at handling some error types than others. Ultimately, we suggest that teachers use GCs with specially designed classroom activities that target selected error types before learners apply the technology to their own writing.

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas. [ABSTRACT FROM AUTHOR]

Evidence for approach bias tendencies to underly automatic behavioural impulses towards seeking out gambling activities in the presence of appetitive salient cues was first shown by Boffo et al. (2018) in a Dutch sample. Relative to non-problem gamblers, moderate-to-high-risk gamblers demonstrated stronger approach tendencies towards gambling-related stimuli compared with neutral ones. Moreover, gambling approach bias was associated with past-month gambling behaviour and predictive of gambling activity persistence over time. The current study aimed to replicate these findings within a Canadian sample evaluating the concurrent and longitudinal correlates of gambling approach bias. The study was conducted online, available throughout Canada. Twenty-seven non-treatment-seeking moderate-to-high-risk gamblers and 26 non-problem gamblers community-recruited via multiple channels (i.e., internet and newspaper advertisements, land-based flyers, and university recruitment portals). Participants completed two online assessment sessions 6-months apart. Each session included (1) self-report measures of gambling behaviour (frequency, duration, and expenditure), (2) self-report assessment of problem gambling severity (PGSI), and (3) a gambling approach-avoidance task, utilising culturally relevant stimuli tailored to individual gambling habits. However, our study failed to replicate Boffo et al. (2018) findings in a Canadian sample. Relative to non-problem gamblers, moderate-to-high-risk gamblers did not exhibit greater approach bias tendencies towards gambling-related stimuli compared to neutral stimuli. Moreover, gambling approach bias was not predictive of prospective gambling behaviour (frequency, duration, or expenditure) or severity of gambling problems. Reported results do not provide evidence for approach tendencies contributing to problematic gambling behaviour in a Canadian sample of moderate-to-high-risk gamblers compared to non-problematic gambler controls. Further replications on the topic are needed. Future research should evaluate approach tendencies within the gambling context, considering the potential impact of task reliability to assess approach bias in light of individual gambling modality preferences., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98 [ABSTRACT FROM AUTHOR]

Rapid mapping of near-shore and coastal regions has become an indispensable task for the local authority to serve the purpose of coastal management and post-disaster monitoring. Aerial photogrammetry and satellite remote sensing have been utilized to fulfill such a task in the last few decades. Airborne LiDAR can further compensate the drawbacks of these image capturing approaches as a result of the direct geo-referenced 3D point cloud. The recent introduction of multispectral airborne LiDAR, such as the Teledyne Optech Titan, can potentially enhance the capability of water mapping, minimize the involvement of manual intervention and reduce the use of supplementary information or ancillary data. This study demonstrates the use of multispectral airborne LiDAR data for automatic land-water classification under different coastal and inland river environments. Two automatic training data selection methods are proposed. The first method utilizes Gaussian mixture model (GMM) to split preliminarily the land and water region based on the elevation/intensity histogram, and the second method is developed based on the use of scan line intensity-elevation ratio (SLIER). Subsequently, various LiDAR-derived feature sets, particularly based on the multispectral LiDAR intensity, are constructed in order to serve as an input for the log-likelihood classification model. Two optional post-classification enhancements can be implemented to further adjust the misclassified data points. The proposed workflow was evaluated with four Optech Titan datasets collected for different near-shore and river environments that are located nearby Lake Ontario, Ontario, Canada. Our experimental work demonstrated that the multispectral LiDAR intensity data was capable of enhancing the classification capability, where an overall accuracy better than 96% was achieved in most of the cases. [ABSTRACT FROM AUTHOR]

Double modals are a well-known nonstandard feature of some regional varieties of English in North America, but due to their rareness in spoken language, questions remain as to the inventory of possible combinatorial types and the geographic extent of their use in contemporary naturalistic speech. This study investigates double modals in the Corpus of North American Spoken English (CoNASE), a 1.2-billion-word corpus of time-stamped and geolocated automatic speech recognition (ASR) YouTube transcripts from the United States and Canada. Double modal sequences were identified in the corpus using regular expressions, then verified via manual examination of videos. The study represents the first large-scale, continent-wide analysis of double modals based entirely on recent naturalistic production data, rather than data such as elicited responses or sentence acceptability judgments, and it demonstrates a larger double modal inventory and a broader geographic range of use for the feature than has previously been documented, including in Canada. [ABSTRACT FROM AUTHOR]

The automatic classification of marine species based on images is a challenging task for which multiple solutions have been increasingly provided in the past two decades. Oceans are complex ecosystems, difficult to access, and often the images obtained are of low quality. In such cases, animal classification becomes tedious. Therefore, it is often necessary to apply enhancement or pre-processing techniques to the images, before applying classification algorithms. In this work, we propose an image enhancement and classification pipeline that allows automated processing of images from benthic moving platforms. Deep-sea (870 m depth) fauna was targeted in footage taken by the crawler "Wally" (an Internet Operated Vehicle), within the Ocean Network Canada (ONC) area of Barkley Canyon (Vancouver, BC; Canada). The image enhancement process consists mainly of a convolutional residual network, capable of generating enhanced images from a set of raw images. The images generated by the trained convolutional residual network obtained high values in metrics for underwater imagery assessment such as UIQM (~ 2.585) and UCIQE (2.406). The highest SSIM and PSNR values were also obtained when compared to the original dataset. The entire process has shown good classification results on an independent test data set, with an accuracy value of 66.44% and an Area Under the ROC Curve (AUROC) value of 82.91%, which were subsequently improved to 79.44% and 88.64% for accuracy and AUROC respectively. These results obtained with the enhanced images are quite promising and superior to those obtained with the non-enhanced datasets, paving the strategy for the on-board real-time processing of crawler imaging, and outperforming those published in previous papers. [ABSTRACT FROM AUTHOR]

BACKGROUND: During the COVID-19 pandemic, a need for innovative, inexpensive, and simple ventilator devices for mass use has emerged. The Oxylator (CPR Medical Devices, Markham, Ontario, Canada) is an FDA-approved, fist-size, portable ventilation device developed for out-of-hospital emergency ventilation. It has not been tested in conditions of severe lung injury or with added PEEP. We aimed to assess the performance and reliability of the device in simulated and experimental conditions of severe lung injury, and to derive monitoring methods to allow the delivery of safe, individualized ventilation during situations of surge. METHODS: We bench-tested the functioning of the device with an added PEEP valve extensively, mimicking adult patients with various respiratory mechanics during controlled ventilation, spontaneous breathing, and prolonged unstable conditions where mechanics or breathing effort was changed at every breath. The device was further tested on a porcine model (4 animals) after inducing lung injury, and these results were compared with conventional ventilation modes. RESULTS: The device was stable and predictable, delivering a constant flow (30 L/min) and cycling automatically at the inspiratory pressure set (minimum of 20 cm H2O) above auto-PEEP. Changes in respiratory mechanics manifested as changes in respiratory timing, allowing prediction of tidal volumes from breathing frequency. Simulating lung injury resulted in relatively low tidal volumes (330 mL with compliance of 20 mL/cm H2O). In the porcine model, arterial oxygenation, CO2, and pH were comparable to conventional modes of ventilation. CONCLUSIONS: The Oxylator is a simple device that delivered stable ventilation with tidal volumes within a clinically acceptable range in bench and porcine lung models with low compliance. External monitoring of respiratory timing is advisable, allowing tidal volume estimation and recognition of changes in respiratory mechanics. The device can be an efficient, low-cost, and practical rescue solution for providing short-term ventilatory support as a temporary bridge, but it requires a caregiver at the bedside. [ABSTRACT FROM AUTHOR]

Automatic item generation represents a relatively new but rapidly evolving research area where cognitive and psychometric theories are used to produce tests that include items generated using computer technology. Automatic item generation requires two steps. First, test development specialists create item models, which are comparable to templates or prototypes, that highlight the features or elements in the assessment task that must be manipulated. Second, these item model elements are manipulated to generate new items with the aid of computer-based algorithms. With this two-step process, hundreds or even thousands of new items can be created from a single item model. The purpose of our article is to describe seven different but related topics that are central to the development and use of item models for automatic item generation. We start by defining item model and highlighting some related concepts; we describe how item models are developed; we present an item model taxonomy; we illustrate how item models can be used for automatic item generation; we outline some benefits of using item models; we introduce the idea of an item model bank; and finally, we demonstrate how statistical procedures can be used to estimate the parameters of the generated items without the need for extensive field or pilot testing. (Contains 4 notes, 2 tables and 4 figures.)

We examined whether activating independent or interdependent self-construal modulates attention shifting in response to group gaze cues. European Canadians (Study 1) and East Asian Canadians (Study 2) primed with independence vs. interdependence completed a multi-gaze cueing task with a central face gazing left or right, flanked by multiple background faces that either matched or mismatched the direction of the foreground gaze. Results showed that European Canadians (Study 1) mostly ignored background gaze cues and were uninfluenced by the self-construal primes. However, East Asian Canadians (Study 2), who have cultural backgrounds relevant to both independence and interdependence, showed different attention patterns by prime: those primed with interdependence were more distracted by mismatched (vs. matched) background gaze cues, whereas there was no change for those primed with independence. These findings suggest activating an interdependent self-construal modulates social attention mechanisms to attend broadly, but only for those who may find these representations meaningful., Competing Interests: The authors have declared that no competing interests exist.

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Segmentation of cardiac MRI images plays a key role in clinical diagnosis. In the traditional diagnostic process, clinical experts manually segment left ventricle (LV), right ventricle (RV) and myocardium (Myo) to get the guideline for cardiopathy diagnosis. However, manual segmentation is time-consuming and labor-intensive. In this paper, we propose automatic cardiac MRI segmentation and cardiopathy classification based on deep neural networks and point clouds. The cardiac MRI segmentation consists of two steps: (i) We use a detector based on you only look once (YOLO) to obtain region of interest (ROI) from the sequential diastolic and systolic MRI. (ii) We obtain the segmentation masks from the ROI automatically by a fully convolutional neural network (FCN). Subsequently, we reconstruct 3D surfaces by a simple linear interpolation method, then randomly sample uniform 3D point clouds from the 3D surfaces. From the cardiac point clouds, we perform cardiopathy classification using a cardiopathy diagnosis network (CDN). Experimental results show that the proposed method successfully segments LV, RV, and Myo from cardiac MRI images and achieves comparable results against several existing ones. Moreover, the CDN successfully classifies heart diseases based on point clouds and achieves 92% accuracy on the testing dataset. [ABSTRACT FROM AUTHOR]

Implementing and sustaining technological innovations in healthcare is a complex process. Commonly, innovations are abandoned due to unsuccessful attempts to sustain and scale-up post implementation. Limited information is available on what characterizes successful e-health innovations and the enabling factors that can lead to their sustainability in complex hospital environments. We present a successful implementation, sustainability and scale-up of a virtual care program consisting of three e-health applications (telemedicine, telehome monitoring, and interactive voice response) in a major cardiac care hospital in Canada. We describe their evolution and adaptation over time, present the innovative approach for their "business case" and funding that supported their implementation, and identify key factors that enabled their sustainability and success, which may inform future research and serve as a benchmark for other health care organizations. Despite resource constraints, e-health innovations can be deployed and successfully sustained in complex healthcare settings contingent key considerations: simplifying technology to make it intuitive for patients; providing significant value proposition that is research supported to influence policy changes; involving early supporters of adoption from administrative and clinical staff; engaging patients throughout the innovation cycle; and partnering with industry/technology providers. [ABSTRACT FROM AUTHOR]

New approaches are required to undertake the substantial task of monitoring ongoing fishing activity in marine conservation areas to ensure conservation goals are achieved. To address this need, we applied previously developed, yet currently underused, vessel tracking methods based on Automatic Identification System (AIS) and aerial surveillance ("flyovers") to Canada's Pacific marine conservation areas from 2012 to 2019. We used satellite and terrestrial‐based AIS receivers and flyover‐based visual observations to estimate illegal and legal fishing activity after 185 conservation area (CA) enactments (i.e., static, geographically defined areas with fishing regulations). We compared the effectiveness in detecting fishing activity between the AIS‐ and flyover‐based methods, and used the latter to determine that 93% of vessels were actively fishing in CAs without AIS. The AIS‐based method still detected 3303 h of fishing in CAs after enactment, and both methods estimated 22%–24% of fishing activity in CAs was illegal. The application of these methods also shed light on the complexity of fishing regulations across CAs (i.e., varying and CA‐specific restrictions). This highlighted the need to better align vessel tracking fishing gear classifications with CA regulation specifications, and conversely to simplify regulations (e.g., no‐take), for more accurate monitoring and evaluation moving forward. [ABSTRACT FROM AUTHOR]

The unconditioned data retrieved from accumulating automated weighing precipitation gauges are inherently noisy due to the sensitivity of the instruments to mechanical and electrical interference. This noise, combined with diurnal oscillations and signal drift from evaporation of the bucket contents, can make accurate precipitation estimates challenging. Relative to rainfall, errors in the measurement of solid precipitation are exacerbated because the lower accumulation rates are more impacted by measurement noise. Precipitation gauge measurement post-processing techniques are used by Environment and Climate Change Canada in research and operational monitoring to filter cumulative precipitation time series derived from high-frequency, bucket-weight measurements. Four techniques are described and tested here: (1) the operational 15 min filter (O15), (2) the neutral aggregating filter (NAF), (3) the supervised neutral aggregating filter (NAF-S), and (4) the segmented neutral aggregating filter (NAF-SEG). Inherent biases and errors in the first two post-processing techniques have revealed the need for a robust automated method to derive an accurate noise-free precipitation time series from the raw bucket-weight measurements. The method must be capable of removing random noise, diurnal oscillations, and evaporative (negative) drift from the raw data. This evaluation primarily focuses on cold-season (October to April) accumulating automated weighing precipitation gauge data at 1 min resolution from two sources: a control (pre-processed time series) with added synthetic noise and drift and raw (minimally processed) data from several WMO Solid Precipitation Intercomparison Experiment (SPICE) sites. Evaluation against the control with synthetic noise shows the effectiveness of the NAF-SEG technique, recovering 99 %, 100 %, and 102 % of the control total precipitation for low-, medium-, and high-noise scenarios respectively for the cold-season (October–April) and 97 % of the control total precipitation for all noise scenarios in the warm season (May–September). Among the filters, the fully automated NAF-SEG produced the highest correlation coefficients and lowest root-mean-square error (RMSE) for all synthetic noise levels, with comparable performance to the supervised and manually intensive NAF-S method. Compared to the O15 method in cold-season testing, NAF-SEG shows a lower bias in 37 of 44 real-world test cases, a similar bias in 5 cases, and a higher bias in 2 cases. In warm-season testing, the NAF-SEG bias was lower or similar in 7 of 11 cases. The results indicate that the NAF-SEG post-processing technique provides substantial improvement over current automated techniques, reducing both uncertainty and bias in accumulating-gauge measurements of precipitation, with a 24 h latency. Because it cannot be implemented in real time, we recommend that NAF-SEG be used in combination with a simple real-time filter, such as the O15 or similar filter. [ABSTRACT FROM AUTHOR]

Sexual and mental health disparities are reported in Arctic Canada as in other Arctic regions that experience shared challenges of insufficient healthcare resources, limited transportation, and a scarcity of healthcare research. Lesbian, gay, bisexual, transgender, and queer persons (LGBTQ+) report sexual and mental health disparities in comparison with their heterosexual and cisgender counterparts, and these disparities may be exacerbated in rural versus urban settings. Yet limited research has explored sexual healthcare experiences among LGBTQ+ persons in the Arctic who are at the juncture of Arctic and LGBTQ+ health disparities. We conducted a qualitative study from May 2015 to October 2015 with LGBTQ+ persons in the Northwest Territories, Canada that involved in-depth individual interviews with LGBTQ+ youth (n = 16), LGBTQ+ adults (n = 21), and key informants (e.g. coaches, teachers, nurses, social workers, and healthcare providers) (n = 14). We conducted thematic analysis, a theoretically flexible approach that integrates deductive and inductive approaches, to identify and map themes in the data. Findings reveal geographical, social, and healthcare factors converge to shape healthcare access. Specifically, the interplay between heterosexism and cisnormativity, intersectional forms of stigma, and place limited LGBTQ+ persons' sexual healthcare access and produced negative experiences in sexual healthcare. Limited healthcare facilities in small communities resulted in confidentiality concerns. Heteronormativity and cisnormativity constrained the ability to access appropriate sexual healthcare. LGBTQ+ persons experienced LGBTQ+, HIV, and sexually transmitted infections stigma in healthcare. Participants also discussed healthcare provider recommendations to better serve LGBTQ+ persons: non-judgment, knowledge of LGBTQ+ health issues, and gender inclusivity. Findings can inform multi-level strategies to reduce intersecting stigma in communities and healthcare, transform healthcare education, and build LGBTQ+ persons' healthcare navigation skills., (© 2019 John Wiley & Sons Ltd.)

Purpose: To evaluate whether interpretive "health" labels placed in vending machines in recreation centres represented products' nutrient content when compared with provincial nutrition guidelines. Methods: A cross-sectional audit (November 2015 - April 2016) of 139 vending machines in recreation facilities found 525 foods and beverages in 17 machines labelled by vendors according to healthfulness. Product nutrient content was compared with provincial nutrition guideline criteria. Cross-tabulation and weighted Cohen's kappa evaluated agreement between vendor interpretive labels and guideline ranks. Descriptive statistics evaluated how mislabelled products deviated from recommended nutrient content. Mann-Whitney tests compared nutrient content of "healthy" and "unhealthy" labelled products. Results: Almost one-third of all products were mislabelled by vendors with 72% of those labelled healthier than their actual guideline rank. Energy, total fat, sugar, and sodium contents exceeded recommended levels in one-third to one-half of mislabelled products. Overall, products labelled healthy by vendors were significantly lower in energy, sodium, and fat compared with those labelled unhealthy; however, not for all food types (e.g., bars, fruit snacks, nuts). Conclusions: For certain product categories, vendor interpretive nutrition labels poorly represented products' nutrient content according to provincial nutrition guidelines. Dietitians may be a valuable resource to help implement nutrition guidelines to create credible interpretive product labelling systems.

Expansion of marine conservation areas (CA) necessitates resource‐efficient and achievable strategies for monitoring and evaluation of ongoing fishing activity at national levels. To demonstrate and explore such a strategy, we conducted the first extensive analysis of fishing activity within Canada's static, geographically defined marine CAs with fishing regulations (n = 264 areas). We used 8 years of Automatic Identification System data to estimate fishing effort across three oceans and conducted temporal and spatial comparisons specific to each CA's regulations and enactment date. We addressed questions on CA effectiveness, fishing displacement, fishing the line behavior, and relationships between fishing activity and spatial CA attributes. We estimated 22,000 h of fishing activity within CAs after enactments, 22% of which was identified as illegal. CA effectiveness appeared to be lowest for Atlantic CAs based on illegal fishing effort density within CAs. Fishing displacement and fishing the line was generally not apparent as buffer areas around CAs tended to already have higher fishing effort prior to enactments. CA effectiveness and responses to CAs varied considerably, as was visualized using timeseries plots and maps developed for each CA. Our evaluation of a nation's full suite of CAs provides managers with a foundation and approach for continued monitoring and reporting. [ABSTRACT FROM AUTHOR]

The unconditioned data retrieved from automated accumulating precipitation gauges is inherently noisy due to the sensitivity of the instruments to mechanical and electrical interference. This noise, combined with diurnal oscillations and signal drift from evaporation of the bucket contents, can make accurate precipitation estimates challenging. Relative to rainfall, errors in the measurement of solid precipitation are exacerbated because the lower accumulation rates are more impacted by measurement noise. Precipitation gauge measurement post-processing techniques are used by Environment and Climate Change Canada in research and operational monitoring to filter cumulative precipitation time series derived from high-frequency, bucket-weight measurements. Four techniques are described and tested here: 1) the operational 15-minute filter (O15), 2) the Neutral Aggregating Filter (NAF), 3) the Supervised Neutral Aggregating Filter (NAF-S), and 4) the Segmented Neutral Aggregating Filter (NAF-SEG). Inherent biases and errors in the first two post-processing techniques have revealed the need for a robust automated method to derive an accurate noise-free precipitation time series from the raw bucket-weight measurements. The method must be capable of removing random noise, diurnal oscillations, and evaporative (negative) drift from the raw data. This evaluation focuses on cold-season (October to April) accumulating-precipitation-gauge data at 1-min resolution from two sources: a control (pre-processed time series) with added synthetic noise and drift; and raw (minimally-processed) data from several WMO Solid Precipitation Inter-Comparison Experiment (SPICE) sites. Evaluation against the control with synthetic noise shows the effectiveness of the NAF-SEG technique, recovering 99%, 100%, and 102% of the control total precipitation for low, medium, and high noise scenarios respectively. Among the filters, the fully-automated NAF-SEG produced the highest correlation coefficients and lowest RMSE for all synthetic noise levels, with comparable performance to the supervised and manually-intensive NAF-S method. Compared to the operational O15 method, NAF-SEG shows a lower bias in 37 of 44 real-world test cases, a similar bias in 5 cases, and a higher bias in 2 cases. The results indicate that the NAF-SEG post-processing technique provides substantial improvement over current automated techniques, reducing both uncertainty and bias in accumulating-gauge measurements of precipitation, with a 24-hour latency. Because it cannot be implemented in real time, we recommend that NAF-SEG be used in consort with a simple real-time filter, such as the operational O15 or similar filter. [ABSTRACT FROM AUTHOR]

Automatic milking systems (AMS), or milking robots, are becoming widely accepted as a milking technology that reduces labour and increases milk yield. However, reported amount of labour saved, changes in milk yield, and milk quality when transitioning to AMS vary widely. The purpose of this study was to document the impact of adopting AMS on farms with regards to reported changes in milking labour management, milk production, milk quality, and participation in dairy herd improvement (DHI) programmes. A survey was conducted across Canada over the phone, online, and in-person. In total, 530 AMS farms were contacted between May 2014 and the end of June 2015. A total of 217 AMS producers participated in the General Survey (Part 1), resulting in a 41% response rate, and 69 of the respondents completed the more detailed follow-up questions (Part 2). On average, after adopting AMS, the number of employees (full- and part-time non-family labour combined) decreased from 2.5 to 2.0, whereas time devoted to milking-related activities decreased by 62% (from 5.2 to 2.0 h/day). Median milking frequency was 3.0 milkings/day and robots were occupied on average 77% of the day. Producers went to fetch cows a median of 2 times/day, with a median of 3 fetch cows or 4% of the herd per robot/day. Farms had a median of 2.5 failed or incomplete milkings/robot per day. Producers reported an increase in milk yield, but little effect on milk quality. Mean milk yield on AMS farms was 32.6 kg/cow day. Median bulk tank somatic cell count was 180 000 cells/ml. Median milk fat on AMS farms was 4.0% and median milk protein was 3.3%. At the time of the survey, 67% of producers were current participants of a DHI programme. Half of the producers who were not DHI participants had stopped participation after adopting AMS. Overall, this study characterized impacts of adopting AMS and may be a useful guide for making this transition.

Despite the growing popularity of automatic milking systems (AMS), or milking robots, in Canada, little documentation is available on how Canadian dairy producers experience the transition to this milking technology. The objective of this national study was to document the experiences of Canadian dairy producers during the transition to, and use of, AMS. This paper reports on producers' experiences with cow training, challenges during the transition and their solutions, and effect of the AMS on quality of life. The AMS producers (n = 217) were surveyed from 8 Canadian provinces. Overall, producers experienced a positive transition to AMS. Producers perceived that AMS improved profitability, quality of their lives and their cows' lives, and had met expectations, despite experiencing some challenges during transition such as learning to use the technology and data, cow training, demanding first few days, and changing health management. Less than half of the AMS producers (42%) trained cows or heifers to use the AMS before the first milking with the robot. Producers who implemented training before first milking reported that it took an average of 1 wk to train a cow or heifer to use the AMS. Producers reported it took a median of 30 d for an entire herd to adapt to the AMS, whether or not cow training took place. On average, 2% of a herd was culled for not adapting, or not voluntarily milking, when otherwise physically and behaviorally normal. With AMS, producers suggested they gained more time flexibility, found work to be less stressful and physically demanding, found employee management easier, and had improved herd health and management. The vast majority (86%) of producers would recommend others to transition to AMS., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

This paper investigates the performance of a spatio-spectral template on Ikonos imagery to automatically detect small recreational boats. The spatio-spectral template is employed which is then enhanced through use of a weighted Euclidean distance metric adapted from the Mahalanobis distance metric. The purpose is to assist the Canadian Coast Guard in gathering data on recreational boating for the modeling of search and rescue incidence risk. To test the detection accuracy of the enhanced spatio- spectral template, a dataset was created by gathering position and attribute data for 53 recreation vessel targets purposely moored for this research within Cadboro Bay, British Columbia, Canada. The Cadboro Bay study site containing the targets was imaged using Ikonos. The overall detection accuracy was 77 percent. The targets were broken down into two categories: Category A-less than 6 m in length, and Category B-greater than 6 m long. The detection rate for the Category B targets was 100 per- cent, while the detection rate for the Category A targets was 61 percent. It is important to note that some Category A targets were purposely selected for their small size to specifically test the detection limits of the enhanced spatio-spectral template. The smallest target detected was 2.2 meters long and 1.1 meters wide. The analysis also revealed that the ability to detect targets between 2.2 meters and 6 meters long was diminished if the target was a dark color. [ABSTRACT FROM AUTHOR]

Automatic milking systems (AMS), or milking robots, are becoming increasingly common, but there is little documentation of how AMS have affected farms as a whole and what challenges and benefits producers are experiencing during their transition to AMS. The objective of this national survey was to document the effect of transitioning to AMS on producer perceptions of change in housing, farm management, and cow health. In total, 217 AMS producers were surveyed from 8 Canadian provinces. Median time since transition for respondents was 30 mo. The mean number of lactating cows per robot was 51 cows, with a median of 2 AMS units per farm. Fifty-five percent of producers built a new barn to accommodate the AMS. Changing housing systems was necessary for 47% of producers, not necessary for 50%, and not applicable to 3% (as the AMS farm was their first farm). Cleaning and feeding practices remained the same. Overall, farms increased herd size from a median of 77 to 85 lactating cows with the transition to AMS. After the transition to AMS, 66% of producers changed their health-management practices. Producers reported either a decrease or no change in rate of clinical mastitis. Reports on change in rate of lameness and total bacterial count varied. Conception rate was reported to have increased for 63% of producers. Culling rate was perceived to have stayed the same for 59% of producers. Overall, producers perceived their transitions to AMS as successful. Findings from this project provide a benchmark of the effects of AMS on important aspects of Canadian dairy farming, as well as provide producers, AMS manufacturers, veterinarians, and dairy advisors with more detailed knowledge on what to expect when transitioning to AMS. [ABSTRACT FROM AUTHOR]

Glacier mass balance models are needed at sites with scarce long-term observations to reconstruct past glacier mass balance and assess its sensitivity to future climate change. In this study, North American Regional Reanalysis (NARR) data were used to force a physically based, distributed glacier mass balance model of Saskatchewan Glacier for the historical period 1979–2016 and assess its sensitivity to climate change. A 2-year record (2014–2016) from an on-glacier automatic weather station (AWS) and historical precipitation records from nearby permanent weather stations were used to downscale air temperature, relative humidity, wind speed, incoming solar radiation and precipitation from the NARR to the station sites. The model was run with fixed (1979, 2010) and time-varying (dynamic) geometry using a multitemporal digital elevation model dataset. The model showed a good performance against recent (2012–2016) direct glaciological mass balance observations as well as with cumulative geodetic mass balance estimates. The simulated mass balance was not very sensitive to the NARR spatial interpolation method, as long as station data were used for bias correction. The simulated mass balance was however sensitive to the biases in NARR precipitation and air temperature, as well as to the prescribed precipitation lapse rate and ice aerodynamic roughness lengths, showing the importance of constraining these two parameters with ancillary data. The glacier-wide simulated energy balance regime showed a large contribution (57 %) of turbulent (sensible and latent) heat fluxes to melting in summer, higher than typical mid-latitude glaciers in continental climates, which reflects the local humid "icefield weather" of the Columbia Icefield. The static mass balance sensitivity to climate was assessed for prescribed changes in regional mean air temperature between 0 and 7 ∘ C and precipitation between -20 % and + 20 %, which comprise the spread of ensemble Representative Concentration Pathway (RCP) climate scenarios for the mid (2041–2070) and late (2071–2100) 21st century. The climate sensitivity experiments showed that future changes in precipitation would have a small impact on glacier mass balance, while the temperature sensitivity increases with warming, from -0.65 to -0.93 m w.e. a -1 ∘ C -1. The mass balance response to warming was driven by a positive albedo feedback (44 %), followed by direct atmospheric warming impacts (24 %), a positive air humidity feedback (22 %) and a positive precipitation phase feedback (10 %). Our study underlines the key role of albedo and air humidity in modulating the response of winter-accumulation type mountain glaciers and upland icefield-outlet glacier settings to climate. [ABSTRACT FROM AUTHOR]

Objectives: Bystander resuscitation efforts, such as cardio-pulmonary resuscitation (CPR) and use of an automatic external defibrillator (AED), save lives in cardiac arrest cases. School training in CPR and AED use may increase the currently low community rates of bystander resuscitation. The study objective was to determine the rates of CPR and AED training in Toronto secondary schools and to identify barriers to training and training techniques. Methods: This prospective study consisted of telephone interviews conducted with key school staff knowledgeable about CPR and AED teaching. An encrypted Web-based tool with prespecified variables and built-in logic was employed to standardize data collection. Results: Of 268 schools contacted, 93% were available for interview and 83% consented to participate. Students and staff were trained in CPR in 51% and 80% of schools, respectively. Private schools had the lowest training rate (39%). Six percent of schools provided AED training to students and 47% provided AED training to staff. Forty-eight percent of schools had at least one AED installed, but 25% were unaware if their AED was registered with emergency services dispatch. Cost (17%), perceived need (11%), and school population size (10%) were common barriers to student training. Frequently employed training techniques were interactive (32%), didactic instruction (30%) and printed material (16%). Conclusions: CPR training rates for staff and students were moderate overall and lowest in private schools, whereas training rates in AED use were poor in all schools. Identified barriers to training include cost and student population size (perceived to be too small to be cost-effective or too large to be implemented). Future studies should assess the application of convenient and cost effective teaching alternatives not presently in use. [ABSTRACT FROM AUTHOR]

The city of Abha, in south west Saudi Arabia, has a new reservoir on top of a hill which releases water down to a main reservoir 6 km away. Problems with dangerous pressure differentials threatened to disrupt the water supply until a Canadian manufacturer of automatic control valves came up with a solution. [ABSTRACT FROM AUTHOR]

The school bus is a major transportation mode for students in Canada. Unexpected delay of a school bus may be a major source of inconvenience for students and their parents. Accordingly, the provision of timely and reliable information on the expected arrivals of school buses would be of great benefit to them. This study develops an expected time of arrival (ETA) model for school buses. The model predicts arrival time from the input of two categories: the last several days' historical data and the current day's operational conditions. An operational strategy is additionally incorporated into the model to reduce the risk that an overestimated arrival time can result in missing the bus. This study evaluates the model using data collected from real-world operations of school buses on which a global positioning system-based automatic vehicle location (AVL) system is installed. The proposed model consistently shows lower levels of prediction error than moving average and regression approaches. With the operational strategy, the model provides a sufficiently reliable service in which approximately 99%-100% of students do not miss the bus, with the tolerable wait time of 162-177. [ABSTRACT FROM AUTHOR]

Background: We have previously developed a novel digital tool capable of automatically recognizing correct electrocardiography (ECG) diagnoses in an online exam and demonstrated a significant improvement in diagnostic accuracy when utilizing an inductive-deductive reasoning strategy over a pattern recognition strategy. In this study, we sought to validate these findings from participants at the International Winter Arrhythmia School meeting, one of the foremost electrophysiology events in Canada., Methods: Preregistration to the event was sent by e-mail. The exam was administered on day 1 of the conference. Results and analysis were presented the following morning to participants., Results: Twenty-five attendees completed the exam, providing a total of 500 responses to be marked. The online tool accurately identified 195 of a total of 395 (49%) correct responses (49%). In total, 305 responses required secondary manual review, of which 200 were added to the correct responses pool. The overall accuracy of correct ECG diagnosis for all participants was 69% and 84% when using pattern recognition or inductive-deductive strategies, respectively., Conclusion: Utilization of a novel digital tool to evaluate ECG competency can be set up as a workshop at international meetings or educational events. Results can be presented during the sessions to ensure immediate feedback., (© 2015 Wiley Periodicals, Inc.)

The extraction of oceanic wave spectrum information from radar data has been a challenging problem that has been the subject of a vast amount of research over the past several decades. This research has resulted in a multitude of approaches to extract ocean wave spectra from Doppler spectrum returns. One common feature of many of these methods is the reduction of the wave spectrum extraction problem from a nonlinear problem to a linear one. In this article, a new approach is introduced, which does not linearize the Fredholm integral equation relating the ocean wave spectrum to the radar Doppler spectrum, but instead maintains its nonlinear nature. Also, unlike previous nonlinear optimization solutions, the proposed method is automatic in the sense that no regularization parameters have to be manually set purely dependent on the radar data from which the ocean wave parameters are being extracted, thereby reducing the need for human intervention in the wave spectrum extraction process. In addition to describing this new method for wave spectrum extraction, this article presents results from a case study on field data from Argentia, NL, Canada, comparing the oceanographic parameters obtained with the proposed method to those recorded by in situ buoy instrumentation. The significant wave height calculated from ocean wave spectra extracted via the method are found to match with those from the buoy, whereas the values of other oceanographic parameters, such as wave period and direction extracted using the proposed method, are less accurate potentially due to the low quality of data available to test the method. [ABSTRACT FROM AUTHOR]

Human listening tests were conducted to investigate if participants could distinguish between samples of target echoes and clutter obtained from a broadband active sonar experiment. For each echo, the listeners assigned a rating based on how confident they were that it was a target echo or clutter. The measure of performance was the area under the binormal receiver-operating-characteristic (ROC) curve, Az. The mean performance was Az = 0.95 ± 0.04 when signals were presented with their full available acoustic bandwidth of approximately 0-2 kHz. It was Az = 0.77 ± 0.08 when the bandwidth was reduced to 0.5-2 kHz. The error bounds are stated as 95% confidence intervals. These results show that the listeners could definitely hear differences, but their performance was significantly degraded when the low-frequency signal information was removed. The performance of an automatic aural classifier was compared against this human-performance baseline. Results of statistical tests showed that it outperformed 2 of 13 listeners and 5 of 9 human listeners in the full-bandwidth and reduced-bandwidth tests, respectively, and performed similarly to the other listeners. Given its performance, the automatic aural classifier may prove beneficial to Navy sonar systems. [ABSTRACT FROM AUTHOR]

There are limited data regarding the effect of age on the risk of ventricular tachyarrhythmias (VTAs). The present study was designed to compare the risk for VTAs in young and older patients with left bundle branch block (LBBB) and mildly symptomatic heart failure who receive device therapy. The risk of the first ventricular tachycardia (VT) or ventricular fibrillation (VF) event and the risk of first appropriate implantable cardioverter defibrillator (ICD) shock was compared between young (<75 years, n = 1,037) and older (≥75 years, n = 227) patients with LBBB enrolled in Multicenter Automatic Implantation Trial with Cardiac Resynchronization Therapy. The cumulative incidence of a first VTA through 2 years of follow-up was significantly lower in older patients than in younger patients. Multivariate analysis showed that older patients experienced a significantly lower risk of VT/VF (hazard ratio 0.38, 95% confidence interval 0.22 to 0.64, p <0.001) and a significantly lower risk of appropriate ICD shocks (hazard ratio 0.37, 95% confidence interval 0.17 to 0.82, p = 0.014) compared with younger patients. Each increasing decade of life was associated with a 19% (p = 0.002) and 22% (p = 0.018) reduction in the risk of VT/VF and appropriate ICD shocks, respectively. The lower risk of VT/VF and appropriate ICD shocks in older patients was evident in patients implanted with an ICD only and in those implanted with a cardiac resynchronization therapy with defibrillator. In conclusion, in patients with LBBB and mild symptoms of heart failure, aging is associated with a significant decrease in the incidence of VT/VF and ICD shocks., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Background: The benefit of novel implantable cardioverter defibrillator (ICD) programming in reducing inappropriate ICD therapy and mortality was demonstrated in Multicenter Automatic Defibrillator Implantation Trial-Reduce Inappropriate Therapy (MADIT-RIT). However, the cause of mortality reduction remains incompletely evaluated. We aimed to identify factors associated with mortality, with focus on ICD therapy and programming in the MADIT-RIT population., Methods and Results: In MADIT-RIT, 1500 patients with a primary prophylactic indication for ICD or cardiac resynchronization therapy with defibrillator were randomized to 1 of 3 different ICD programming arms: conventional programming (ventricular tachycardia zone ≥170 beats per minute), high-rate programming (ventricular tachycardia zone ≥200 beats per minute), and delayed programming (60-second delay before therapy ≥170 beats per minute). Multivariate Cox models were used to assess the influence of time-dependent appropriate and inappropriate ICD therapy (shock and antitachycardia pacing) and randomized programming arm on all-cause mortality. During an average follow-up of 1.4±0.6 years, 71 of 1500 (5%) patients died: cardiac in 40 patients (56.3%), noncardiac in 23 patients (32.4%), and unknown in 8 patients (11.3%). Appropriate shocks (hazard ratio, 6.32; 95% confidence interval, 3.13-12.75; P<0.001) and inappropriate therapy (hazard ratio, 2.61; 95% confidence interval, 1.28-5.31; P=0.01) were significantly associated with an increased mortality risk. There was no evidence of increased mortality risk in patients who experienced appropriate antitachycardia pacing only (hazard ratio, 1.02; 95% confidence interval, 0.36-2.88; P=0.98). Randomization to conventional programming was identified as an independent predictor of death when compared with patients randomized to high-rate programming (hazard ratio, 2.0; 95% confidence interval, 1.06-3.71; P=0.03)., Conclusions: In MADIT-RIT, appropriate shocks, inappropriate ICD therapy, and randomization to conventional ICD programming were independently associated with an increased mortality risk. Appropriate antitachycardia pacing was not related to an adverse outcome., Clinical Trial Registration Url: clinicaltrials.gov Unique identifier: NCT00947310., (© 2014 American Heart Association, Inc.)

Heart failure (HF) is the leading cause of cardiovascular morbidity and health care utilization globally. Much of the cost for HF is related to hospitalization, strategies to decrease cost need to focus on avoiding unnecessary hospital readmission. Interactive voice response (IVR) is an automated telephony system that leverages existing telephone lines to monitor patients post-discharge, for early intervention. This study explores the pattern of IVR use by HF patients in the IVR program at the University of Ottawa Heart Institute (UOHI) and assesses IVR use by patients in relation to symptoms, compliance behavior, lifestyle, and hospital readmission. A total of 902 HF patients were considered; the mean age was 70 years, and 59.4% were male. Over a 12-week period of IVR use, there was an overall increase in medication adherence and a decrease in symptoms occurrence, weight gain and readmission rate. The highest and lowest compliance rates were associated with medication adherence and exercise, respectively. Overall, older, female patients from rural/community hospitals were more likely to complete the IVR calls, have less symptoms occurrence, comply with medications, weight, and lifestyle recommendations. The findings suggest that IVR system use can have a positive impact on HF patients' management. The increased use of IVR in remote patient monitoring will allow for a cheaper and more accessible form of home monitoring. Leveraging IVR technology to support other conditions, especially during a pandemic, may be beneficial for patients to avoid unnecessary visits to the hospital and complications due to delay in seeking care. [ABSTRACT FROM AUTHOR]

This paper presents a two-phased research project aiming to improve email triage for public administration managers. The first phase developed a typology of email classification patterns through a qualitative study involving 34 participants. Inspired by the fields of pragmatics and speech act theory, this typology comprising four top level categories and 13 subcategories represents the typical email triage behaviors of managers in an organizational context. The second study phase was conducted on a corpus of 1,703 messages using email samples of two managers. Using the k- NN ( k-nearest neighbor) algorithm, statistical treatments automatically classified the email according to lexical and nonlexical features representative of managers' triage patterns. The automatic classification of email according to the lexicon of the messages was found to be substantially more efficient when k = 2 and n = 2,000. For four categories, the average recall rate was 94.32%, the average precision rate was 94.50%, and the accuracy rate was 94.54%. For 13 categories, the average recall rate was 91.09%, the average precision rate was 84.18%, and the accuracy rate was 88.70%. It appears that a message's nonlexical features are also deeply influenced by email pragmatics. Features related to the recipient and the sender were the most relevant for characterizing email. [ABSTRACT FROM AUTHOR]

Video log images are often used by transportation agencies to manually or automatically extract roadway infrastructure information, including roadway geometry, signs, etc. Poor-quality images, especially those having illumination-related deficiencies caused by color corruption with a plain-like grayscale histogram, sun glare, or darkness problems, are unacceptable and need to be identified. Manually reviewing the tens of millions of video log images for quality control is labor intensive and time-consuming, so there is a need to develop automatic video log image quality control procedures. The contribution of this article is that it formulates a new problem of roadway video log image quality control and then proposes a reasonable solution to address this problem in the hope that it will motivate the development of new algorithms by other researchers. For the first time, an algorithm using a Histogram Equity Index (HEI) and an adaptive Gaussian Mixture Model is proposed to address the video log image quality issue by automatically detecting illumination-related deficiencies. The Alberta Department of Transportation provided 15,489 video log images to test the proposed algorithm. Test results show that the developed algorithm can detect illumination-related video log image deficiencies with a false positive rate of 4%, 3%, and 12%; a false negative rate of 15%, 17%, and 19% for plain-like color corruption, dark, and sun glare conditions, respectively; computation time is 0.1 second/image. The proposed algorithm could potentially be used to improve video log image data quality control. [ABSTRACT FROM AUTHOR]

This article presents the arguments of the Women's Legal Education and Action Fund on the issue of retroactive child support. It argues that the systemic inequality experienced by women both prior to and after separation must not be exacerbated by an interpretation of the legislative child support regime that fails to recognize the feminization of poverty associated with the caregiving of children. By considering the family law case of D.B.S. v. S.R.G. and the legislative history of the child support regime, the authors contend that the Alberta Court of Appeal's progressive approach regarding the automatic calculation of child support best supports women's equality. The court of appeal's approach in D.B.S. is grounded in the principles of statutory interpretation, is in accordance with the equality guarantee of the Canadian Charter of Rights and Freedoms, and complies with international human rights obligations. The D.B.S. approach is contrasted with another line of reasoning in the jurisprudence that is more restrictive in its recalculation of child support and, accordingly, contributes to women's inequality. The article concludes by endorsing the D.B.S. approach, acknowledging the perils of reprivatization and critiquing the Supreme Court of Canada's recent decision in this case. [ABSTRACT FROM AUTHOR]

Experimental research has shown that beaked whales exhibit strong avoidance reactions to naval active sonars used during antisubmarine warfare training exercises, including cessation of echolocation and foraging activity. Behavioural responses to sonar have also been linked to strandings and mortality. Much of the research on the responses of beaked whales and other cetaceans to naval active sonar has occurred on or near U.S. naval training ranges, and the impacts of sonar in other regions remain poorly understood, particularly as these impacts, including mortality, are likely to go unobserved in offshore areas. In September 2016 the multinational naval exercise 'CUTLASS FURY 2016' (CF16) was conducted off eastern Canada. We used passive acoustic recordings collected in the region to quantify the occurrence and characteristics of sonar signals, measure ambient noise levels, and assess changes in the acoustic activity of beaked and sperm whales. The number of hours per day with echolocation clicks from Cuvier's beaked whales and sperm whales were significantly reduced during CF16, compared to the pre-exercise period in 2016 (sperm whales) and to control data from 2015 (both species). Clicks from an unidentified Mesoplodont beaked whale species, sporadically detected prior to CF16, were absent during the exercise and for 7 days afterward. These results suggest that beaked and sperm whales ceased foraging in the vicinity of CF16 and likely avoided the affected area. Such disturbance may have energetic, health, and fitness consequences. [ABSTRACT FROM AUTHOR]

Background: Use of the serum creatinine concentration, the most widely used marker of kidney function, has been associated with under-reporting of chronic kidney disease and late referral to nephrologists, especially among women and elderly people. To improve appropriateness of referrals, automatic reporting of the estimated glomerular filtration rate (eGFR) by laboratories was introduced in the province of Ontario, Canada, in March 2006. We hypothesized that such reporting, along with an ad hoc educational component for primary care physicians, would increase the number of appropriate referrals., Methods: We conducted a population-based before-after study with interrupted time-series analysis at a tertiary care centre. All referrals to nephrologists received at the centre during the year before and the year after automatic reporting of the eGFR was introduced were eligible for inclusion. We used regression analysis with autoregressive errors to evaluate whether such reporting by laboratories, along with ad hoc educational activities for primary care physicians, had an impact on the number and appropriateness of referrals to nephrologists., Results: A total of 2672 patients were included in the study. In the year after automatic reporting began, the number of referrals from primary care physicians increased by 80.6% (95% confidence interval [CI] 74.8% to 86.9%). The number of appropriate referrals increased by 43.2% (95% CI 38.0% to 48.2%). There was no significant change in the proportion of appropriate referrals between the two periods (-2.8%, 95% CI -26.4% to 43.4%). The proportion of elderly and female patients who were referred increased after reporting was introduced., Interpretation: The total number of referrals increased after automatic reporting of the eGFR began, especially among women and elderly people. The number of appropriate referrals also increased, but the proportion of appropriate referrals did not change significantly. Future research should be directed to understanding the reasons for inappropriate referral and to develop novel interventions for improving the referral process.

Objectives: The purpose of this study was to assess the effectiveness of contemporary automatic external defibrillator (AED) use., Background: In the PAD (Public Access Defibrillation) trial, survival was doubled by focused training of lay volunteers to use an AED in high-risk public settings., Methods: We performed a population-based cohort study of persons with nontraumatic out-of-hospital cardiac arrest before emergency medical system (EMS) arrival at Resuscitation Outcomes Consortium (ROC) sites between December 2005 and May 2007. Multiple logistic regression was used to assess the independent association between AED application and survival to hospital discharge., Results: Of 13,769 out-of-hospital cardiac arrests, 4,403 (32.0%) received bystander cardiopulmonary resuscitation but had no AED applied before EMS arrival, and 289 (2.1%) had an AED applied before EMS arrival. The AED was applied by health care workers (32%), lay volunteers (35%), police (26%), or unknown (7%). Overall survival to hospital discharge was 7%. Survival was 9% (382 of 4,403) with bystander cardiopulmonary resuscitation but no AED, 24% (69 of 289) with AED application, and 38% (64 of 170) with AED shock delivered. In multivariable analyses adjusting for: 1) age and sex; 2) bystander cardiopulmonary resuscitation performed; 3) location of arrest (public or private); 4) EMS response interval; 5) arrest witnessed; 6) initial shockable or not shockable rhythm; and 7) study site, AED application was associated with greater likelihood of survival (odds ratio: 1.75; 95% confidence interval: 1.23 to 2.50; p < 0.002). Extrapolating this greater survival from the ROC EMS population base (21 million) to the population of the U.S. and Canada (330 million), AED application by bystanders seems to save 474 lives/year., Conclusions: Application of an AED in communities is associated with nearly a doubling of survival after out-of-hospital cardiac arrest. These results reinforce the importance of strategically expanding community-based AED programs., (Copyright (c) 2010 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Syndemics (socially produced intertwined epidemics) of psychosocial issues have been shown empirically to increase the risk of HIV infection among gay, bisexual and other men who have sex with men (GBM). However, to intervene effectively identifying those who are most vulnerable is critically important. Using an intersectionality framework, this study analyzed data drawn from 8490 Canadian GBM respondents to an online survey to evaluate demographic factors associated with experiencing a syndemic. A multivariable regression model was used to identify demographic characteristics among those experiencing a syndemic of two or more issues. Then, applying an intersectionality framework, the sample was stratified by gay and bisexual men, and by men partnered with woman (whether they were gay, bisexual or other), and the multivariate analyses for demographic variables were repeated for each strata. Multivariate regression models found that men were more at risk of syndemics if they identified as gay, were single, young (< 30-years-old), did not have a university degree and were low-income earners (< $60,000 per year). On stratified analysis, variations were found between gay, bisexual and female-partnered men where gay men were at greater odds of reporting a syndemic. These results provide further evidence that syndemics of psychosocial issues increase the risk of HIV infection among GBM. More so, the occurrence of syndemics was dependent on every single variable investigated including ethnicity and income. As such, interventions to reduce the effects of syndemics should be targeted to those most at risk and address social and structural inequities. [ABSTRACT FROM AUTHOR]

Background. Mobility limitations represent the third most prevalent cause of disability, affecting more than 1.9 million community-dwelling Canadians. Walking aids are often prescribed to reduce the impacts of mobility impairments. There are limited data on walking aids since 2004. Objective. The objectives of this study were to investigate the prevalence of walking aid use in Canada and to explore demographic characteristics among users of walking aids. Design. The design used was a secondary analysis of a cross-sectional national survey. Methods. Data were obtained from the 2012 Canadian Survey on Disability from community- dwelling individuals who were 15 years old or older, had a self-identified activity limitation, and indicated that they used at least 1 walking aid (cane/walking stick/crutches or walker). Prevalence estimates were calculated as weighted frequencies. Analytic variables included walking aid type, sex, age, province/territory of residence, and main cause of activity limitation. Results. Approximately 1,125,000 community-dwelling individuals who were 15 years old or older used walking aids, representing 4.1% of the Canadian population. Of these individuals, 962,290 used canes/walking sticks/crutches, and 465,340 used a walker. Users of walking aids were predominantly female, with a mean age of 68 years. Limitations. Self-reported results reflect only the perceptions of individuals living in Canadian communities. Analyses excluded individuals in residential or long-term care settings and individuals living on First Nations reserves. Conclusions. Since 2004, there has been a 2% increase in the prevalence of walking aid use by the Canadian population, which is likely related to the aging of the population. The high prevalence of walking aid use highlights the need for better use of existing resources to ensure that individuals are receiving the correct devices. Results of this study suggest a need to evaluate the impact of device use to better understand how resources should be allocated for prescription and maintenance of walking aids and training of users. [ABSTRACT FROM AUTHOR]

Automatic methods for recognizing topically relevant documents supported by high quality research can assist clinicians in practicing evidence-based medicine. We approach the challenge of identifying articles with high quality clinical evidence as a binary classification problem. Combining predictions from supervised machine learning methods and using deep semantic features, we achieve 73.5% precision and 67% recall.

In June 2003, a large, Canadian tertiary care facility introduced automatic drug dispensing machines on all units when it opened up a new building. In this paper, we provide an overview of the automatic drug dispensing system (ADS) implementation at the hospital. Our findings, based on daily field observations and interviews during and after implementation, with regular follow-up visits to the field site illustrate how the introduction of the ADS brought to light work practices that sometimes compromised patient safety. We suggest that utopian views of automatic drug dispensing machines obfuscate the challenges inherent to implementing such systems, and deter stakeholders from performing rigorous evaluation of the costs (both social and economic) and benefits of investing in such systems. Our work contributes to debates about the socio-technical efficacy of automating medication dispensing and delivery, and suggests that the balance of power in the patient safety equation lies in the work context and implementation issues, and not just the technology. For technology implementations to be successful considering that technologies frequently cross over jurisdictional boundaries, planning and implementation have to be conducted at a system wide level.

Background: A software based tool has been developed (Optem) to allow automatize the recommendations of the Canadian Multiple Sclerosis Working Group for optimizing MS treatment in order to avoid subjective interpretation. Methods: Treatment Optimization Recommendations (TORs) were applied to our database of patients treated with IFN β1a IM. Patient data were assessed during year 1 for disease activity, and patients were assigned to 2 groups according to TOR: "change treatment" (CH) and "no change treatment" (NCH). These assessments were then compared to observed clinical outcomes for disease activity over the following years. Results: We have data on 55 patients. The "change treatment" status was assigned to 22 patients, and "no change treatment" to 33 patients. The estimated sensitivity and specificity according to last visit status were 73.9% and 84.4%. During the following years, the Relapse Rate was always higher in the "change treatment" group than in the "no change treatment" group (5 y; CH: 0.7, NCH: 0.07; p < 0.001, 12 m -- last visit; CH: 0.536, NCH: 0.34). We obtained the same results with the EDSS (4 y; CH: 3.53, NCH: 2.55, annual progression rate in 12 m -- last visit; CH: 0.29, NCH: 0.13). Conclusion: Applying TOR at the first year of therapy allowed accurate prediction of continued disease activity in relapses and disability progression. [ABSTRACT FROM AUTHOR]