Search

Your search keyword '"Cote, Melissa"' showing total 12 results
Start Over Author "Cote, Melissa" Search Limiters Full Text
12 results on '"Cote, Melissa"'

1. Web-Based Training for Nurses on Shared Decision Making and Prenatal Screening for Down Syndrome: Protocol for a Randomized Controlled Trial

Author

Poulin Herron, Alex, Agbadje, Titilayo Tatiana, Cote, Melissa, Djade, Codjo Djignefa, Roch, Geneviève, Rousseau, Francois, and Légaré, France

Subjects
Medicine, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundPregnant women often find it difficult to choose from among the wide variety of available prenatal screening options. To help pregnant women and their partners make informed decisions based on their values, needs, and preferences, a decision aid and a web-based shared decision making (SDM) training program for health professionals have been developed. In Canada, nurses provide maternity care and thus can train as decision coaches for prenatal screening. However, there is a knowledge gap about the effectiveness of SDM interventions in maternity care in nursing practice. ObjectiveThis study aims to assess the impact of an SDM training program on nurses’ intentions to use a decision aid for prenatal screening and on their knowledge and to assess their overall impressions of the training. MethodsThis is a 2-arm parallel randomized trial. French-speaking nurses working with pregnant women in the province of Quebec were recruited online by a private survey firm. They were randomly allocated (1:1 ratio) to either an experimental group, which completed a web-based SDM training program that included prenatal screening, or a control group, which completed a web-based training program focusing on prenatal screening alone. The experimental intervention consisted of a 3-hour web-based training hosted on the Université Laval platform with 4 modules: (1) SDM; (2) Down syndrome prenatal screening; (3) decision aids; and (4) communication between health care professionals and the patient. For the control group, the topic of SDM in Module 1 was replaced with “Context and history of prenatal screening,” and the topic of decision aids in Module 3 was replaced with “Consent in prenatal screening.” Participants completed a self-administered sociodemographic questionnaire with close-ended questions. We also assessed the participants' (1) intention to use a decision aid in prenatal screening clinical practice, (2) knowledge, (3) satisfaction with the training, (4) acceptability, and (5) perceived usefulness of the training. The randomization was done using a predetermined sequence and included 40 nurses. Participants and researchers were blinded. Intention to use a decision aid will be assessed by a t test. Bivariate and multivariate analysis will be performed to assess knowledge and overall impressions of the training. ResultsThis study was funded in 2017 and approved by Genome Canada. Data were collected from September 2019 to late January 2020. This paper was initially submitted before data analysis began. Results are expected to be published in winter 2020. ConclusionsStudy results will inform us on the impact of an SDM training program on nurses’ intention to use and knowledge of decision aids for prenatal screening and their overall impressions of the training. Participant feedback will also inform an upgrade of the program, if needed. Trial RegistrationClinicalTrials.gov NCT04162288; https://clinicaltrials.gov/ct2/show/NCT04162288 International Registered Report Identifier (IRRID)DERR1-10.2196/17878

Published
2020
Full Text
View/download PDF

4. Symbol Spotting on Digital Architectural Floor Plans Using a Deep Learning-based Framework

Author

Rezvanifar, Alireza, Cote, Melissa, and Albu, Alexandra Branzan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

This papers focuses on symbol spotting on real-world digital architectural floor plans with a deep learning (DL)-based framework. Traditional on-the-fly symbol spotting methods are unable to address the semantic challenge of graphical notation variability, i.e. low intra-class symbol similarity, an issue that is particularly important in architectural floor plan analysis. The presence of occlusion and clutter, characteristic of real-world plans, along with a varying graphical symbol complexity from almost trivial to highly complex, also pose challenges to existing spotting methods. In this paper, we address all of the above issues by leveraging recent advances in DL and adapting an object detection framework based on the You-Only-Look-Once (YOLO) architecture. We propose a training strategy based on tiles, avoiding many issues particular to DL-based object detection networks related to the relative small size of symbols compared to entire floor plans, aspect ratios, and data augmentation. Experiments on real-world floor plans demonstrate that our method successfully detects architectural symbols with low intra-class similarity and of variable graphical complexity, even in the presence of heavy occlusion and clutter. Additional experiments on the public SESYD dataset confirm that our proposed approach can deal with various degradation and noise levels and outperforms other symbol spotting methods., Comment: Accepted to CVPR2020 Workshop on Text and Documents in the Deep Learning Era

Published
2020

5. A Deep Learning-based Framework for the Detection of Schools of Herring in Echograms

Author

Rezvanifar, Alireza, Marques, Tunai Porto, Cote, Melissa, Albu, Alexandra Branzan, Slonimer, Alex, Tolhurst, Thomas, Ersahin, Kaan, Mudge, Todd, and Gauthier, Stephane

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning
Abstract

Tracking the abundance of underwater species is crucial for understanding the effects of climate change on marine ecosystems. Biologists typically monitor underwater sites with echosounders and visualize data as 2D images (echograms); they interpret these data manually or semi-automatically, which is time-consuming and prone to inconsistencies. This paper proposes a deep learning framework for the automatic detection of schools of herring from echograms. Experiments demonstrated that our approach outperforms a traditional machine learning algorithm using hand-crafted features. Our framework could easily be expanded to detect more species of interest to sustainable fisheries., Comment: Accepted to NeurIPS 2019 workshop on Tackling Climate Change with Machine Learning, Vancouver, Canada

Published
2019

10. The Driver of the Carbon Isotope Minima During the Last Deglaciation: A Weakened Biological Pump or Enhanced Southern Ocean Circulation?

Author

Cote, Melissa C

Abstract

The cause of the initial rise in atmospheric CO2 during the last deglaciation remains unknown. Coincident with the rising atmospheric CO2, the δ13C of atmospheric CO2 decreased by ~0.3‰ during Heinrich Stadial 1 (HS1: 14.5-17.5 kyr BP), which requires the input of carbon from an isotopically light reservoir. The light carbon signal in the atmosphere occurred concurrently with the carbon isotope minimum, or a decrease in surface ocean δ13C of ~0.5‰, suggesting the two phenomena are related. The leading hypotheses explaining the δ13C minimum are (1) enhanced ventilation of 13C-depleted abyssal water in the Southern Ocean which in turn caused low δ13C values in the surface ocean and atmosphere, and (2) a reduction in the Atlantic Meridional Overturning Circulation (AMOC) weakened the efficiency of the ocean’s biological pump, thereby increasing the concentration of light carbon in the surface ocean. In order to evaluate these two hypotheses, we compiled 70 published, globally-distributed planktonic foraminiferal δ13C records and enhanced the sampling resolution of three low resolution records from the western tropical Pacific (WTP). The HS1 δ13C anomaly, or the relative difference in δ13C between the LGM and HS1, was calculated for each record, and we compared the spatial patterns between ocean basins and within the tropical Pacific and Southern Oceans. We find that the average δ13C anomaly is similar in all ocean basins. We also find similar δ13C signals in the eastern equatorial Pacific (EEP) upwelling regime and the WTP convergence zone. In the Southern Ocean we find a latitudinal trend of δ13C anomalies decreasing in magnitude progressing towards higher latitudes and the region of abyssal upwelling. Because the Southern Ocean hypothesis implies that the δ13C signal should be largest in the Southern Ocean and in upwelling regions, our results are inconsistent with a Southern Ocean driver. Our findings are instead consistent with a recent modeling study that simulated the effects of a weakened biological pump, which produced an excess of isotopically light carbon in the surface ocean and atmosphere, similar to observations. We conclude that our results are broadly consistent with a biological pump mechanism, suggesting that the initial rise in atmospheric CO2 was driven by biogeochemical processes in the upper ocean as opposed to upwelling of light carbon from the abyss.

Published
2016

11. Labeling Guides Object Individuation in 12-Month-Old Infants

Author

Fei Xu, Cote, Melissa, and Baker, Allison

Subjects
Individuation -- Research, Interpersonal communication in children -- Research, Interpersonal communication in children -- Psychological aspects, Cognition in infants -- Research, Psychology and mental health
Abstract

A new manual search method was used to investigate the impact of naming on object individuation in 12-month-old infants. In Experiment 1, on a two-word trial, an experimenter looked into a box while the infant was watching and provided two labels (e.g., 'Look, a fep!' and 'Look, a wug!'). On a one-word trial, the experimenter instead repeated the same label (e.g., 'Look, a zav!'). After the infant retrieved one object from the box, subsequent search behavior was recorded. Infants searched more persistently (i.e., for a longer duration) after hearing two labels than one, suggesting that hearing two labels led the infants to expect two objects inside the box. In Experiment 2, infants' search behavior did not differ depending on whether they heard one or two emotional expressions, suggesting that the facilitation effect observed in Experiment 1 may be specific to linguistic expressions. Thus, we provide the first evidence that infants as young as 12 months are able to use intentional and referential cues to guide their object representations. These findings also suggest that a rudimentary version of the mutual-exclusivity constraint may be functional by the end of the first year.

Published
2005

Catalog

Books, media, physical & digital resources
See catalog results