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3. Social support and help-seeking worldwide

Author

Szkody, Erica, Spence, Anjolee, Özdoğru, Asil, Tushir, Bhawna, Chang, Fennie, AKKAŞ, Handan, Sotomayor, Ian, Pavlova, Iuliia, Petrovic, Ivana, Norvilitis, Jill, Pena-Shaff, Judith, Maney, Julia, Arrow, Kaitlyn, Rodriguez, Laura, Moussa-Rogers, Mary, McTighe, Michael, Ogba, Kalu T. U., Yeung, Stephanie Ka Wai Au, Stoppa, Tara, Yang, Yuanyuan, Gosnell, Courtney L., Jérémie-Brink, Gihane, Van Nostrand, Joshua J., Arriaga, Patrícia, Martin, Amy, Maksimovic, Ana, Ursu, Andreea, Karakulak, Arzu, Fitapelli, Brianna, Ashdown, Brien K., Sen, Celia K. Naivar, Chartier, Chris, Shane-Simpson, Christina, Redker, Christopher M., McKinney, Cliff, Baro, Danisha, Manrique-Millones, Denisse, Reis, Eduardo Silva, Adamopoulou, Eirini, Volkan, Eliz, Tair, Ergyul, Trujillo, Ethan, Kocalar, Halil Emre, Blocker, Heidi, Malik, Hinza, Orta, İrem Metin, Santos, Jay Claus, Grahe, Jon, Cuccolo, Kelly, Wignall, Liam, McLain, Malorie, Kosic, Marianna, Aita, Moet, Nash, Monique, Miracle, Ogba Oluchi, Christiano, Olivia, Dimitrova, Radosveta, Varma, Rahul, Mann, Rebecca, Dhakal, Sandesh, Estrada-Villalta, Sara, Haden, Sara, Hamilton, Sarah, Camgöz, Selin Metin, Aljuberi, Shams, Chin, Stephanie, Kohn, Steven, Verma, Sunil K., Fletcher, Tifani, Singh, Tushar, Sanders, Abigail, Collado, Adryana, Adusei, Akua, Itani, Alaa, Kaser, Amanda, Wolfe, Amber, Stout, Amy, Akhavan, Anahita, Kirton, Angelique, Çeçen-Eroğul, Ayşe Rezan, Bilir, Bilge, Dupiton, Camille, Lovett, Caroline, Orsini, Chloe, Kpodo, Christney, Aceto, Christopher, Redden, Clare, NyKanen, Danielle, Yildiz, Deniz, Lutringer, Emily, Sevinç, Ender, Baranski, Erica, Khan, Fahd, Jia, Fanli, Cramariuc, Gabriel, Zhang, Guolin, Resulbegoviq, Hakile, Maree, Haneen, Kaur, Harleen, Nelson, Jessie, Espinoza, Jimena Santa Cruz, Hubbard, JoAnna, Edlund, John, Protzko, John, Hoang, Jolie, Stork, Jordan, Vasu, Jordan, Salazar, Jose Verdis, Myhers, Karyssa, Hayward, Kaylynn, Lu, Kevin, Beardmore, Leisha, Levkiv, Liliia, Godoy, Linda Katheryn Hernandez, Paulett, Liseth, Gonzalez, María Fernanda Bonilla, Kalantzis, Maria, Rodrigues, Mariana, Álvarez, Marinés Mejía, Ott, Marissa, Zlokovich, Martha, Brosnan, Mary Kate, Mazzaferro, Mateus, Yetkin, Melis, Johnson, Mikayla, Vukelic, Milica, Clark, Mitchell, AlMalik, Mohammad, Fedavi, Neda, Means-Simonsen, Noah, Cabrera, Onassis, Kovacevic, Panta, Zhang, Qingyi, Rushing, Rachel, Varakis, Rafail, Richardson, Randall, Koch, Sara, Lewis, Savannah, Barrera, Scott, Zheng, Sifan, Liu, Siyu, Papka, Sophia, Das, Sreeja, Ghimire, Srijana, Verma, Tanya, Hillman, Taylor, Ozkusen, Ugur C., Zhang, Xinyi (Spencer), Gu, Yiwen, Redd, Bryce, and Cascalheira, Cory J.

Published
2024
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4. Domain independent post-processing with graph U-nets: Applications to Electrical Impedance Tomographic Imaging

Author

Herzberg, William, Hauptmann, Andreas, and Hamilton, Sarah J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning
Abstract

Reconstruction of tomographic images from boundary measurements requires flexibility with respect to target domains. For instance, when the system equations are modeled by partial differential equations the reconstruction is usually done on finite element (FE) meshes, allowing for flexible geometries. Thus, any processing of the obtained reconstructions should be ideally done on the FE mesh as well. For this purpose, we extend the hugely successful U-Net architecture that is limited to rectangular pixel or voxel domains to an equivalent that works flexibly on FE meshes. To achieve this, the FE mesh is converted into a graph and we formulate a graph U-Net with a new cluster pooling and unpooling on the graph that mimics the classic neighborhood based max-pooling. We demonstrate effectiveness and flexibility of the graph U-Net for improving reconstructions from electrical impedance tomographic (EIT) measurements, a nonlinear and highly ill-posed inverse problem. The performance is evaluated for simulated data and from three measurement devices with different measurement geometries and instrumentations. We successfully show that such networks can be trained with a simple two-dimensional simulated training set and generalize to very different domains, including measurements from a three-dimensional device and subsequent 3D reconstructions., Comment: 12 pages, 11 figures

Published
2023

12. Adolescent Mental Health Priorities during the COVID-19 Pandemic

Author

Stewart, Tracy M., Fry, Debi, Wilson, Jenny, McAra, Lesley, Hamilton, Sarah, King, Albert, Laurie, Margaret, and McCluskey, Gillean

Abstract

Increasing evidence has shown that the COVID-19 outbreak has impacted adolescents' mental health. Utilising a mixed-method design, the current study examined a total of 518 adolescent perspectives (60% female), in Scotland, on what "has" and "could" help their mental health in the context of COVID-19. A reflexive thematic analysis revealed three themes in relation to what "has" helped adolescents' mental health since the COVID-19 outbreak. These related to findings about the value of: (1) engaging in recreational activities, (2) engaging with friends, and (3) the disruption to schooling. The remaining four themes related to what could have helped adolescents mental health and wellbeing since the COVID-19 outbreak. These focussed on (1) better support: in relation to mental health; school work; and communication, (2) contact with friends, and (3) more opportunities for recreational activities. Males were more likely to report recreational activities "had" helped and less likely to report better support "could" have helped. Adolescents who reached clinical threshold for depression and anxiety and those with elevated PTSD-like symptoms about COVID-19 were more likely to state more support "could" have helped, and adolescents who reached clinical threshold for depression were "less" likely to report that friends could have helped their mental health. The findings may inform mental health policy and interventions in the recovery from the COVID-19 pandemic.

Published
2023
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17. Contents

Author

Hamilton, Sarah

Published
1998

18. Appendixes

Author

Hamilton, Sarah

Published
1998

22. Notes

Author

Hamilton, Sarah

Published
1998

26. Index

Author

Hamilton, Sarah

Published
1998

33. Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems

Author

Herzberg, William, Rowe, Daniel B., Hauptmann, Andreas, and Hamilton, Sarah J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Mathematics - Numerical Analysis, Mathematics - Optimization and Control
Abstract

The majority of model-based learned image reconstruction methods in medical imaging have been limited to uniform domains, such as pixelated images. If the underlying model is solved on nonuniform meshes, arising from a finite element method typical for nonlinear inverse problems, interpolation and embeddings are needed. To overcome this, we present a flexible framework to extend model-based learning directly to nonuniform meshes, by interpreting the mesh as a graph and formulating our network architectures using graph convolutional neural networks. This gives rise to the proposed iterative Graph Convolutional Newton-type Method (GCNM), which includes the forward model in the solution of the inverse problem, while all updates are directly computed by the network on the problem specific mesh. We present results for Electrical Impedance Tomography, a severely ill-posed nonlinear inverse problem that is frequently solved via optimization-based methods, where the forward problem is solved by finite element methods. Results for absolute EIT imaging are compared to standard iterative methods as well as a graph residual network. We show that the GCNM has strong generalizability to different domain shapes and meshes, out of distribution data as well as experimental data, from purely simulated training data and without transfer training., Comment: 9 figures, 5 tables

Published
2021

35. 3D EIT Reconstructions from Electrode Data using Direct Inversion D-bar and Calderon Methods

Author

Hamilton, Sarah J., Isaacson, David, Kolehmainen, Ville, Muller, Peter A., Toivanen, Jussi, and Bray, Patrick F.

Subjects
Mathematics - Numerical Analysis, Mathematics - Analysis of PDEs, 65N21, 94A08, 78A46, 92C55
Abstract

The first numerical implementation of a D-bar method in 3D using electrode data is presented. Results are compared to Calder\'on's method as well as more common TV and smoothness regularization-based methods. D-bar methods are based on tailor-made non-linear Fourier transforms involving the measured current and voltage data. Low-pass filtering in the non-linear Fourier domain is used to stabilize the reconstruction process. D-bar methods have shown great promise in 2D for providing robust real-time absolute and time-difference conductivity reconstructions but have yet to be used on practical electrode data in 3D, until now. Results are presented for simulated data for conductivity and permittivity with disjoint non-radially symmetric targets on spherical domains and noisy voltage data. The 3D D-bar and Calder\'on methods are demonstrated to provide comparable quality to their 2D CGO counterparts, and hold promise for real-time reconstructions., Comment: 29 pages, 13 figures, 6 tables

Published
2020

41. Leaders in Limbo: The Role of Collaborative Inquiry Influencing School Leaders' Levels of Efficacy

Author

Adams, Pamela, Braunberger, Dana, Hamilton, Sarah, and Caldwell, Bonnie

Abstract

Using collaborative inquiry as a methodology for investigation, this study examined the implementation of a model of generative leadership (Adams et al., 2019) to explore the question: In what ways can implementation of a generative leadership model of professional learning for school leaders influence their sense of efficacy? An adapted model of generative leadership was adopted by seven school leaders in Alberta, Canada over a period of three years. Data was collected at the end of each year through journal reflections (Bashan & Holsblat, 2017) to ascertain school leaders' levels of efficacy. Findings revealed four themes that identified how a generative leadership model influenced participants' effectiveness in their roles. These findings offer insight into how models of school leader professional learning might be re-imagined in order to positively influence their sense of efficacy.

Published
2021
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43. Reply by Author

Author

St-Laurent, Marie-Pier, primary, Acland, George, additional, Hamilton, Sarah N., additional, Hamm, Jeremy, additional, Sunderland, Katherine, additional, Black, Peter C., additional, McKenzie, Michael, additional, Keyes, Mira, additional, Miller, Stacy, additional, Gleave, Martin E., additional, and Tyldesley, Scott, additional

Published
2024
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44. Long Term Second Malignancies in Prostate Cancer Patients Treated with Low-Dose-Rate Brachytherapy and Radical Prostatectomy

Author

St-Laurent, Marie-Pier, primary, Acland, George, additional, Hamilton, Sarah N., additional, Hamm, Jeremy, additional, Sunderland, Katherine, additional, Black, Peter C., additional, McKenzie, Michael, additional, Keyes, Mira, additional, Miller, Stacy, additional, Gleave, Martin E., additional, and Tyldesley, Scott, additional

Published
2024
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45. Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks

Author

Hamilton, Sarah Jane and Hauptmann, Andreas

Subjects
Mathematics - Numerical Analysis, Computer Science - Neural and Evolutionary Computing, Mathematics - Analysis of PDEs
Abstract

The mathematical problem for Electrical Impedance Tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation. D-bar methods are based on a rigorous mathematical analysis and provide robust direct reconstructions by using a low-pass filtering of the associated nonlinear Fourier data. Similarly to low-pass filtering of linear Fourier data, only using low frequencies in the image recovery process results in blurred images lacking sharp features such as clear organ boundaries. Convolutional Neural Networks provide a powerful framework for post-processing such convolved direct reconstructions. In this study, we demonstrate that these CNN techniques lead to sharp and reliable reconstructions even for the highly nonlinear inverse problem of EIT. The network is trained on data sets of simulated examples and then applied to experimental data without the need to perform an additional transfer training. Results for absolute EIT images are presented using experimental EIT data from the ACT4 and KIT4 EIT systems., Comment: 11 pages, 13 figures

Published
2017
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46. School Closures, Exam Cancellations and Isolation: The Impact of COVID-19 on Young People's Mental Health

Author

McCluskey, Gillean, Fry, Debi, Hamilton, Sarah, King, Albert, Laurie, Margaret, McAra, Lesley, and Stewart, Tracy M.

Abstract

Schools often provide structure and safety to students but particularly for vulnerable children and young people. The COVID 19 pandemic has caused unprecedented upheaval across society as a whole but it is clear that the effects are uneven and that some groups have been more directly and negatively impacted, both in the UK and globally. While there are now much helpful quantitative data on the effects of COVID 19 on education, there has been little in-depth qualitative study which examines the experiences and attitudes of young people as students and specifically the effect of school closures and exam cancellations on their mental health and wellbeing. This paper discusses findings from online group interviews with a total of 45 young people and aims to understand the impact on mental health and wellbeing and what young people, themselves, suggest would help mitigate the effects of the global pandemic on their mental health and wellbeing in school.

Published
2021
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50. Addressing the Rising Trend in Early-Age-Onset Cancers in Canada.

Author

Wildgoose, Petra, Servidio-Italiano, Filomena, Raphael, Michael J., Slovinec D'Angelo, Monika, Macaulay, Cassandra, Kassam, Shaqil, Nixon, Nancy, Perea, José, Hamilton, Sarah, Ramjeesingh, Ravi, Gill, Sharlene, Pollett, Aaron, Ogino, Shuji, Ugai, Tomotaka, and Gupta, Abha

Subjects
PATIENT participation, EARLY detection of cancer, SUPPORT groups, PATIENTS' families, SELF-efficacy
Abstract

A multi-disciplinary symposium on early-age onset cancer (EAOC) was held in October 2023 to explore challenges experienced by this rapidly growing population. A major outcome of the symposium was recognition of the remarkable similarities of EAOC patients' journeys across cancer sites. Prevention and early detection of cancer are hindered by a lack of awareness among patients and family doctors that cancer can and does occur in younger persons. Distinct characteristics of the disease—such as a later stage at diagnosis and more aggressive tumor biology—require more potent treatments, which result in profound physical and psychosocial consequences that are unique to this age group. EAOC patient empowerment emerged as another key theme of the symposium. The development of a greater number of specialized clinics was called for, and patient support groups were recognized for the vital role they play in empowering patients and their families. Leading-edge medical advancements hold tremendous hope across the spectrum of EAOC care. New technologies based on genomic profiling, immunotherapy and microbiome alteration contribute to the development of highly effective, personalized approaches to treatment. All symposium participants expressed their commitment to speak with one resounding voice to advocate for equitable access to leading care practices for EAOC patients; thus, a fourth symposium is planned for November 2024. [ABSTRACT FROM AUTHOR]

Published
2024
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