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The number of people suffering from mental health disorders is steadily rising as a result of growing social and economic inequality, ongoing political conflict, and, not least, the COVID 19 pandemic. The rapid progress of artificial intelligence, and within it chatbots, presents an opportunity to address these deficiencies by reducing treatment barriers and providing economic benefits to service providers and consumers. To assure the effectiveness of chatbots in psychological health applications, they have to be accepted by users. A chatbot’s acceptance in mental health interventions is influenced by the benefits of intelligent machines, their expectation of nonjudgmental and unbiased support, and the effect of stigma on trust and belief in healthcare. Based on these insights, the experimental study examines whether users of psychological health apps more readily accept chatbots as opposed to physical health apps. Furthermore, the humanization of chatbots is a proven tool to enhance the quality of interaction with users. Thus, this dissertation additionally aims to investigate if a humanized chatbot entity affects their acceptance in the context of mental health apps. The results suggest that chatbots are more widely accepted in mental health applications compared to physical health applications. Moreover, the findings lead to the recommendation to implement humanized entities in chatbots within mental health applications. The results provide a rationale for conducting additional research to investigate the subject in greater depth. Due to the continuous development of AI, the utilization of chatbots in mental health care should be investigated continuously. O número de pessoas que sofrem de perturbações de saúde mental está a aumentar constantemente devido à desigualdade social e económica, conflitos políticos e da pandemia de COVID-19. O rápido progresso da inteligência artificial representa uma oportunidade para resolver estas perturbações, reduzindo os obstáculos ao tratamento e proporcionando benefícios económicos aos prestadores de serviços e aos pacientes. Para garantir a eficácia dos chatbots nas aplicações de saúde mental, estes têm de ser aceites pelos utilizadores. Esta aceitação nas intervenções de saúde mental é influenciada pelos benefícios das máquinas inteligentes, pela sua expectativa de apoio imparcial e sem juízos de valor e pelo efeito do estigma na confiança e na crença nos cuidados de saúde. Com base nestes conhecimentos, o estudo experimental examina se os chatbots são mais facilmente aceites pelos utilizadores de aplicações de saúde psicológica do que aplicações de saúde física. Além disso, a humanização dos chatbots é uma ferramenta comprovada para melhorar a qualidade da interacção com os utilizadores. Assim, esta dissertação tem como objetivo investigar se uma entidade chatbot humanizada afeta a sua aceitação no contexto de aplicações de saúde mental. Os resultados sugerem que os chatbots são melhor aceites em aplicações de saúde mental do que em aplicações de saúde física. Além disso, os resultados levam à recomendação da implementação de entidades humanizadas em chatbots dentro de aplicações de saúde mental. Devido ao desenvolvimento contínuo da IA, a utilização de chatbots nos cuidados de saúde mental deve ser investigada numa base contínua.

Advancements in classroom technology have resulted in new types of data collection in educational settings. Along with improvements in the fields of artificial intelligence and machine learning, this educational data can be used to study how we learn and create more personalised learning environments. Starting in March 2020 all in-person courses were abruptly moved to remote instruction in order to combat the COVID-19 pandemic. This influx of students taking remote courses presented a new opportunity to study how students interact with course materials. Remote learning courses at the University of Manitoba are offered using a learning management system (LMS) that centralizes all course activities and files and records user-activities. The use of machine learning techniques with education-based data is an emerging discipline that offers an opportunity to provide new insights in this area. This thesis presents a code-based tool to create student timelines from raw LMS date-time stamp data and extract features describing student behaviours within a single-term online course. The successes and limitations of these features to predict student grade outcomes were investigated using supervised and unsupervised machine learning models. The LMS data was also explored using neural network-based CNNs and transformers. The experiments presented in this thesis indicate that students predominately interact with the system at the same time on any given day relative to their previous interaction. The results further demonstrate that temporal features created from LMS interactions can predict student outcomes with greater than random accuracy. The neural network-based classifiers produced more accurate student outcome predictions than the feature-based ML models at the expense of interpretability. This thesis contributes to the body of knowledge on student modelling and prediction, as well as student behaviour within an LMS in an online course, and suggests that educators can help to reduce students' cognitive load and improve students' learning by updating the LMS at a consistent time of day.

Abstract Mental, physical, and emotional health are essential for everyone to enable the totality and completeness of one’s health and well-being to enhance quality of life. Mental health is one of the deep-rooted issues in the racialized community. Among Black disabled people, mental health is among some neglected areas in research. Generally, there is a lack of culturally appropriate mental health promotion and advocacy for Black disabled people. Due to my professional experience in the health, social services and community non-profit organisations, I was motivated to utilize auto ethnographic methodology to explore my personal experiences, thoughts and ideas regarding the intersectionality of race, COVID-19 and disability and how these interconnected factors affect the mental health of Black disabled people. In this study, I performed reflective examination of my ideas and thoughts revealing how race, COVID-19 and disability have negatively impacted mental health of Black disabled people. I also relied on my own memory of events from my interaction with disabled people, participant observation field notes and research dairies as data collection techniques. Within my stories, I addressed how Black disabled people experienced racism and COVID-19 as a dual pandemic that impact mental health. As the growing awareness of negative impact of the pandemic on Black disabled people widens, I provided readers with possible strategies and recommendations that might solve the impact of racism and COVID-19 on the mental health of Black disabled people. I recommended that mental health training package, toolkit and resources about Black disabled people should be widely distributed and used by everyone who want to embark on the critical journey toward greater awareness, implementation of human rights and efficient creation of an inclusive society.

This paper aims to understand the effects of the COVID-19 pandemic on human rights complaints received by the Manitoba Human Rights Commission. It should be noted that there is not currently any published research available studying the effects of the pandemic on provincial territorial, or federal Canadian human rights tribunals and commissions. As each human rights jurisdiction in Canada follows the applicable provincial or territorial acts, this paper first looks as human rights legislation in Manitoba, applicable legal tests and the investigation process used by the Commission. The paper also aims to understand current research barriers and what is being done to resolve these by the MHRC and other actors. There have been multiple strategies implemented to respond to these barriers, with varying success. To understand the effects of the pandemic on human rights complaints, this paper looks at two separate three-year periods from 2017-2019 and 2020-2022 and studies the complaint data registered by the Commission during these pre- and post-COVID periods. This allows for the comparison of important information such as: types of complaints, protected characteristics and areas of discrimination complaints were filed under, as well as the number breakdowns of complaints each year. The importance of this research is also discussed, explaining not only why the research is needed, but also looks at potential uses for said research.

A significant number of students are living with diagnosed and undiagnosed mental health disorders such as anxiety, depression, post-traumatic stress disorder, schizophrenia and substance-use, and this number has only increased due to the COVID-19 worldwide pandemic. When students are demonstrating symptoms of languishing mental health within the classroom, it is the classroom teachers, resource teachers and counsellors who experience first-hand, the challenges that come along with this, and work to implement strategies to support these students. This qualitative study has the purpose of obtaining the perspectives of classroom and support teachers on their experiences in working with students with mental health needs and how they are addressed and supported in school-based settings that are implementing Positive Behavioural Interventions and Supports (PBIS). The data from individual interviews were analysed and coded, revealing six themes: 1) Diversity of student mental health and wellbeing in the classroom, 2) Implementation and integration of PBIS and SEL, 3) Strategies to support student mental health and wellbeing in schools, 4) Barriers and challenges in supporting student mental health and wellbeing in schools, 5) Effect of PBIS on student mental health, and 6) Impact of Covid-19 on students’ and teachers’ mental health and wellbeing. This study makes recommendations for change in current practice in schools, including the integration of PBIS and SEL, administrator support of instructional strategies that foster positive mental health, and the development of school culture that has an emphasis on collaboration and co-teaching.

Purpose of Review: The COVID-19 pandemic and its associated restrictions have had a significant impact on people’s everyday lives, including the lives of Autistic adults. We aimed to (a) synthesise all papers currently published on the impact of the COVID-19 pandemic on autistic adults and (b) identify lessons for the care and support of Autistic adults in pandemic and post-pandemic times. Recent Findings: Fifty-five papers met the inclusion criteria. Most studies focused on the pandemic’s impact on the wellbeing of Autistic adults. Several studies focused on the use of (telehealth) services or the risk of COVID-19 infection/hospitalisation. Summary: Autistic adults were significantly impacted by the pandemic, both directly as indicated by higher COVID-19 infection and hospitalisation rates, but also indirectly due to severe service disruptions and social restrictions. Even though there were large differences observed both between as well as within individuals in terms of the negative/positive effects of the COVID-19 pandemic, most studies reported a negative effect on Autistic adults’ mental health. We draw several lessons from this review for the future care and support of Autistic adults, all of which must be underpinned by participatory research methods, that is, where Autistic community members are actively involved in setting research questions, testing the acceptability of the methods and interpreting and disseminating the results.

Background Maternal mental health concerns and parenting stress in the first few years following childbirth are common and pose significant risks to maternal and child well-being. The COVID-19 pandemic has led to increases in maternal depression and anxiety and has presented unique parenting stressors. Although early intervention is crucial, there are significant barriers to accessing care. Methods To inform a larger randomized controlled trial, the current open-pilot trial investigated initial evidence for the feasibility, acceptability, and efficacy of a newly developed online group therapy and app-based mental health and parenting program (BEAM) for mothers of infants. Forty-six mothers 18 years or older with clinically elevated depression scores, with an infant aged 6–17 months old, and who lived in Manitoba or Alberta were enrolled in the 10-week program (starting in July 2021) and completed self-report surveys. Results The majority of participants engaged in each of the program components at least once and participants indicated relatively high levels of app satisfaction, ease of use, and usefulness. However, there was a high level of attrition (46%). Paired-sample t-tests indicated significant pre- to post-intervention change in maternal depression, anxiety, and parenting stress, and in child internalizing, but not externalizing symptoms. Effect sizes were in the medium to high range, with the largest effect size observed for depressive symptoms (Cohen’s d = .93). Discussion This study shows moderate levels of feasibility and strong preliminary efficacy of the BEAM program. Limitations to program design and delivery are being addressed for testing in adequately powered follow-up trials of the BEAM program for mothers of infants. Trial registration NCT04772677 . Registered on February 26 2021.

BACKGROUND: During the COVID-19 pandemic cancer patients might have experienced delays in screening, diagnosis and/or treatment. A systematic review was conducted to give an overview of the effects of COVID-19 induced delays in oncological care on the physical and mental health outcomes of cancer patients. METHODS: MEDLINE and EMBASE databases were searched for articles on the effects of COVID-19 induced delays on physical and mental health outcomes. RESULTS: Out of 1333 papers, eighteen observational, and twelve modelling studies were included. In approximately half of the studies, tumor stage distribution differed during the pandemic compared to before the pandemic. Modelling studies predicted that the estimated increase in the number of deaths ranged from -0.04 to 30%, and the estimated reduction in survival ranged from 0.4 to 35%. Varying results on the impact on mental health, e.g. anxiety and depression, were seen. CONCLUSIONS: Due to large methodological discrepancies between the studies and the varying results, the effect of COVID-19 induced delays on the physical and mental health outcomes of cancer patients remains uncertain. While modelling studies estimated an increase in mortality, observational studies suggest that mortality might not increase to a large extent. More longitudinal observational data from the pandemic period is needed for more conclusive results.

High rates of psychological distress including anxiety and depression are common in the doctoral community and the learning environment has a role to play. With the coronavirus disease (Covid-19) pandemic taking a toll on mental health it is necessary to explore the risk and protective factors for this population. Using data from the Covid-19: Global Study of Social Trust and Mental Health, the present study examined the relationship between Covid-19-related stressful educational experiences and doctoral students’ mental health problems. Moreover, it assessed the role of attentional ability and coping skills in promoting good mental health. One hundred and fifty-five doctoral students completed an online survey where micro-, meso- and macro-level educational stressors were measured. The Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire were used to measure depression and anxiety symptoms, respectively. We also measured coping skills using a 13-item scale and attentional ability using a questionnaire. The results of multiple linear regression analyses showed that specific stressful educational experiences were unrelated but cumulative stressful educational experiences were related to increased depression symptoms (but not anxiety symptoms) in fully adjusted models. Additionally, higher coping skills and attentional ability were related to fewer depression and anxiety symptoms. Finally, no associations between demographics and other covariates and mental health problems were found. The experience of multiple educational stressful events in their learning environment due to Covid-19 is a key risk factor for increased mental illness in the doctoral community. This could be explained by the uncertainty that the Covid-19 pandemic has caused to the students.

Introduction: Unpaid caregiving, care provided by family/friends, is a public health issue of increasing importance. COVID-19 worsened the mental health conditions of unpaid caregivers, increasing substance/drug use and early development of chronic disease. The impact of the intersections of race and ethnicity, sex, age and gender along with unpaid care work and caregivers’ health and well-being is unknown. The aim of this study is to describe the inequities of caregiver well-being across the intersections of race and ethnicity, sex, age and gender using a cross-sectional survey design. Methods and analysis: We are collaborating with unpaid caregivers and community organisations to recruit a non-probability sample of unpaid caregivers over 18 years of age (n=525). Recruitment will focus on a target sample of 305 South Asian, Chinese and Black people living in Canada, who represent 60% of the Canadian racial and ethnic populations. The following surveys will be combined into one survey: Participant Demographic Form, Caregiver Well-Being Index, interRAI Self-report of Carer Needs and the GENESIS (GENdEr and Sex DetermInantS of Cardiovascular Disease: From Bench to Beyond-Premature Acute Coronary Syndrome) PRAXY Questionnaire. Sample characteristics will be summarised using descriptive statistics. The scores from the Caregiver Well-Being Index will be dichotomised into fair/poor and good/excellent. A two-stage analytical strategy will be undertaken using logistic regression to model fair/poor well-being and good/excellent well-being according to the following axes of difference set a priori: sex, race and ethnicity, gender identity, age, gender relations, gender roles and institutionalised gender. The first stage of analysis will model the main effects of each factor and in the second stage of analysis, interaction terms will be added to each model. Ethics and dissemination: The University of Toronto’s Health Sciences Research Ethics Board granted approval on 9 August 2022 (protocol number: 42609). Knowledge will be disseminated in pamphlets/infographics/email listservs/newsletters and journal articles, conference presentation and public forums, social media and through the study website.