Trauma has been described as a pressing public health concern and research evidence demonstrates how unresolved trauma can lead to multiple co-morbidities including chronic medical conditions such as cardiovascular disease. Furthermore, epidemiological evidence demonstrates the high prevalence of trauma histories amongst service users seeking care across a range of systems including child welfare, education, health, social services and the criminal justice system. In response, the concept of trauma-informed care (TIC) has emerged, but how TIC can be conceptually defined and utilized remains unclear in the scholarly literature. This dissertation utilizes a variety of methodological approaches to explore how and under what conditions TIC can be utilized within and across systems of care to address the prevalence of trauma-affected individuals seeking care. First, a critical interpretive synthesis of the TIC literature provides an overview of how TIC can be defined and utilized through the development of a conceptual framework situating TIC within and across systems of care. A theoretical framework outlines important contextual factors, such as system arrangements as well as the political system, that can act as either barriers or facilitators to the operationalization of TIC. Second, a document analysis examines how and under what conditions TIC is utilized in adult mental health policy documents in Ontario, Canada. Finally, a case study explores what factors led to the exclusion of TIC from Ontario’s first province-wide strategy on mental health and addictions. Collectively, these three studies add several substantive, methodological and theoretical contributions regarding a cohesive understanding of what is trauma, how TIC can be defined and operationalized and the role of TIC at various levels within and across systems of care. Mobilizing sustainable and effective TIC has been demonstrated to improve the overall health and well-being of both service users and services providers, leading to stronger systems of care and healthier communities and societies at large. / Thesis / Candidate in Philosophy / The concept of trauma-informed care (TIC) has emerged in response to increased awareness regarding the prevalence and impact of trauma. A growing body of empirical literature has demonstrated the effectiveness of TIC within specific programs and services as well as at the organizational and system levels. What constitutes trauma, however, and how TIC can be defined and operationalized at various levels remains unclear. This thesis aims to address these gaps in the literature by: (1) developing a conceptual framework on TIC and a theoretical framework outlining the barriers and facilitators of TIC (2) examining how and under what conditions can TIC be utilized in mental health policy documents (3) exploring what are the political factors that can lead governments to decide against utilizing TIC.

Background. In order to deliver effective and efficient public health services, the best available research evidence should be considered when making public health decisions. The process of evidence-informed decision making (EIDM) involves searching for research evidence, appraising and synthesizing the high quality evidence, and adapting the evidence with consideration of local contextual factors and community preferences. For many public health departments achieving EIDM has been a challenge. Methods. This study aimed to learn how EIDM spread through interpersonal interactions within the health department of a provincial government in Canada. The health department was selected based on anecdotal evidence suggesting that ideas of EIDM had diffused within this department. Employees were invited to participate in an electronic survey about their interactions regarding EIDM. The data collected from this survey were analyzed using social network analysis methods. This helped to show how the interpersonal connections helped to spread the ideas of EIDM within the organization. Results. In this organization EIDM discussion occurred most often within the organizational divisions, whereas influence for EIDM often occurred both within and between divisions. The type of relationship that appeared most important in discussion of EIDM was colleague relationships, while supervisors were more important for encouraging use of EIDM. Furthermore, individuals in leadership positions within the organization were shown to have played an important role in the diffusion of EIDM. Limitations and Conclusions. Low participation resulted in a limited picture of the whole network of this organization. The use of social network analysis is a relatively novel approach for studying the diffusion of EIDM, and there are challenges to this approach that requires special consideration when working with organizations. / Thesis / Master of Science in Nursing (MSN)

The purpose of this study was to examine the academic, social, and emotional effects of COVID-19 on 11th- and 12th-grade students. There has been little documentation regarding the specific change that the pandemic has had on these students; it is imperative that we look at the effects of COVID-19 on the students who experienced COVID-19 pandemic. Data collection was completed through individual interviews. Analysis of data occurred in three phases: (a) categorization of data under the four organizational factors, (b) building the explanation in a phenological form, and (c) re-examination of the data. The analysis of the narrative study was based on the theoretical proposition that the teachers were focused on the whole child and not just academic standards. The humanistic theory framework was established as teachers examined the influences in their classrooms based on the well-being of their students. The credibility of the analysis was protected by triangulation of data through multiple sources of evidence, establishment of a chain of evidence, and member checking. The results revealed that the main concerns were students attending school and getting the credits needed to graduate through both in-person classes and online learning. The themes that emerged were classroom management, relationship building, communication, social-emotional learning, trauma-informed learning, and the effects of COVID-19.

Autenticitetsbegreppet inom historiskt informerat framförande (HIP, eng. historically informed performance) kopplas till strävan efter historisk informerarad kunskap om tonsättarens intentioner, tonsättartidens spelsätt och originalpublikens upplevelse av musiken. År 1995 betraktas som ett viktigt år för diskursen om autenticitet inom HIP. Richard Taruskins och Peter Kivys positioner blev startskottet till ett perspektiv som anser att det finns en till dimension av autenticitet i utövarens roll. Musikernas interpretationsrelaterade val grundas på estetiska värderingar och konstnärliga intensioner. Utifrån detta perspektiv studeras hur fem nutida musiker verksamma inom olika musikgenrer utanför den västerländsk konstmusiken, förhåller sig till diskussionen om HIP i sina framföranden av Bachs musik. Metoden som används är ostrukturerade intervjuer med insatser av stimulated recall samt musikanalys. Studien visar att dessa musikers adresserar framförandet utifrån ett förhållningssätt i vilket (explicit och medvetet i vissa fall, implicit och omedvetet i andra) diskursen om autenticitet inom HIP speglas.

Storing manure on dairy farms is essential for maximizing its fertilizer value, reducing management costs, and minimizing potential environmental pollution challenges. However, ammonia loss through volatilization during storage remains a challenge. Quantifying these losses is necessary to inform decision-making processes to improve manure management, and design ammonia mitigation strategies. In 2003, the National Research Council recommended using process-based models to estimate emissions of pollutants, such as ammonia, from animal feeding operations. While much progress has been made to meet this call, still, their accuracy is limited because of the inadequate values of manure properties such as heat and mass transfer coefficients. Additionally, the process-based models lack realistic estimations for manure temperatures; they use ambient air temperature surrogates which was found to underestimate the atmospheric emissions during storage. This study uses machine learning algorithms' unique abilities to address some of the challenges of process-based modeling. Firstly, ammonia concentrations, manure temperature, and local meteorological factors were measured from three dairy farms with different manure management practices and storage types. This data was used to estimate the influence of manure characteristics and meteorological factors on the trend of ammonia emissions. Secondly, the data was subjected to four data-driven machine learning algorithms and a physics-informed neural network (PINN) to predict manure temperature. Finally, a deep-learning approach that combines process-based modeling and recurrent neural networks (LSTM) was introduced to estimate ammonia loss from dairy manure during storage. This method involves inverse problem-solving to estimate the heat and mass transfer coefficients for ammonia transport and emission from stored manure using the hyperparameters optimization tool, Optuna. Results show that ammonia flux patterns mirrored manure temperature closely compared to ambient air temperature, with wind speed and crust thickness significantly influencing ammonia emissions. The data-driven machine learning models used to estimate the ammonia emissions had a high predictive ability; however, their generalization accuracy was poor. However, the PINN model had superior generalization accuracy with R2 during the testing phase exceeded 0.70, in contrast to -0.03 and 0.66 for finite-elements heat transfer and data-driven neural network, respectively. In addition, optimizing the process-based model parameters has significantly improved performance. Finally, Physics-informed LSTM has the potential to replace conventional process-based models due to its computational efficiency and does not require extensive data collection. The outcomes of this study contribute to precision agriculture, specifically designing suitable on-farm strategies to minimize nutrient loss and greenhouse gas emissions during the manure storage periods. / Doctor of Philosophy / Dairy farming is critical for meeting the global demand for animal protein products; however, it generates a lot of manure that must be appropriately managed. Manure can only be applied to crop or pasture lands during growing seasons. Typically, manure is stored on farms until time permits for land application. During storage, microbial processes occur in the manure, releasing gases such as ammonia. Ammonia emitted contributes to the degradation of ambient air quality, human and animal health problems, biodiversity loss, and soil health deterioration. Furthermore, releasing ammonia from stored manure reduces the nitrogen fertilizer value of stored manure. Implementing control measures to mitigate ammonia emission is necessary to reduce nitrogen loss from stored manure. Deciding and applying appropriate control measures require knowledge of the rate of ammonia emission and when it occurs. Process-based models are a less expensive and more reliable method for estimating ammonia emissions from stored liquid dairy manure. Process-based model is a mathematical model that simulates processes related to ammonia production and emission from stored manure. However, process-based models have limitations because they require estimates of manure properties, which vary depending on the manure management. Additionally, these models use air temperature instead of manure temperature, underestimating the ammonia lost during storage. Therefore, this study used machine learning algorithms to develop more accurate models for predicting manure temperature and estimating ammonia emissions. First, we collected manure temperature, ammonia emissions, and weather data from three dairy farms with different manure management practices and storage structures. We used it to estimate the factors that affect ammonia emissions. The data was then used to develop four machine-learning models and one integrated machine-learning-based to assess their ability to predict manure temperature. Finally, a different machine learning approach that combines process-based modeling and neural networks was used to directly estimate ammonia loss from dairy manure during storage. The results show that manure temperature is closely related to the amount of ammonia lost, and factors like wind speed and crust thickness also influence the amount of ammonia lost. Machine learning algorithms offer a more accurate way to predict manure temperature than traditional methods. Finally, combining machine learning and process-based modeling improved the ammonia emission estimates. This study contributes to precision agriculture by designing suitable on-farm strategies to minimize nutrient loss during manure storage periods. It provides valuable information for dairy farmers and policymakers on managing manure storage more effectively and sustainably.

The past few decades of research support both the impact of trauma (e.g., abuse, neglect, violence) particularly in childhood, and the ability to lessen its effects through the implementation of trauma-informed care (TIC). We have successfully developed a communitywide system of TIC enhancing collaboration and common language across sectors and organizations within sectors. The collaboration involved more than 100 individuals from more than 45 organizations including healthcare, education, children’s services, the faith community, behavioral health providers, criminal justice, law enforcement, private businesses, and others. The process for developing a system of care has been evaluated through community surveys and focus groups, verifying its ability to increase understanding and implementation of TIC principles, replication in a nearby city, and the development of an instructional toolkit to aid other communities in creating such systems of care.

Short selling, and its informational role in the formation of stock prices have been the epicenter of prior literature. Is there a relationship between short selling and abnormal returns? While numerous studies found a negative relationship, researchers do not unanimously agree on the existence, nor the strength, of this relationship. Using net short positions extracted from the registry of the FI for stocks listed in the OMX Stockholm 30 Exchange from January 2017 to December 2020, we examine this relationship exclusively in Sweden. The results have been scrutinized via regression analysis to verify if there is any significant relationship between the announcements of total net short positions and the non-adjusted, as well as the risk-adjusted abnormal returns. We did not find enough evidence to validate previous studies that supported the notion that heavily shorted stocks generate negative abnormal returns for the long buyers. There was a perceptible increase in both risk-adjusted and non-adjusted abnormal returns within a three-day window after the announcement of a short position. Yet, the value was merely zero, inferring that a higher level of short interest does not lead to negative stock returns.

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Abstract available through the Annals of Behavioral Medicine.