A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine. / The rate of technology expansion is rapidly covering even the most remote parts of the globe and in the lowest resource settings. With globalization however, low and middle income areas are facing emerging health issues such as injuries and chronic medical conditions. With these illnesses, there are inevitable demands on emergency services. It has been thought that technology be utilized to augment emergency medical care in such settings where formal Emergency Medical Services. To aggregate and analyze the existing literature on the topic a systematic literature review was conducted. This study analyzed the existing literature on prehospital emergency care in settings in which no formal EMS services were utilized. Four databases were searched with inclusion and exclusion criteria, yielding 1782 results. The initial screening excluded all but 21 articles. Of the 21 articles in full review, 15 were included in the final review. Studies included in the final review were grouped into those reporting outcomes from five categories: Feasibility, Quality of Care, Response Time, Patient Outcomes, and Cost Effectiveness. Only one study was identified to be of high quality. There was a lack of studies with adequate statistical analysis to conduct statistical aggregation. Most studies however reported prehospital telemedicine in settings without EMS to be feasible, provide quality care, are be cost effective. However, the lack of statistical analysis makes it difficult to make conclusions. Also, several studies did show response time of a trained basic life support volunteer to be faster than EMS in many of the settings. But no positive health outcomes were observed in patients treated with projects utilizing technology in the prehospital setting. The prehospital emergency medicine setting is a young field of study that may have significant hurdles in application. The studies conducted have shown promise in the use of technology in prehospital settings without formal EMS services, but are not robust enough to make strong conclusions or recommendations that could be put into practice. Thus, more robust, statistically oriented research is imperative in the field so that we can fully explore the potential of technology in the prehospital setting, especially in low resource and rural settings without formal EMS services. With more robust studies, we can hope to integrate new technologies into practice and better serve the populations without adequate EMS coverage to provide more timely emergency care.

No / Several international organizations have suggested the need for disseminating existing evidence-based parenting interventions into low-resource settings of the world in order to prevent societal difficulties such as violence. Before dissemination efforts take place, it is important to examine the fit of existing interventions in these contexts. In the present study, 80 practitioners from low-resource communities in Panama, Central America, were surveyed in order to explore their views on materials, principles and strategies of an evidence-based parenting program, the Triple P Positive Parenting Program. This study is part of a larger project in which cultural relevance was also explored from parents’ perspective, instruments were translated and validated, and a RCT was carried out to determine efficacy. Practitioners in the present study were psychologists, teachers, social workers and learning disability specialists based in school settings. Descriptive statistics were used to analyze the data and regression analyses were carried out in order to determine whether socio-demographic variables predicted acceptability scores. Scores for cultural relevance and usefulness of the program were high. A sample of material was found to be interesting, familiar, and acceptable. All practitioners (100 %) expressed a need to implement a parenting program in their community. Only being female and greater hours of consultation per week were associated with greater acceptability. These results have the potential to inform implementation efforts in Panama and the study offers a methodology which can be used to explore the relevance of other programs in other low-resource settings.

A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine. / The role of enzyme‐assisted subcutaneous fluid administration (EASFA) in treating mild to moderate dehydration in pediatrics, geriatrics, and palliative care has been studied in developed countries. However, it has historically been underutilized due to widely available health care and alternative treatments, namely peripheral intravenous (IV) fluid administration. Fluid infusions in the subcutaneous tissue have a low risk of infection, are easy to administer, and have wide potential use. The use of EASFA in low resource settings to treat those with difficult IV access or where skilled healthcare workers are not as readily available could prove to be a live saving measure in many situations, including the care of patients in remote areas of the world, mass casualty events, or other disasters. Our objective was to determine if EASFA is a valid and appropriate technique to utilize in pediatric and elderly patients, and evaluate if it could be a safe and efficient way to provide fluid resuscitation in low resource settings. For this systematic review MEDLINE and Cochrane Library were searched from January 1950 to December 2015 to recover all available literature relevant to this topic. Studies that met the inclusion criteria were analyzed using Cohen’s D. This was calculated using the mean difference between intervention and control divided by the pooled standard deviation. For dichotomous outcome of the placement success rate the odds ratios were calculated with 95% confidence intervals. In reviewing 7 articles using Cohen’s D to compare mean differences to determine effect size, we found that catheter placement success rates and infusion rates were similar between EASFA and peripheral intravenous fluid administration. Additionally, it was found that the odds of correct initial needle placement was 7.19 times higher in EASFA versus intravenous administration. EASFA is a comparable alternative to intravenous fluid administration when delivering fluids to pediatric and elderly patients with mild to moderate dehydration. While infusion rates and total volume of fluids administered were similar, the high rate of success with placement of the subcutaneous catheter proves it to be more useful in some situations. Venous cannulation is difficult, even for a trained healthcare provider, and the ease of placement of subcutaneous catheters makes training lay people to administer subcutaneous fluids a possibility. Additionally, this type of fluid administration may lead to less psychological trauma to a child from multiple needle sticks, while still achieving a similar outcome of effective volume replacement. Based on the results of this study, further research is needed to evaluate the effectiveness of utilizing EASFA in low resource settings.

Tesis por compendio / [ES] La detección del lenguaje abusivo es una tarea que se ha vuelto cada vez más importante en la era digital moderna, donde la comunicación se produce a través de diversas plataformas en línea. El aumento de las interacciones en estas plataformas ha provocado un aumento de la aparición del lenguaje abusivo. Abordar dicho contenido es crucial para mantener un entorno en línea seguro e inclusivo. Sin embargo, esta tarea enfrenta varios desafíos que la convierten en un área compleja y que demanda de continua investigación y desarrollo. En particular, detectar lenguaje abusivo en entornos con escasez de datos presenta desafíos adicionales debido a que el desarrollo de sistemas automáticos precisos a menudo requiere de grandes conjuntos de datos anotados. En esta tesis investigamos diferentes aspectos de la detección del lenguaje abusivo, prestando especial atención a entornos con datos limitados. Primero, estudiamos el sesgo hacia palabras clave abusivas en modelos entrenados para la detección del lenguaje abusivo. Con este propósito, proponemos dos métodos para extraer palabras clave potencialmente abusivas de colecciones de textos. Luego evaluamos el sesgo hacia las palabras clave extraídas y cómo se puede modificar este sesgo para influir en el rendimiento de la detección del lenguaje abusivo. El análisis y las conclusiones de este trabajo revelan evidencia de que es posible mitigar el sesgo y que dicha reducción puede afectar positivamente el desempeño de los modelos. Sin embargo, notamos que no es posible establecer una correspondencia similar entre la variación del sesgo y el desempeño de los modelos cuando hay escasez datos con las técnicas de reducción del sesgo estudiadas. En segundo lugar, investigamos el uso de redes neuronales basadas en grafos para detectar lenguaje abusivo. Por un lado, proponemos una estrategia de representación de textos diseñada con el objetivo de obtener un espacio de representación en el que los textos abusivos puedan distinguirse fácilmente de otros textos. Por otro lado, evaluamos la capacidad de redes neuronales convolucionales basadas en grafos para clasificar textos abusivos. La siguiente parte de nuestra investigación se centra en analizar cómo el aumento de datos puede influir en el rendimiento de la detección del lenguaje abusivo. Para ello, investigamos dos técnicas bien conocidas basadas en el principio de minimización del riesgo en la vecindad de instancias originales y proponemos una variante para una de ellas. Además, evaluamos técnicas simples basadas en el reemplazo de sinónimos, inserción aleatoria, intercambio aleatorio y eliminación aleatoria de palabras. Las contribuciones de esta tesis ponen de manifiesto el potencial de las redes neuronales basadas en grafos y de las técnicas de aumento de datos para mejorar la detección del lenguaje abusivo, especialmente cuando hay limitación de datos. Estas contribuciones han sido publicadas en conferencias y revistas internacionales. / [CA] La detecció del llenguatge abusiu és una tasca que s'ha tornat cada vegada més important en l'era digital moderna, on la comunicació es produïx a través de diverses plataformes en línia. L'augment de les interaccions en estes plataformes ha provocat un augment de l'aparició de llenguatge abusiu. Abordar este contingut és crucial per a mantindre un entorn en línia segur i inclusiu. No obstant això, esta tasca enfronta diversos desafiaments que la convertixen en una àrea complexa i contínua de recerca i desenvolupament. En particular, detectar llenguatge abusiu en entorns amb escassetat de dades presenta desafiaments addicionals pel fet que el desenvolupament de sistemes automàtics precisos sovint requerix de grans conjunts de dades anotades. En esta tesi investiguem diferents aspectes de la detecció del llenguatge abusiu, prestant especial atenció a entorns amb dades limitades. Primer, estudiem el biaix cap a paraules clau abusives en models entrenats per a la detecció de llenguatge abusiu. Amb este propòsit, proposem dos mètodes per a extraure paraules clau potencialment abusives de col·leccions de textos. Després avaluem el biaix cap a les paraules clau extretes i com es pot modificar este biaix per a influir en el rendiment de la detecció de llenguatge abusiu. L'anàlisi i les conclusions d'este treball revelen evidència que és possible mitigar el biaix i que esta reducció pot afectar positivament l'acompliment dels models. No obstant això, notem que no és possible establir una correspondència similar entre la variació del biaix i l'acompliment dels models quan hi ha escassetat dades amb les tècniques de reducció del biaix estudiades. En segon lloc, investiguem l'ús de xarxes neuronals basades en grafs per a detectar llenguatge abusiu. D'una banda, proposem una estratègia de representació textual dissenyada amb l'objectiu d'obtindre un espai de representació en el qual els textos abusius puguen distingir-se fàcilment d'altres textos. D'altra banda, avaluem la capacitat de models basats en xarxes neuronals convolucionals basades en grafs per a classificar textos abusius. La següent part de la nostra investigació se centra en analitzar com l'augment de dades pot influir en el rendiment de la detecció del llenguatge abusiu. Per a això, investiguem dues tècniques ben conegudes basades en el principi de minimització del risc en el veïnatge d'instàncies originals i proposem una variant per a una d'elles. A més, avaluem tècniques simples basades en el reemplaçament de sinònims, inserció aleatòria, intercanvi aleatori i eliminació aleatòria de paraules. Les contribucions d'esta tesi destaquen el potencial de les xarxes neuronals basades en grafs i de les tècniques d'augment de dades per a millorar la detecció del llenguatge abusiu, especialment quan hi ha limitació de dades. Estes contribucions han sigut publicades en revistes i conferències internacionals. / [EN] Abusive language detection is a task that has become increasingly important in the modern digital age, where communication takes place via various online platforms. The increase in online interactions has led to an increase in the occurrence of abusive language. Addressing such content is crucial to maintaining a safe and inclusive online environment. However, this task faces several challenges that make it a complex and ongoing area of research and development. In particular, detecting abusive language in environments with sparse data poses an additional challenge, since the development of accurate automated systems often requires large annotated datasets. In this thesis we investigate different aspects of abusive language detection, paying particular attention to environments with limited data. First, we study the bias toward abusive keywords in models trained for abusive language detection. To this end, we propose two methods for extracting potentially abusive keywords from datasets. We then evaluate the bias toward the extracted keywords and how this bias can be modified in order to influence abusive language detection performance. The analysis and conclusions of this work reveal evidence that it is possible to mitigate the bias and that such a reduction can positively affect the performance of the models. However, we notice that it is not possible to establish a similar correspondence between bias mitigation and model performance in low-resource settings with the studied bias mitigation techniques. Second, we investigate the use of models based on graph neural networks to detect abusive language. On the one hand, we propose a text representation framework designed with the aim of obtaining a representation space in which abusive texts can be easily distinguished from other texts. On the other hand, we evaluate the ability of models based on convolutional graph neural networks to classify abusive texts. The next part of our research focuses on analyzing how data augmentation can influence the performance of abusive language detection. To this end, we investigate two well-known techniques based on the principle of vicinal risk minimization and propose a variant for one of them. In addition, we evaluate simple techniques based on the operations of synonym replacement, random insertion, random swap, and random deletion. The contributions of this thesis highlight the potential of models based on graph neural networks and data augmentation techniques to improve abusive language detection, especially in low-resource settings. These contributions have been published in several international conferences and journals. / This research work was partially funded by the Spanish Ministry of Science and Innovation under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). The authors thank also the EU-FEDER Comunitat Valenciana 2014-2020 grant IDIFEDER/2018/025. This work was done in the framework of the research project on Fairness and Transparency for equitable NLP applications in social media, funded by MCIN/AEI/10.13039/501100011033 and by ERDF, EU A way of making EuropePI. FairTransNLP research project (PID2021-124361OB-C31) funded by MCIN/AEI/10.13039/501100011033 and by ERDF, EU A way of making Europe. Part of the work presented in this article was performed during the first author’s research visit to the University of Mannheim, supported through a Contact Fellowship awarded by the DAAD scholarship program “STIBET Doktoranden”. / Peña Sarracén, GLDL. (2024). On the Keyword Extraction and Bias Analysis, Graph-based Exploration and Data Augmentation for Abusive Language Detection in Low-Resource Settings [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203266 / Compendio