This thesis focuses on student motivation for studies at secondary medical schools, as well as on drivers of motivation of secondary medical school students for Bachelor studies. We wanted to know how motivation of present-day students differs from that of general nurses attending medical schools prior to 2004 and what is the impact of practical training teachers of secondary medical schools on professional motivation of students. Theoretical part describes development of nursing care education since 1945 until now, changes in concept of nursing care as they result from Act no. 96/ 2004 Coll. on ?acquiring and recognition of qualifications for execution of non-doctoral medical professions and activities relating to provision of health care and on amending some related legislation?. Further, we focused on job selection and its factors, and last but not least, on motivation and some motivational drivers. The first objective was to find out what motivates students of secondary medical schools in their first and fourth years. The second objective was to look at motivation of students for General Nurse Bachelor studies. The third objective was to describe motivation of general nurses for studies at secondary medical schools prior to 2004. Finally, the last objective was to find out how practical training teachers of secondary medical schools motivate students for their future profession. In the thesis empirical part, we used methods of qualitative and quantitative research. As far as quantitative research is concerned, anonymous questionnaires were used to collect data. The first population of 102 (100%) respondents included first-year students of three secondary medical schools. The second research population of 100 (100%) respondents included fourth-year students of three secondary medical schools. The third population of 107 (100%) respondents included general nurses of two medium-size hospitals. As far as qualitative research is concerned, questioning method and, in particular, semi-structured interview technique were used. A population of six practical training teachers of two secondary medical schools was involved. These practical training teachers were asked two questions: 1. Is there enough time scheduled for practical training to motivate students? 2. In your opinion, are motivational drivers used in practical training sufficient to get medical assistants involved in the practical training nursing process? Quantitative research has the following results: the first assumption that first-year students were influenced more by external motivational drivers than by internal ones, was not borne out. The second assumption that fourth-year students perceived acquired experience as a more significant motivational driver than external motivation, was not borne out either. The third assumption that motivation of students of secondary medical schools for university studies was depending on experience acquired in practical training was not borne out either. Similarly, the last assumption that motivation of students of secondary medical schools prior to 2004 was depending on the then existing value system, was not borne out either. On the other hand, it is clear from qualitative research that practical training teachers do not have enough time to motivate students and that motivational elements used not always achieve their defined goals. This often results from excessive number of students per group. The above research results will be provided to medical schools for thein self- examination. They may also be used for professional public at specialist seminars or congresses as well as for publication in specialized journals.

Esta tese trata do perfil sócio-demográfico, político e de atividades dos agentes comunitários de saúde que atuam há mais de um ano em unidades de saúde da família, integrantes da rede assistencial de sete municípios da região de saúde de Juiz de Fora - MG. Considera a percepção do próprio agente comunitário de saúde sobre o seu trabalho, a concepção das equipes e coordenadores do programa e a visão dos usuários. Apoia-se, na interação entre as metodologias qualitativa e quantitativa, atuando de forma complementar, sendo seus principais resultados: (i) o perfil social dos agentes no que concerne aos aspectos de residência, segue os requisitos propostos pelo programa nacional. Quanto aos aspectos de liderança e ajuda solidária, somente uma pequena parcela já desenvolvia ações sociais e de solidariedade com sua comunidade, não sendo referência para a mesma, em termos de saúde, (ii) o perfil de atividades se prende às diretrizes e normas ditadas pela política de saúde vigente, emanadas das Secretarias Municipais de Saúde, reforçando a dimensão técnica universalista do seu trabalho.

Esta tese trata do perfil sócio-demográfico, político e de atividades dos agentes comunitários de saúde que atuam há mais de um ano em unidades de saúde da família, integrantes da rede assistencial de sete municípios da região de saúde de Juiz de Fora - MG. Considera a percepção do próprio agente comunitário de saúde sobre o seu trabalho, a concepção das equipes e coordenadores do programa e a visão dos usuários. Apoia-se, na interação entre as metodologias qualitativa e quantitativa, atuando de forma complementar, sendo seus principais resultados: (i) o perfil social dos agentes no que concerne aos aspectos de residência, segue os requisitos propostos pelo programa nacional. Quanto aos aspectos de liderança e ajuda solidária, somente uma pequena parcela já desenvolvia ações sociais e de solidariedade com sua comunidade, não sendo referência para a mesma, em termos de saúde, (ii) o perfil de atividades se prende às diretrizes e normas ditadas pela política de saúde vigente, emanadas das Secretarias Municipais de Saúde, reforçando a dimensão técnica universalista do seu trabalho.

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The use of electronic health records (EHR) from various sources like text, images and time-series data to make predictions or diagnosis have been researchedpreviously. Many previous methods have used separate models either for sepa-rate modalities or for distinct tasks. Recently, models trained to make medicalpredictions using multimodal input have emerged, as a unified approach wouldbe beneficial for health practitioners. We present a single model to make medicalpredictions for several tasks, using diverse input from different modalities. Wedemonstrate the effectiveness of using an autoencoder method to project (EHR)data from three different modalities – images, text and time-series data – into thesmall language model Gemma-2B. 6 projector models are used together with the small language model to perform multi-label prediction for 12 different medicalprediction tasks. Results show that a jointly trained model using asymmetric loss,a loss function that dynamically emphasises positives that are poorly predicted,shows good performance and predicts evenly across tasks.

The integration of Large Language Models (LLMs) such as GPT-4 and Gemini into healthcare, particularly for elderly care, represents a significant opportunity in the use of artificial intelligence in medical settings. This thesis investigates the capabilities of these models to understand and respond to the healthcare needs of older adults effectively. A framework was developed to evaluate their performance, consisting of specifically designed medical scenarios that simulate real-life interactions, prompting strategies to elicit responses and a comprehensive user evaluation to assess technical performance and contextual understanding.  The analysis reveals that while LLMs such as GPT-4 and Gemini exhibit high levels of technical proficiency, their contextual performance shows considerable variability, especially in personalization and handling complex, empathy-driven interactions. In simpler tasks, these models demonstrate appropriate responsiveness, but they struggle with more complex scenarios that require deep medical reasoning and personalized communication.  Despite these challenges, the research highlights the potential of LLMs to significantly enhance healthcare delivery for older adults by providing timely and relevant medical information. However, to realize a truly effective implementation, further development is necessary to improve the models’ ability to engage in meaningful dialogue and understand the nuanced needs of an aging population.  The findings underscore the necessity of actively involving older adults in the development of AI technologies, ensuring that these models are tailored to their specific needs. This includes focusing on enhancing the contextual and demographic awareness of AI systems. Future efforts should focus on enhancing these models by incorporating user feedback from the older population and applying user-centered design principles to improve accessibility and usability. Such improvements will better support the diverse needs of aging populations in healthcare settings, enhancing care delivery for both patients and doctors while maintaining the essential human touch in medical interactions. / Integrationen av stora språkmodeller (LLMs) såsom GPT-4 och Gemini inom sjukvården, särskilt inom äldrevård, representerar betydande möjligheter i användningen av artificiell intelligens i medicinska sammanhang. Denna avhandling undersöker dessa modellers förmåga att förstå och effektivt svara på äldres vårdbehov. För att utvärdera deras prestanda utvecklades ett ramverk bestående av specifikt utformade medicinska situationer som simulerar verkliga interaktioner, strategier för att framkalla relevanta svar från modellerna och en omfattande användarutvärdering för att bedöma både teknisk prestanda och kontextuell förståelse.  Analysen visar att även om LLMs såsom GPT-4 och Gemini visar på hög teknisk prestationsförmåga, är dess kontextuella förmåga mer begränsad, särskilt när det gäller personalisering och hantering av komplexa, empatidrivna interaktioner. Vid enklare uppgifter visar dessa modeller på en lämplig responsivitet, men de utmanas vid mer komplexa scenarier som kräver djup medicinsk resonemang och personlig kommunikation.  Trots dessa utmaningar belyser denna forskning potentialen hos LLMs att väsentligt förbättra vårdleveransen för äldre genom att tillhandahålla aktuell och relevant medicinsk information. Däremot krävs ytterligare utveckling för att verkligen möjliggöra en effektiv implementering, vilket inkluderar att förbättra modellernas förmåga att delta i en meningsfull dialog och förstå de nyanserade behoven hos äldre patienter.  Resultaten från denna avhandling understryker nödvändigheten av att aktivt involvera äldre individer i utvecklingen av AI-teknologier, för att säkerställa att dessa modeller är skräddarsydda för deras specifika behov. Detta inkluderar ett fokus på att förbättra den kontextuella och demografiska medvetenheten hos AI-system. Framtida insatser bör inriktas på att förbättra dessa modeller genom att integrera användarfeedback från äldre populationer och tillämpa principer för användarcentrerad design för att förbättra tillgänglighet och användbarhet. Sådana förbättringar kommer att bättre stödja de mångsidiga behoven hos äldre i vårdsammanhang, förbättra vårdleveransen för både patienter och läkare samtidigt som den väsentliga mänskliga kontakten i medicinska interaktioner bibehålls.