[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123006-en":3,"doc-seo-123006-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123006,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Bridging Technology and Well-being - Applying Machine Learning to Employee Mental Health - An Investigation into the Integration, Impact, and Adoption of Intelligent Solutions","This master’s thesis explores the use of machine learning techniques to tackle business-oriented challenges, centering on strengthening support for employee mental health. The research targets gaps in understanding how machine learning can improve employee well-being and workplace performance. Advanced statistical and probabilistic methods evaluate multiple data sources, such as surveys and IT company records, to inform improvement opportunities. It also examines integration strategies and adoption barriers to guide effective rollout within workplace wellness programs, supporting organizational performance and well-being.","MGI  \nMaster Degree Program in  \nInformation Management  \nBridging Technology and Well-being: Applying Machine Learning to Employee Mental Health  \nAn Investigation into the Integration, Impact, and Adoption of  \nIntelligent Solutions  \nGuilherme Dinis Landeiro Vaz Castel-Branco  \nMaster Thesis  \npresented as a partial requirement for obtaining the Master Degree in Information Management  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nBridging Technology and Well-being: Applying Machine Learning to Employee Mental  \nHealth  \nAn Investigation into the Integration, Impact, and Adoption of Intelligent Solutions  \nby  \nGuilherme Dinis Landeiro Vaz Castel-Branco  \nMaster Thesis presented as a partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Information Systems and Technologies  \nManagement.  \nSupervised by  \nProf. Mauro Castelli, PhD, NOVA Information Management School  \nFebruary, 2024  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nGuilherme Castel-Branco  \nLisbon, 2023  \nDEDICATION  \nMy dear Family, I want to express my heartfelt gratitude for the unwavering support and boundless love you've provided throughout my journey. Your presence has been a constant anchor, guiding me through challenges and celebrating my triumphs.  \nGrandparents, you hold a special place in my heart as a wellspring of wisdom, strength, and inspiration without limits. The stories you've shared, your enduring values, and the legacy of resilience and kindness you've imparted continue to illuminate my path.  \nThis achievement underscores the strength of our family bond and the limitless love that courses through it. Each one of you has played a pivotal role in shaping the person I am today, and for that, I am endlessly thankful.  \nABSTRACT  \nThis master's thesis explores the application of machine learning techniques to address business-related challenges, with a specific focus on enhancing support for employee mental health. The research aims to fill existing gaps in our understanding of how machine learning can positively impact employee well-being and workplace performance. Utilizing advanced statistical and probabilistic methods, the study will analyze a diverse range of data sources, including surveys and IT company records, to gain insights into various aspects of employee mental health and identify opportunities for improvement. The study will critically evaluate the influence of machine learning tools on employee well-being and performance while also investigating effective strategies for seamlessly integrating these tools into existing workplace wellness programs. Additionally, the thesis will address potential barriers to the adoption and utilization of machine learning tools in supporting employee mental health, offering practical solutions to overcome these challenges. This paper aims to contribute to the development of innovative tools and methodologies that foster a supportive work environment conducive to the improvement of employee mental health, thereby enhancing overall organizational performance and well-being.  \nKEYWORDS  \nMachine Learning; Employee Mental Health; Data Analysis; Workplace Wellness; BusinessOriented Issues  \nJEL Codes – C38; C55; I12; I18; J24; J28; M12; M54  \nTABLE OF CONTENTS  \nStatement of Integrity ....................................................................................................... ii  \nDedication .................","cbCaii85NHW5pgGX","https://ap.wps.com/l/cbCaii85NHW5pgGX","pdf",3210003,1,111,"English","en",105,"# 1. Introduction\n## 1.1. Scientific & Research Question\n# 2. Literature review\n## 2.1. Mental Health\n## 2.2. IT Companies\n## 2.3. Predictive Analytics and Machine Learning","[{\"question\":\"What is the core objective of the thesis?\",\"answer\":\"To apply machine learning to enhance support for employee mental health by clarifying how such methods can improve well-being and workplace performance.\"},{\"question\":\"Which data sources does the study plan to analyze?\",\"answer\":\"The thesis analyzes diverse data sources, including surveys and IT company records, to examine aspects of employee mental health and identify improvement opportunities.\"},{\"question\":\"How does the thesis address practical adoption of intelligent solutions?\",\"answer\":\"It evaluates integration strategies for embedding machine learning tools into existing workplace wellness programs and investigates barriers to adoption, proposing practical solutions to overcome them.\"}]","Bridging Technology and Well-being - Applying Machine Learning to Employee Mental Health - An Investigation into the Integration, Impact, and Adoption of 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