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This study examined whether vicarious trauma, the emotional and psychological effects of repeated exposure to others’ traumatic experiences, may affect hospital workers’ physical health. Focusing on nurses and patient care associates at two large hospitals, researchers linked workers’ vicarious trauma symptoms to subsequent health insurance claims for stress-sensitive gastrointestinal conditions. The research suggests that the effects of caring for traumatized patients may extend beyond emotional distress to become a broader occupational health risk.

This study examined whether a hospital safe patient-handling intervention worked equally well for nurses and patient care associates, two groups with different roles, job demands and exposures. The intervention itself went beyond equipment installation to include training, coaching, and organizational supports to help workers actually use the installed equipment. Although the intervention successfully reduced injuries overall, those reductions were concentrated among nurses rather than shared equally across worker groups. The study shows why workplace safety interventions should be judged not only by their overall impact, but also by whether they protect the workers facing the greatest risks.

This past decade has seen unprecedented success in Artificial Intelligence (AI), pushing the frontiers in ways most experts could have never predicted. However, most of this success has come in the form of performing well inside the data distribution the models have been trained with. Out-of-distribution (OOD) generalization still remains the Achilles’ heel of modern AI. In contrast, biological systems exhibit a remarkable ability to adapt to novel situations. This thesis addresses this critical generalization gap, by studying biological and artificial intelligence in tandem. The work presented includes new mathematical frameworks designed to better formalize generalization, behavioral benchmarks to identify the limits of both human and AI generalization capabilities, experiments to identify the underlying mechanisms driving generalization in both brains and neural networks, and engineering solutions to incorporate these findings to improve AI. To this end, this thesis presents scientific contributions made to the fields of Machine Learning, Computer Vision, Computer Graphics, Computational Neuroscience, and Psychophysics. Throughout the thesis, the goal of this work has been to advance our understanding and improve OOD generalization by working at the intersection of biological and artificial intelligence.

The rapid integration of artificial intelligence (AI) in healthcare has outpaced formal training in nursing and medical education. This study explores the perceptions of Master of Nursing students toward AI in clinical learning, and evaluates the use of an AI-enabled Virtual Integrated Patient (VIP) platform in developing clinical reasoning and knowledge.

Part 1 of this mixed-methods study involved surveys and interviews to examine students' views. Quantitative findings showed strong support for AI in education: most students believed AI could enhance learning and should be included in the curriculum. However, concerns were also prominent, with students citing risks such as over-reliance, data security, and the potential loss of humanistic care. Qualitative data echoed these findings—students valued AI for improving efficiency, supporting knowledge acquisition, and enhancing simulation training. Still, they expressed greater trust in human clinicians and stressed the need to build institutional trust in both AI systems and healthcare professionals.

Part 2 focuses on the VIP platform. Students appreciated its adaptability, ability to organise information, and support for structured, case-based learning. The platform was seen as a helpful educational tool, with some students using it to guide clinical reasoning. However, critiques included the repetitive nature of responses, lack of intuitive interaction, and insufficient narrative nuance, leading some to prefer more traditional learning approaches. Analysis of VIP usage showed a weak correlation with final exam scores, but this relationship strengthened among students who engaged with the platform for more than 10 minutes. Multi-regression analysis highlighted variability in learning outcomes, indicating that while the VIP platform may support clinical learning, its effectiveness differs across learners.

This study reveals both enthusiasm and caution among postgraduate nursing students about AI in education. While AI-powered tools like VIP offer innovative ways to promote self-directed, clinically relevant learning, they should be integrated thoughtfully alongside traditional pedagogies. As AI becomes increasingly embedded in healthcare, cultivating both technological fluency and critical thinking is vital to prepare learners for responsible, autonomous clinical practice in a digitally augmented future.

Continuing professional development (CPD) is an intricate form of medical education intended to foster ongoing growth in medical practice. It emphasizes lifelong learning and a holistic approach to professional development, aligning with the evolving needs of patients, healthcare systems, and society. In Chile’s public health sector, the Chilean General Practitioner Training Program (EDF Program), established in 1955, provides the national health workforce with 2,500 general practitioners (GPs) working in rural and vulnerable areas at the primary healthcare level. All are mandated by law to receive CPD, yet planning, implementation, and evaluation deficits have been identified. This research aimed to assess the CPD needs of the EDF Program in the Valparaiso Health Care District, incorporating perspectives from intersectoral stakeholders, EDF GPs, and healthcare network users. A qualitative descriptive approach was selected as the study design. An indicative and purposefully stratified sample was constructed, based on three participant groups from Valparaiso’s local healthcare district. Semi-structured, in-depth interviews were conducted. A data-driven codebook was piloted, refined, and then applied to all transcriptions. Axial coding and constant comparative analysis were systematically conducted using MAXQDA 2024 software. Interviews continued until saturation was achieved, requiring a total of 22 interviews. The study identified CPD benefits, enablers, barriers, and areas for improvement amid ongoing challenges and gaps. Key findings revealed a structural and functional mismatch between the CPD program for EDF physicians and the actual needs of the primary healthcare system they serve. This mismatch, both a technical shortcoming and policy opportunity, has led to calls for a shift in the program’s framework. As a result, several pathways to realign the EDF CPD training with the realities of Chilean primary healthcare have emerged from this research.
This research highlights the inherent complexity of a formal CPD system, as it comprises vast and diverse interrelated components influencing training provision and health workforce development outcomes. Intersectoral collaboration appears vital for advancing the development of practitioners' capacities for primary health care. Future directions include developing a tailored needs assessment tool, which could improve readiness for health workforce CPD innovations.