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Health care has long been viewed as a system built on science, skill, and trust, yet when a clinician becomes a patient, a stark reality emerges: the trust we assume is inherent can often be fractured by systemic disconnection, dehumanization, and a culture that prioritizes protocols over people. In this narrative reflection, I share my personal journey as a nurse who became a patient during the coronavirus disease 2019 pandemic-an experience that exposed not only the failures of care but the heavy burdens placed on health care workers by a system designed to stretch them beyond human limits. This story underscores the urgent need to reconnect with the humanity that lives at the heart of healing and to recognize that rebuilding trust begins not with blaming individuals but with transforming a broken culture.
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Social Sciences Department (Social Work Program), College of Arts and Sciences, Qatar University, Doha, Qatar.
Introduction: Stalking is a serious and often overlooked form of gender-based violence that disproportionately affects adolescent girls. In Pakistan, especially in Sindh, the issue remains under-researched, despite legal reforms aimed at criminalizing such behaviors.
Methods: This study employed Interpretative Phenomenological Analysis (IPA) to explore the lived experiences of 20 adolescent female stalking victims aged 15-17 at a co-educational college in Sindh.
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General 3D Vision-Language Model With Fast Rendering and Pre-Training Vision-Language Alignment.
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Current prevailing vision-language models have achieved remarkable progress in 3D scene understanding while trained in the closed-set setting and with full labels. The major bottleneck for the current robot 3D scene recognition approach for robotic applications is that these models do not have the capacity to recognize any unseen novel classes beyond the training categories in diverse real-world robot applications such as robot manipulation as well as robot navigation. In the meantime, current state-of-the-art 3D scene understanding approaches primarily require a large number of high-quality labels to train neural networks, which merely perform well in a fully supervised manner.
View Article and Find Full Text PDFUnseen But Urgent: Recognizing and Treating Dyspnea in Intubated Critically Ill Patients.
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AP-HP, Groupe Hospitalier Universitaire APHP-Sorbonne Université, Site Pitié-Salpêtrière, Service de Médecine Intensive-Réanimation-SRPR, Département R3S, Paris, France.