Healthcare practitioners' construction of occupational risk during the COVID-19 pandemic

An adaptive ensemble deep learning framework for reliable detection of pandemic patients

Nurses, often considered the backbone of global health services, are disproportionately vulnerable to COVID-19 due to their front-line roles. They conduct essential patient tests, including blood pressure, temperature, and complete blood counts. The pandemic-induced loss of nursing staff has resulted in critical shortages. To address this, robotic solutions offer promising avenues. To solve this problem, we developed an ensemble deep learning (DL) model that uses seven different models to detect patients. Detected images are then used as input for the soft robot, which performs basic assessment tests. In this study, we introduce a deep learning-based approach for nursing soft robots, and propose a novel deep learning model named Deep Ensemble of Adaptive Architectures. Our method is twofold: firstly, an ensemble deep learning technique detects COVID-19 patients; secondly, a soft robot performs basic assessment tests on the identified patients. We evaluate the performance of various deep learning-based object detectors for patient detection, examining implementations of You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), Region-Based Convolutional Neural Network (RCNN), and Region-Based Fully Convolutional Network (R-FCN) on a proprietary dataset comprising 32,668 hospital surveillance images. Our results indicate that while YOLO and VGG facilitate rapid detection, Faster-RCNN (Inception ResNet-v2) and our proposed Ensemble-DL achieve the highest accuracy. Ensemble-DL offers accurate results in a reasonable timeframe, making it apt for patient detection on embedded platforms. Through real-world experiments, our method outperforms baseline approaches (including Faster-RCNN, R-FCN variants, CNN+LSTM, etc.) in terms of both precision and recall. Achieving an impressive accuracy of 98.32%, our deep learning-based model for nursing soft robots presents a significant advancement in the identification and assessment of COVID-19 patients, ultimately enhancing healthcare efficiency and patient care.

Revisiting Vaccine Hesitancy in Residential Care Homes for the Elderly for Pandemic Preparedness: A Lesson from COVID-19

Residents in residential care homes for the elderly (RCHEs) are at high risk of severe illnesses and mortality, while staff have high exposure to intimate care activities. Addressing vaccine hesitancy is crucial to safeguard vaccine uptake in this vulnerable setting, especially amid a pandemic. In response to this, we conducted a cross-sectional survey to measure the level of vaccine hesitancy and to examine its associated factors among residents and staff in RCHEs in Hong Kong. We recruited residents and staff from 31 RCHEs in July-November 2022. Of 204 residents, 9.8% had a higher level of vaccine hesitancy (scored ≥ 4 out of 7, mean = 2.44). Around 7% of the staff (n = 168) showed higher vaccine hesitancy (mean = 2.45). From multi-level regression analyses, higher social loneliness, higher anxiety, poorer cognitive ability, being vaccinated with fewer doses, and lower institutional vaccination rates predicted residents' vaccine hesitancy. Similarly, higher emotional loneliness, higher anxiety, being vaccinated with fewer doses, and working in larger RCHEs predicted staff's vaccine hesitancy. Although the reliance on self-report data and convenience sampling may hamper the generalizability of the results, this study highlighted the importance of addressing the loneliness of residents and staff in RCHEs to combat vaccine hesitancy. Innovative and technology-aided interventions are needed to build social support and ensure social interactions among the residents and staff, especially amid outbreaks.

Self-rated health impact of COVID 19 confinement on inmates in Southeastern of Europe: a qualitative study

INTRODUCTION

The COVID-19 pandemic necessitated the implementation of various measures within closed institutions like prisons to control the spread of the virus. Analyzing the impact of these measures on the health of inmates is crucial from a public health perspective. This study aimed to explore inmates' subjective perception of the COVID-19 lockdown, the implemented measures, their physical self-perception, and their views on the vaccination process.

METHOD

Between April 2021 and January 2022, 27 semi-structured individual interviews and 1 focus group were conducted with inmates in a prison located in northwest Spain. The interviews were conducted in person and audio-recorded. Thematic content analysis was employed, utilizing methodological triangulation to enhance the coherence and rigor of the results.

RESULTS

The analysis revealed two main themes and nine subthemes. The first theme focused on inmates' perception of the implementation of protective measures against COVID-19 within the prison and its impact on their well-being. The second theme explored the pandemic's emotional impact on inmates. All participants reported negative consequences on their health resulting from the measures implemented by the institution to contain the pandemic. However, they acknowledged that measures like lockdowns and mass vaccination helped mitigate the spread of the virus within the prison, contrary to initial expectations.

CONCLUSION

COVID-19 and related measures have directly affected the health of inmates. To improve their health and minimize the impact of pandemic-induced changes, community participation and empowerment of individuals are essential tools, particularly within closed institutions such as prisons.