Nurses and physicians attitudes towards factors related to hospitalized patient safety

Introduction The attitudes of healthcare staff towards patients’ safety, including awareness of the risk for adverse events, are significant elements of an organization’s safety culture. Aim of research To evaluate nurses and physicians’ attitudes towards factors influencing hospitalized patient safety. Materials and methods The research included 606 nurses and 527 physicians employed in surgical and medical wards in 21 Polish hospitals around the country. The Polish adaptation of the Safety Attitudes Questionnaire (SAQ) was used to evaluate the factors influencing attitudes towards patient safety. Results Both nurses and physicians scored highest in stress recognition (SR) (71.6 and 80.86), while they evaluated working conditions (WC) the lowest (45.82 and 52,09). Nurses achieved statistically significantly lower scores compared to physicians in every aspect of the safety attitudes evaluation (p<0.05). The staff working in surgical wards obtained higher scores within stress recognition (SR) compared to the staff working in medical wards (78.12 vs. 73.72; p = 0.001). Overall, positive working conditions and effective teamwork can contribute to improving employees’ attitudes towards patient safety. Conclusions The results help identify unit level vulnerabilities associated with staff attitudes toward patient safety. They underscore the importance of management strategies that account for staff coping with occupational stressors to improve patient safety.

Patient safety attitudes of frontline healthcare workers in Lahore: A multicenter study

Objectives: To evaluate patient safety attitudes of the frontline health workers in a hospital of Lahore, Pakistan. Methods: A self-administered Safety Attitudes Questionnaire (SAQ) survey was deployed in five hospitals across Lahore, Pakistan (July 2019 to June 2020). A total of 1250 consecutive consenting nurses and postgraduate trainee physicians of under five years working experience were recruited. Assessment for each of the six subdomains (teamwork climate, safety climate, job satisfaction, stress recognition, perception of management, working conditions) was done on a 0-100 scale. Multivariate analyses examined their relationship with job cadre (nurses and physicians), duration of respondents’ work experience (< 2 years, 3 - 4 years, > 4 years), and hospital sector (private and public). Results: The response rate was 97% (1212 individuals; 765 nurses, 447 physicians). Nurses scored less than physicians in teamwork climate (-2.4, 95% CI -4.5 – -0.2, p=0.02) and stress recognition (-10.6, 95% CI -13.5 – -7.7, p<0.001), but more in perception of management (4.2, 95% CI 1.5 – 6.8, p=0.002) and working conditions (3.4, 95% CI 0.66 – 6.2, p=0.01). Increasing work experience was related to greater scores in all subdomains. Private hospitals scored generally higher than public ones. Conclusion: Duration of job experience was positively correlated with patient safety attitudes of hospital staff. These finding could serve as the baseline to shape staff perceptions by cadre in both public and private sector hospitals. doi: https://doi.org/10.12669/pjms.38.1.4964 How to cite this:Arkam J, Azhar S, Khan KS, Aman A. Patient safety attitudes of frontline healthcare workers in Lahore: A multicenter study. Pak J Med Sci. 2022;38(1):---------. doi: https://doi.org/10.12669/pjms.38.1.4964 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information

In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.

A Resilient and Hierarchical IoT-based Solution for Stress Monitoring in Everyday Settings

<div><div><div><p>The conventional mental healthcare regime follows a reactive, symptom-focused, and episodic approach in a non-continuous manner, wherein the individual discretely records their biomarker levels or vital signs for a short period prior to a subsequent doctor’s visit. Recognizing that each individual is unique and requires continuous stress monitoring and personally tailored treatment, we propose a holistic hybrid edge-cloud Wearable Internet of Things (WIoT)-based online stress monitoring solution to address the above needs. To eliminate the latency associated with cloud access, appropriate edge models—Spiking Neural Network (SNN), Conditionally Parameterized Convolutions (CondConv), and Support Vector Machine (SVM)—are trained, enabling low-energy real-time stress assessment near the subjects on the spot. This work leverages design-space exploration for the purpose of optimizing the performance and energy efficiency of machine learning inference at the edge. The cloud exploits a novel multimodal matching network model that outperforms six state-of-the-art stress recognition algorithms by 2-7% in terms of accuracy. An offloading decision process is formulated to strike the right balance between accuracy, latency, and energy. By addressing the interplay of edge-cloud, the proposed hierarchical solution leads to a reduction of 77.89% in response time and 78.56% in energy consumption with only a 7.6% drop in accuracy compared to the IoT-Cloud scheme, and it achieves a 5.8% increase in accuracy on average compared to the IoT-Edge scheme.</p></div></div></div>

Stress Recognition Using Facial Landmarks and Cnn (Alexnet)

Stress is a psychological disorder that affects every aspect of life and diminishes the quality of sleep. The strategy presented in this paper for detecting cognitive stress levels using facial landmarks is successful. The major goal of this system was to employ visual technology to detect stress using a machine learning methodology. The novelty of this work lies in the fact that a stress detection system should be as non-invasive as possible for the user. The user tension and these evidences are modelled using machine learning. The computer vision techniques we utilized to extract visual evidences, the machine learning model we used to forecast stress and related parameters, and the active sensing strategy we used to collect the most valuable evidences for efficient stress inference are all discussed. Our findings show that the stress level identified by our method is accurate is consistent with what psychological theories predict. This presents a stress recognition approach based on facial photos and landmarks utilizing AlexNet architecture in this research. It is vital to have a gadget that can collect the appropriate data. The use of a biological signal or a thermal image to identify stress is currently being investigated. To address this limitation, we devised an algorithm that can detect stress in photos taken with a standard camera. We have created DNN that uses facial positions points as input to take advantage of the fact that when a person is worried their eye, mouth, and head movements differ from what they are used to. The suggested algorithm senses stress more efficiently, according to experimental data.

Feature Selection Based on Evolutionary Algorithms for Affective Computing and Stress Recognition

In the area of affective computing, machine learning is used to recognize patterns in datasets based on extracted features. Feature selection is used to select the most relevant features from the large number of extracted features. Conventional feature selection methods are associated with a high computational cost depending on the classifier used. This paper presents a feature selection approach based on evolutionary algorithms using techniques inspired by natural evolution to optimize the computational process. Our method is implemented using an Optimize Selection operator from RapidMiner and is integrated within our previously developed workflow for affective computing and stress recognition from biosignals. The performance is evaluated based on the random forests classifier and a cross validation using our uulmMAC database for machine learning applications. Our proposed approach is faster than the forward selection method at similar recognition rates and does not stop at a local optimum, allowing a promising feature selection alternative in the field of affective computing.