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1.
Machine Learning with Applications ; JOUR: 100427,
Article in English | ScienceDirect | ID: covidwho-2105601

ABSTRACT

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitable benchmark for developing such algorithms. Collecting images with measured ground-truth pair-wise distances using different camera settings is cumbersome. Moreover, performance evaluation for these algorithms is not straightforward, and there is no widely accepted evaluation protocol. In this paper, we provide an image dataset with measured pair-wise social distances under different camera positions and settings. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate such algorithms. We also propose an automatic social distance estimation method that can be applied on general photo collections. Our method is a hybrid method that combines deep learning-based object detection and human pose estimation with projective geometry. The method can be applied on uncalibrated single images with known focal length and sensor size. The results on our benchmark are encouraging with 91% human detection rate and only 38.24% average relative distance estimation error among the detected people.

2.
Cogent Business & Management ; 9(1), 2022.
Article in English | Web of Science | ID: covidwho-2070057

ABSTRACT

The current COVID-19 pandemic is a worldwide challenge, so organizationsneed to create innovative management to drive effective performance. Effective performance can be achieved, among others, by creating interpersonal trust between employees and supervisors. Therefore, examining the antecedents of this interpersonal trust is an important study. The purpose of this study is to examine the effect of formal performance evaluation system and intrinsic religiosity on a person's trust toward their superiors. Data were obtained using an online questionnaire survey method. A total of 222 full-time faculty members of economics and business from 24 Christian higher education institutions across 14 provinces in Indonesia participated in this study. The data were analyzed using hierarchical regression analysis. This study provides evidence on the positive effect of formal performance evaluation systems on trust based on social exchange theory. This study also reveals that intrinsic religiosity positively influences subordinates' trust in their superiors. This study is, to the best of our knowledge, the first to introduce supernatural monitoring hypothesis as a theoretical base to examine the effect of intrinsic religiosity on trust. Further, this study provides evidence that supernatural monitoring hypothesis is the complementing theory of social exchange theory in building trust.

3.
International Journal of Research in Business and Social Science ; 11(6):300-306, 2022.
Article in English | ProQuest Central | ID: covidwho-2067464

ABSTRACT

Economists, academics, and practitioners are worried that the COVID-19 pandemic has a negative impact on the banking industry in the world, especially in Indonesia, such as other events that have occurred in the world in the last 25 years, namely the Asian Financial Crisis in 1997, Severe Acute Respiratory Syndrome (SARS) in 2003, and the Global Financial Crisis in 2008 (Overby et al., 2004;Hill and Shiraishi, 2007;Winoto and Bustaman, 2020). Rahmi and Sumirat (2021) used commercial banks for their research data and stated that the COVID-19 pandemic has a negative effect on ROA. [...]this research was conducted, studies examining the impact of the COVID-19 pandemic on the performance of rural banks in Indonesia had never been done. [...]this study proposes several research hypotheses as follows: H1: the COVID-19 pandemic has a negative effect on CAR H2: the COVID-19 pandemic has a positive effect on NPL H3: the COVID-19 pandemic has a negative effect on ROA H4: the COVID-19 pandemic has a negative effect on CR Research and Methodology Data and sample The sample selection used the purposive sampling method by selecting rural banks that had complete data, selecting rural banks operating before the COVID-19 pandemic occurred and were still operating until this research was conducted, and selecting rural banks from the largest bank size to the smallest.

4.
Wireless Communications & Mobile Computing (Online) ; 2022, 2022.
Article in English | ProQuest Central | ID: covidwho-2064323

ABSTRACT

Contactless authentication is crucial to keep social distance and prevent bacterial infection. However, existing authentication approaches, such as fingerprinting and face recognition, leverage sensors to verify static biometric features. They either increase the probability of indirect infection or inconvenience the users wearing masks. To tackle these problems, we propose a contactless behavioral biometric authentication mechanism that makes use of heterogeneous sensors. We conduct a preliminary study to demonstrate the feasibility of finger snapping as a natural biometric behavior. A prototype-SnapUnlock system was designed and implemented for further real-world evaluation in various environments. SnapUnlock adopts the principle of contrastive-based representation learning to effectively encode the features of heterogeneous readings. With the representations learned, enrolled samples trained with the classifier can achieve superior performances. We extensively evaluate SnapUnlock involving 50 participants in different experimental settings. The results show that SnapUnlock can achieve a 4.2% average false reject rate and 0.73% average false accept rate. Even in a noisy environment, our system performs similar results.

5.
Multiple Criteria Decision Making ; : 101-113, 2022.
Article in English | Scopus | ID: covidwho-2059683

ABSTRACT

Healthcare service demand during the COVID-19 pandemic has significantly increased compared to the pre-pandemic period. In some cases, it is known that the hospitals are insufficient and the health system were at the point of collapse. In order to provide a better service to patients, it is very important to measure and improve current performance. In this study, intensive care unit’s performance of hospitals under the COVID-19 pandemic was evaluated by using Hesitant Fuzzy MABAC (Multi-Attributive Border Approximation Area Comparison) method. There are different criteria sets in the literature for the evaluation of hospitals. Within the scope of this study;technical competence of the COVID-19 emergency service, patient satisfaction, sufficiency of health personnel, and patient follow-up process criteria were used. Since the comparison was made from the perspective of experts, the ambiguities and hesitations in the evaluations were reflected in ambiguous and fuzzy linguistic terms. In the application section, performance measurement of three hospitals were made. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

6.
Informatica ; 46(2):223-233, 2022.
Article in English | ProQuest Central | ID: covidwho-2056996

ABSTRACT

This work is part of a learning environment that has virtual laboratories that are designed for distant practical work (Tele-PW). In these environments, Tele-PW is performed in two modes: individual and/or collaborative. In this paper, we are concentrating on the tele-collaborative distant practical work model. The work, presented in this paper, proposes an artificial agent called Synchronizer Coordinator Agent (SCA) to synchronize and coordinate the activities of a cognitive process in order to build and maintain a shared conception of a distant practical work between a set of learners. This agent provides certain features such as managing groups of learners, coordinating tasks, shared workspace among members of the Working Group. It is also responsible for the synchronization of workspace agents when they want to manipulate shared virtual objects simultaneously. We have chosen Petri nets to illustrate the principle of granting access to shared objects in the case of simultaneous requests. Experimental results show the effectiveness, of the artificial agent within any tele-collaborative/tele-cooperative learning situation. Several situations describe the geographical/time dispersion of learners and tutors in our system are considered during the system design phase.

7.
International Journal of Advances in Intelligent Informatics ; 8(2):199-209, 2022.
Article in English | ProQuest Central | ID: covidwho-2056920

ABSTRACT

Chest radiography (CXR) image is usually required for lung severity assessment. However, chest X-rays in COVID-19 interpretation is required expert radiologists' knowledge. This study aims to improve the COVID-19 X-ray image classification using feature selection technique by the regression mutual information deep convolution neuron networks (RMI Deep-CNNs). The dataset consists of 219 COVID-19, 500 viral pneumonias, and 500 normal chest X-ray images. CXR images were comprehensively pre-trained using DCNNs to extract the very large image features, then, the feature selection could reduce the complexity of a model and reduce the model overfitting. Therefore, the critical features were selected using regression mutual information followed by the fully connected with softmax layer for classification. For the classification of two alternative systems, these networks were compared (ResNet152V2 and InceptionV3). The classification performance for both schemes were 92.21%, 100%, 90% and 91.39%, 100%, 82.50%, respectively. In addition, RMI Deep-CNNs not only improve the accuracy but also reduce trainable features by over 80%. This approach tends to significantly improve the computation time and model accuracy for COVID-19 classification.

8.
Review of Economic Perspectives ; 22(3):219-239, 2022.
Article in Czech | ProQuest Central | ID: covidwho-2054855

ABSTRACT

The main purpose of this study is to investigate the best predictor of firm performance among different proxies. A sample of 287 Czech firms was taken from automobile, construction, and manufacturing sectors. Panel data of the firms was acquired from the Albertina database for the time period from 2016 to 2020. Three different proxies of firm performance, return of assets (RoA), return of equity (RoE), and return of capital employed (RoCE) were used as dependent variables. Including three proxies of firm’s performance, 16 financial ratios were measured based on the previous literature. A machine learning-based decision tree algorithm, Chi-squared Automatic Interaction Detector (CHAID), was deployed to gauge each proxy’s efficacy and examine the best proxy of the firm performance. A partitioning rule of 70:30 was maintained, which implied that 70% of the dataset was used for training and the remaining 30% for testing. The results revealed that return on assets (RoA) was detected to be a robust proxy to predict financial performance among the targeted indicators. The results and the methodology will be useful for policy-makers, stakeholders, academics and managers to take strategic business decisions and forecast financial performance.

9.
Clin Microbiol Infect ; 2022 Aug 24.
Article in English | MEDLINE | ID: covidwho-2049052

ABSTRACT

OBJECTIVES: Antigen rapid diagnostic tests (RDTs) for SARS coronavirus 2 (SARS-CoV-2) are quick, widely available, and inexpensive. Consequently, RDTs have been established as an alternative and additional diagnostic strategy to quantitative reverse transcription polymerase chain reaction (RT-qPCR). However, reliable clinical and large-scale performance data specific to a SARS-CoV-2 virus variant of concern (VOC) are limited, especially for the Omicron VOC. The aim of this study was to compare RDT performance among different VOCs. METHODS: This single-centre prospective performance assessment compared RDTs from three manufacturers (NADAL, Panbio, MEDsan) with RT-qPCR including deduced standardized viral load from oropharyngeal swabs for detection of SARS-CoV-2 in a clinical point-of-care setting from November 2020 to January 2022. RESULTS: Among 35 479 RDT/RT-qPCR tandems taken from 26 940 individuals, 164 of the 426 SARS-CoV-2 positive samples tested true positive with an RDT corresponding to an RDT sensitivity of 38.50% (95% CI, 34.00-43.20%), with an overall specificity of 99.67% (95% CI, 99.60-99.72%). RDT sensitivity depended on viral load, with decreasing sensitivity accompanied by descending viral load. VOC-dependent sensitivity assessment showed a sensitivity of 42.86% (95% CI, 32.82-53.52%) for the wild-type SARS-CoV-2, 43.42% (95% CI, 32.86-54.61%) for the Alpha VOC, 37.67% (95% CI, 30.22-45.75%) for the Delta VOC, and 33.67% (95% CI, 25.09-43.49%) for the Omicron VOC. Sensitivity in samples with high viral loads of ≥106 SARS-CoV-2 RNA copies per mL was significantly lower in the Omicron VOC (50.00%; 95% CI, 36.12-63.88%) than in the wild-type SARS-CoV-2 (79.31%; 95% CI, 61.61-90.15%; p 0.015). DISCUSSION: RDT sensitivity for detection of the Omicron VOC is reduced in individuals infected with a high viral load, which curtails the effectiveness of RDTs. This aspect furthert: limits the use of RDTs, although RDTs are still an irreplaceable diagnostic tool for rapid, economic point-of-care and extensive SARS-CoV-2 screening.

10.
Drug Safety ; 45(10):1119, 2022.
Article in English | ProQuest Central | ID: covidwho-2045242

ABSTRACT

Introduction: During the recent covid-19 vaccination campaign, the number of ICSRs reported by patients and professionals has dramatically increased, reaching up to almost 1 M declarations only in Europe (EMA numbers). To deal with such growing amount of data, Synapse Medicine®, in collaboration with The French National Agency for Medicines and Health Products Safety (ANSM), have developed an artificial intelligence (AI) tool, the Medication Shield, which, based on a natural language processing algorithm, is able to detect ADRs from patients' reports and to code them into an appropriate MedDRA preferred term (PT). Before the covid-19 pandemic, this system was successful in detecting ADRs from the patient reports declared through the French web national reporting system (1, 2). However, how it behaves in conditions of higher reporting flow rate is unknown at present. Objective: To evaluate the performance of the Medication Shield in detecting vaccine-related ADRs from patients' ICSRs declared across the covid-19 vaccination campaign. Methods: A machine learning (ML) pipeline composed by a light Gradient Boosting Machine ensemble model was employed to detect and code covid-19 vaccine-related ADRs from patients' ICSRs declared through the web reporting system during the vaccination campaign (Jan 2021-Apr 2022). The encoding of regional pharmacovigilance centers was employed as the reference ground truth to train the algorithm in a supervised manner. Moreover, a panel of three pharmacologists, with significant experience in ADRs encoding, was set-up to perform a case-by-case analysis of 200 hundreds reports for which the algorithm provided improper encoding. Results: Overall, 65.191 ICSRs were extracted and used to train our ML algorithm. Of this, 54.987 were employed to validate the system. Importantly, almost 86% of the ICSRs were related to covid vaccines. Because the percentage of newly reported ADRs increased over time and was higher for vaccine than not-vaccine related reports, we split the training and validation sets in batches with similar ADRs distribution. Performance evaluation is currently under process. Initial feedbacks from the analysis performed by the experts are showing an uneven distribution of false positive and false negative across samples. Results from the other experts are needed to confirm this finding. Conclusion: The core findings of this study will be gathered in the forthcoming weeks and be ready for the ISoP meeting in September. This work will provide new insights about the effectiveness of deploying AI as a support to treat real world data in a context of sanitary crisis.

11.
Front Med (Lausanne) ; 9: 871885, 2022.
Article in English | MEDLINE | ID: covidwho-2039684

ABSTRACT

COVID-19 is a contagious disease that has infected over half a billion people worldwide. Due to the rapid spread of the virus, countries are facing challenges to cope with the infection growth. In particular, healthcare organizations face difficulties efficiently provisioning medical staff, equipment, hospital beds, and quarantine centers. Machine and deep learning models have been used to predict infections, but the selection of the model is challenging for a data analyst. This paper proposes an automated Artificial Intelligence-enabled proactive preparedness real-time system that selects a learning model based on the temporal distribution of the evolution of infection. The proposed system integrates a novel methodology in determining the suitable learning model, producing an accurate forecasting algorithm with no human intervention. Numerical experiments and comparative analysis were carried out between our proposed and state-of-the-art approaches. The results show that the proposed system predicts infections with 72.1% less Mean Absolute Percentage Error (MAPE) and 65.2% lower Root Mean Square Error (RMSE) on average than state-of-the-art approaches.

12.
Clin Chem Lab Med ; 2022 Sep 19.
Article in English | MEDLINE | ID: covidwho-2039466

ABSTRACT

OBJECTIVES: Since December 2019, the worldwide public health has been threatened by a severe acute respiratory syndrome caused by Coronavirus-2. From the beginning, a turning point has been the identification of new cases of infection, in order to minimize the virus spreading among the population. For this reason, it was necessary introducing a panel of tests able to identify positive cases, which became crucial for all countries. METHODS: As a Regional Reference Centre, the CRQ Laboratory (Regional Laboratory for the Quality Control) developed and conducted an External Quality Assessment (EQA) panel of assay, so as to evaluate the quality of real-time reverse transcription polymerase chain reaction (PCR), which were used by 62 Sicilian laboratories, previously authorized to issue certificates for the COVID-19 diagnosis, on behalf of the Public Health Service. RESULTS: The qualitative performance test was based on pooled samples with different viral loads of SARS-CoV-2 or human Coronavirus OC43. 75% of the participating laboratories tested all core samples correctly, while the remaining 25% interpreted incorrectly the EQA exercise samples matching negatively the standards required. CONCLUSIONS: Subsequent inspection visits confirmed the issue of incorrect positive and negative certifications for COVID-19 by private and public laboratories, despite the possession of the authorization requirements currently provided for by current regulations, with a significant impact on the SSR.

13.
International Journal of Computing and Digital Systems ; 12(1):29-43, 2022.
Article in English | Scopus | ID: covidwho-2025570

ABSTRACT

Performance evaluation is a critical part of deep learning (DL) that requires careful conduct to enhance confidence and reliability. Several metrics exist to evaluate DL models, however, choosing one for a given model is not trivial, since it is not a one-fit-all solution. Practically, accuracy is the most popularly used evaluation metric for capsule networks (CapsNets). This is problematic for sensitive applications (e.g. health), since accuracy is overly optimistic in the presence of class imbalance, and does not permit the exact reporting of a model’s risk of bias and potential usefulness. This paper, therefore, aims at demonstrating the usefulness of other metrics for performance evaluation as well as interpretability through the implementation of a custom capsule model. The metrics are effective in measuring the real performance of the models in terms of accuracy (93.03% for proposed model), number of parameters (≈ 4 million fewer for proposed model), ability to scale and fail-safe, and the effectiveness of the routing process when evaluated on the datasets. Evaluating a CapsNet model with all these metrics has the potential to enhance the practitioner’s confidence and also improve model understandability and reliability. © 2022 University of Bahrain. All rights reserved.

14.
Gülhane Tip Dergisi ; 64(3):274-280, 2022.
Article in English | ProQuest Central | ID: covidwho-2024907

ABSTRACT

Aims: We evaluated the knowledge, attitudes, and behaviors of residents and specialists working in tertiary healthcare institutions about drug allergy. Methods: Residents and specialist medical doctors working at a tertiary health institution were included in the study. A questionnaire consisting of questions evaluating occupational and demographic characteristics, knowledge, attitudes, and behaviors about drug allergy was prepared and administered to the participants. Result: Only 26 (21.3%) of the participants had attended any training on drug allergies. Of the participants, 73 (59.8%) felt competent in taking and interpreting an accurate allergy history for drugs. Of the participants, 107 (87.7%) knew that it is often impossible to reach a definite conclusion about drug allergy based on anamnesis alone. Only half of the participants stated that they could spare enough time for detailed anamnesis about drug allergy in their daily practice. Only 19 (15.6%) of the participants stated that they referred patients with suspected drug allergies to an allergist at a rate of 90-100%. When the answers of the assistant and specialists were compared;the proportion of respondents to the question of the most important drug classes responsible for allergic reactions, including antibiotics and aspirin/other NSAIDs (28.9% vs. 67.7%;p<.001). And the rate of those who responded corticosteroids+antihistamines+adrenaline to the question of the most recommended drug classes to treat drug allergies was found to be higher in specialist physicians (19.8% vs. 71%;p<.001). Conclusion: This cross-sectional study showed a low level of awareness, knowledge, and competency in the management of drug allergies among residents and specialists from different fields.

15.
Systems ; 10(4):106, 2022.
Article in English | ProQuest Central | ID: covidwho-2024226

ABSTRACT

Evaluating pharmaceutical enterprises with sustainable and high-quality development ability (SHQDA) can not only provide strategies for the pharmaceutical management department in formulating enterprise development plans, but also provide suggestions and guidance for enterprises to enhance their core competitiveness. Nevertheless, the prior research possesses several deficiencies in coping with the assessment of enterprises with SHQDA under uncertain environments to predict the psychological behavior of the evaluator and the correlation among the evaluation criteria. To conquer the aforementioned defects, we propose an integrated framework for rating pharmaceutical enterprises that incorporates regret theory, measurement alternatives and ranking based on the compromise solution (MARCOS) and Heronian mean operating within a single-value neutrosophic set (SVNS) environment. First, the single-valued neutrosophic number (SVNN) is employed to portray the assessment information of experts. Then, a novel single-valued neutrosophic score function is presented to enhance the rationality of the SVNN comparison. Next, a combined criteria weight model is constructed by synthesizing the best and worst method (BWM) and criteria importance through intercriteria correlation (CRITIC) approach to attain more reasonable and credible weight information. Furthermore, the integrated assessment framework combining regret theory-MARCOS method and Heronian mean operator is put forward to assess and select the enterprises with SHQDA under a single-valued neutrosophic setting. Ultimately, an empirical concerning the pharmaceutical enterprises assessment is presented within SVNS to illustrate the usefulness and effectiveness of the presented SVNS regret theory-MARCOS method. Thereafter, the sensitivity analysis and comparison analysis are implemented to provide evidence for the rationality and superiority of the proposed method.

16.
Sustainability ; 14(17):10644, 2022.
Article in English | ProQuest Central | ID: covidwho-2024187

ABSTRACT

This study aims to develop a framework that enables green marketing practices to regulate the performance evaluation criteria (GFBPC) of consumers and green furniture brands in the Marketing 4.0 period and to prioritize green furniture brands. The first stage was the literature review and decision-making group;it included GFBPC and the selection of three green furniture brands with the highest market value in Turkey. We then applied AHP to determine and prioritize benchmark weights, and TOPSIS to rank the performances of selected brands by GFBPC. We performed SA to test the accuracy of the findings. The results revealed that the Co-creation of Value and Pricing criteria have the highest value, and “Brand Y” is the best. Among the evaluation contributions of the study are a new understanding of green furniture performance criteria, and an integrated framework for new application methods for green marketing. With the Marketing 4.0 period, it is among the first of its kind to offer sustainable solutions to evaluate green marketing practices and increase the performance of green furniture brands in this regard. The results can help furniture industry stakeholders understand ways to compete in the green market and sustainable development.

17.
Sustainability Accounting, Management and Policy Journal ; 13(5):1033-1059, 2022.
Article in English | ProQuest Central | ID: covidwho-2018575

ABSTRACT

Purpose>The purpose of this study is to provide insights into how accounting and accountability systems can contribute to transforming metrics used thus far in research performance evaluation. New metrics are needed to increase research impact on the challenges addressed by science. In particular, we document and reflect on accounting transformations towards responsible research and innovation (RRI).Design/methodology/approach>The study draws on the European H2020 MULTI-ACT research project that focuses on the development of a collective research impact framework in the area of health research. We document, analyse and report our engagement in this project, which also included research funders, patient organizations, health researchers, accounting practitioners and health care providers. Drawing on RRI, Mode 2 knowledge production and accounting performativity, we inquire into the potential of accounting technologies to foster knowledge production and increase research impact.Findings>The study shows how the engagement of accounting with other disciplines enables the development of new and relevant forms of research impact assessment. We document how accounting can be mobilised for the development of new forms of research impact assessment (i.e. indicators that evaluate key accountability dimensions to promote RRI) and how it helps to overcome the difficulties that can emerge during this process. We also show how the design of multiple accountabilities’ indicators, although chronically partial, produced a generative interrogation and discussion about how to translate RRI to research assessment in a workable setting, and the pivotal role of certain circumstances (e.g. the presence of authoritative actors) that appear during the knowledge production process for creating these generative opportunities.Practical implications>This study illustrates the key role of accounts in the generation of knowledge. It also shows the value of considering the stakes of all affected actors in devising fruitful accounting approaches. This collective perspective is timely in the accounting discipline and could foster the connection between academics and practice which is so far under-reported. This perspective should be useful for policymakers such as the European Union and managers in the design of new policies, initiatives and practices.Social implications>Discussing and devising appropriate research assessment frameworks is strategic for the maximization of the social impact of research results. Accounting has a key role to play in optimizing a sustainable return on investment in research.Originality/value>How to assess research impact in a more balanced way is in an early stage of development. The study provides empirical and practical material to advance further work and develop its potential to broaden the conceptualization of accountability.

18.
Diagnostics (Basel) ; 12(9)2022 Aug 30.
Article in English | MEDLINE | ID: covidwho-2005963

ABSTRACT

Low-dose exposure and work convenience are required for mobile X-ray systems during the COVID-19 pandemic. We investigated a novel X-ray detector (FXRD-4343FAW, VIEWORKS, Anyang, Korea) composed of a thin-film transistor based on amorphous silicon with a flexible plastic substrate. This detector is composed of a thallium-doped cesium iodide scintillator with a pixel size of 99 µm, pixel matrix of 4316 × 4316, and weight of 2.95 kg. The proposed detector has the advantages of high-noise characteristics and low weight, which provide patients and workers with an advantage in terms of the dose and work efficiency, respectively. We performed a quantitative evaluation and an experiment to demonstrate its viability. The modulation transfer function, noise power spectrum, and detective quantum efficiency were identified using the proposed and comparative detectors, according to the International Electrotechnical Commission protocol. Additionally, the contrast-to-noise ratio and coefficient of variation were investigated using a human-like phantom. Our results indicate that the proposed detector efficiently increases the image performance in terms of noise characteristics. The detailed performance evaluation demonstrated that the outcomes of the use of the proposed detector confirmed the viability of mobile X-ray devices that require low doses. Consequently, the novel FXRD-4343FAW X-ray detector is expected to improve the image quality and work convenience in extended radiography.

19.
Comput Biol Med ; 149: 106025, 2022 10.
Article in English | MEDLINE | ID: covidwho-2003988

ABSTRACT

The global conflict with the new coronavirus disease (COVID-19) has led to frequent visits to hospitals and medical centers. This significant increase in visits can be severely detrimental to the body of the healthcare system and society if the physical space and hospital staff are not prepared. Given the significance of this issue, this study investigated the performance of a hospital COVID-19 care unit (COCU) in terms of the resilience and motivation of healthcare providers. This paper used a combination of artificial neural networks and statistical methods, in which resilience engineering (RE) and work motivational factors (WMF) were the input and output data of the network, respectively. To collect the required data, we asked the COCU staff to complete a standard questionnaire, after which the best neural network configuration was determined. According to each indicator, sensitivity analysis and statistical tests were performed to evaluate the center's performance. The results indicated that the COCU had the best and worst performance with respect to self-organization and teamwork indicators, respectively. A data envelopment analysis (DEA) method was also used to validate the algorithm, and the SWOT (strengths, weaknesses, opportunities, threats) matrix was eventually presented to recommend appropriate strategies and improve the performance of the studied COCU.


Subject(s)
COVID-19 , Motivation , Delivery of Health Care , Humans , Neural Networks, Computer
20.
Int J Environ Res Public Health ; 19(15)2022 07 26.
Article in English | MEDLINE | ID: covidwho-1994038

ABSTRACT

Organizations worldwide utilize the balanced scorecard (BSC) for their performance evaluation (PE). This research aims to provide a tool that engages health care workers (HCWs) in BSC implementation (BSC-HCW1). Additionally, it seeks to translate and validate it at Palestinian hospitals. In a cross-sectional study, 454 questionnaires were retrieved from 14 hospitals. The composite reliability (CR), interitem correlation (IIC), and corrected item total correlation (CITC) were evaluated. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used. In both EFA and CFA, the scale demonstrated a good level of model fit. All the items had loadings greater than 0.50. All factors passed the discriminant validity. Although certain factors' convergent validity was less than 0.50, their CR, IIC, and CITC were adequate. The final best fit model had nine factors and 28 items in CFA. The BSC-HCW1 is the first self-administered questionnaire to engage HCWs in assessing the BSC dimensions following all applicable rules and regulations. The findings revealed that this instrument's psychometric characteristics were adequate. Therefore, the BSC-HCW1 can be utilized to evaluate BSC perspectives and dimensions. It will help managers highlight which BSC dimension predicts HCW satisfaction and loyalty and examine differences depending on HCWs' and hospital characteristics.


Subject(s)
Health Personnel , Hospitals , Cross-Sectional Studies , Humans , Psychometrics , Reproducibility of Results , Surveys and Questionnaires
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