Job Information Research Articles

Patient Safety Perception Within Hospitals: An Examination of Job Type, Handoffs and Information Exchange, and Hospital Management Support.

The study has 3 aims: (1) to assess to what extent a person's job role relates to their evaluation of patient safety in the hospital setting; 2) to identify the relationship from hospital management aspects, including level of organizational learning-continuous improvement, level of management support, and level of leader support to patient safety perception in the hospital setting; and 3) to examine the relationship between the rating of the ease of information exchange and clinical handoffs and perceived patient safety in the hospital setting. This study used a publicly available, deidentified cross-sectional data set from the 2021 Agency for Healthcare Research and Quality's Survey on Patient Safety Culture Hospital Survey 2.0. Welch's analysis of variance and multiple linear regression were used to examine each factor's impact on patient safety rating. Supervisors had a higher ( P < 0.001) patient safety perception than people in other job types, whereas nurses had a lower ( P < 0.001) patient safety perception than other job types. Level of organizational learning-continuous improvement ( P < 0.001), level of hospital management ( P < 0.001), level of leader support ( P < 0.001), and ease of handoffs and information exchange ( P < 0.001) were positively related to perceived patient safety. This study highlights the importance of identifying the unique issues affecting nurses and supervisors, different from other job types, that may explain their lower patient safety ratings. Findings from this study suggest that it is critical for organizations to focus on initiatives and policies that promote leadership, management, ease in information exchange and handoffs, and continuous learning.

Meaningless Use: Assessing Compliance With a Clinically Meaningless Emergency Department Documentation Requirement.

Introduction A New York State initiative requests that Emergency Department (ED) providers document in the electronic health record (EHR) each admitted patient's employment status and, if applicable, their mode of commute. This initiative diverts them from their primary duties and increases the likelihood they will either disregard the request or input incorrect information to complete the data fields as fast as possible. This study intends to understand how well providers adhere to this regulation, which, while important for society as a whole, has little clinical relevance, especially in the ED, where the focus is to identify and treat emergent conditions. We hypothesized that clinician-collected employment data would contain many more "N/A" responses than registration-collected employment data (the "gold standard"). Methods We took a randomly selected convenience sample of 100 patients admitted from the EDand compared each patient's provider-entered response to the employment data field to the registration-recorded response. The EHR operates such that the "Employment" field must be completed in order to complete the admission electronically. Data fields collected were: last name, first name, date of birth, medical record number, date and time of arrival, date and time of admission, attending physician, resident physician (if there was one), mid-level provider (if there was one), provider-entered employment status, registration-entered employment status, admitting service (eg, Medicine, Surgery, OB/Gyn), and disposition level (eg, ICU). We assessed the percent of employment data that was concordant between the provider's entry and the registration clerk's entry. We also assessed for the potential confounding variable of how busy the ED was at time of admission,as providers may not take ask about employment or enter such data during particularly busy times. Finally, we interviewed providers to elicit reasons they did not enter accurate data. Statistical significance was set a priori at p <0.05. Results One hundred six patients were screened; six were excluded because one of the authors (MR) was their attending physician. For 92 of the remaining 100 patients, providers recorded employment as "N/A," and for eight patients they recorded "retired." For seven of these eight patients, provider entry matched registration entry (87.5% concordance). To adjust for whether how busy the ED was may have impacted the accuracy of data entry, admissions were categorized according to what time of day the patient was admitted. There was no statistically significant correlation between how busy the ED was andaccuracy of data entry.The majority of providers stated they responded "NA" because the employment information was unrelated to the ED visit. Conclusion In New York, for each patient admitted from the ED, the ED provider is requested to enter the patient's job information and, if they commute to work, the method they use. However, this takes providers' attention away from what they should be doing most: diagnosing and treating patients.This study highlights the unintended consequence of requesting data fields that are not clinically relevant and, from the patient and provider perspective, are not good investments of time and energy and distract from the clinical visit. Persons interpreting such clinically irrelevant data should do so with caution, as the results are unlikely to reflect the truth of what the questions intend to determine.

A dynamic scheduling method with Conv-Dueling and generalized representation based on reinforcement learning

In modern industrial manufacturing, there are uncertain dynamic disturbances between processing machines and jobs which will disrupt the original production plan. This research focuses on dynamic multi-objective flexible scheduling problems such as the multi-constraint relationship among machines, jobs, and uncertain disturbance events. The possible disturbance events include job insertion, machine breakdown, and processing time change. The paper proposes a conv-dueling network model, a multidimensional state representation of the job processing information, and multiple scheduling objectives for minimizing makespan and delay time, while maximizing the completion punctuality rate. We design a multidimensional state space that includes job and machine processing information, an efficient and complete intelligent agent scheduling action space, and a compound scheduling reward function that combines the main task and the branch task. The unsupervised training of the network model utilizes the dueling-double-deep Q-network (D3QN) algorithm. Finally, based on the multi-constraint and multi-disturbance production environment information, the multidimensional state representation matrix of the job is used as input and the optimal scheduling rules are output after the feature extraction of the conv-dueling network model and decision making. This study carries out simulation experiments on 50 test cases. The results show the proposed conv-dueling network model can quickly converge for DQN, DDQN, and D3QN algorithms, and has good stability and universality. The experimental results indicate that the scheduling algorithm proposed in this paper outperforms DQN, DDQN, and single scheduling algorithms in all three scheduling objectives. It also demonstrates high robustness and excellent comprehensive scheduling performance.

A Study of Job Failure Prediction on Supercomputers with Application Semantic Enhancement

The powerful computing capabilities of supercomputers play an important role in today’s scientific computing. A large number of high-performance computing jobs are submitted and executed concurrently in the system. Job failure can cause a waste of system resources and impact the efficiency of the system and user jobs. Job failure prediction can support fault-tolerant technology to alleviate this phenomenon in supercomputers. At present, the related work mainly predicts job failure by collecting the real-time performance attributes of jobs, but it is difficult to apply in the real environment because of the high cost of collecting job attributes. In addition to analyzing the time and resource attributes in the job logs, this study also explores the semantic information of jobs. We mine job application semantic information from job names and job paths, where job path is collected by additional monitoring of the job submitting process. A prediction method based on job application semantic enhancement is proposed, and the prediction results of the non-ensemble learning algorithm and the ensemble learning algorithm are compared under each evaluation indicator. This prediction method requires more miniature feature collection and computation overhead and is easy to apply. The experimental results showed that the prediction effect was promisingly improved with job application semantic enhancement, and the final evaluation indicator S_score was improved by 5%–6%, of which was 88.16% accuracy with 95.23% specificity and 88.24% sensitivity.

Empirical Research on Data Science Talent Training and Enterprise Demand in Colleges and Universities

The advent of the information age has given rise to a huge amount of data, and it becomes crucial to use the data well and bring its value into play. How colleges and universities train data science talents to meet the huge demand of the industry is of great significance. In this paper, the demand information of data science talents from recruitment websites was crawled by a typical web scraper and then carried out pre-processing, statistical analysis, and visual display. Through fully excavating the job information and summarizing the law of enterprise demand, constructive suggestions were put forward for data science talent training in colleges and universities, providing a reference basis for talent training units to develop training programs.

Reparación percutánea del tendón de Aquiles con sutura reabsorbible: resultados y complicaciones

ObjectiveThe purpose of this study is to evaluate the clinical outcomes and complications of percutaneous achilles tendon repair with absorbable sutures. Material and methodsA prospective cohort study including patients treated for an achilles tendon rupture from January 2016 to March 2019 was conducted. Inclusion criteria≥18 years of age, non-insertional (2–8cm proximal to insertion) achilles tendon ruptures. Open or partial ruptures were excluded. The diagnosis was based on clinical criteria and confirmed by ultrasonography in all patients. Epidemiological data, rupture and healing risk factors, previous diagnosis of tendinopathy, pre-rupture sport activity, job information, mechanism of rupture and the time in days between lesion and surgery were collected.Patients were assessed using visual analogue scale at the 1, 3, 6 and 12-month follow-up. The achilles tendon rupture score were assessed at the 6 and 12 month follow-up. Ultrasound was performed at the 6-month follow-up. The re-rupture rate and postoperative complications were also collected. ConclusionsIn our experience, percutaneous achilles tendon repair with absorbable sutures in patients with an acute achilles tendon rupture has shown good functional results but with a high incidence of complications. Although most complications were transitory sural nerve symptoms, this complication would be avoided in patients treated conservatively. For this reason, conservative treatment associated with an early weightbearing rehabilitation protocol should be considered a viable option for patients with achilles tendon ruptures, mainly in cooperative young patients.

OCCUPATIONAL BURNOUT SYNDROME AMONG COMMUNITY PHARMACISTS DURING COVID-19 PANDEMIC

Introduction: The community pharmacy is the most accessible medical facility in the current healthcare system. Therefore, the expectations from pharmacists as the most accessible healthcare professionals are constantly rising. However, various studies indicate that pharmacists are subject to very high levels of occupational stress and professional burnout is common.&#x0D; The COVID-19 pandemic has posed new challenges to the work organisation in pharmacies and demand for an adequate reorganisation. In addition, it forced pharmacists to quickly adapt to the new circumstances and requirements and further increased occupational stress. &#x0D; Objective: To establish the level of occupational burnout among community pharmacists during COVID-19.&#x0D; Materials and Methods: An anonymous questionnaire survey was conducted among 127 pharmacists working in community pharmacies in Varna, Bulgaria. The study period was from March 2021 to October 2021. The questionnaire consisted of two parts; the first one was based on the specialised Maslach Burnout Inventory (MBI-HSS) tool, assessing professional burnout in terms of emotional exhaustion, depersonalisation, and personal accomplishment; the second one collected data about the demographic characteristics of the respondents, including their job position, pharmacy type (an independent pharmacy or a pharmacy chain) and information on specific problems during COVID-19.&#x0D; Results: The identified mean values in the group of respondents were as follows: emotional exhaustion M=23.39 (SD 9,298), depersonalisation M=12.37 (SD 6,426), professional achievements M=31.25 (SD 7,531). The sample mean values that stood out were in the depersonalisation scale М=12.37, which is considered a high value. A statistically significant difference was found in the different age groups on the depersonalisation scale (F=3,957, p=0,022). There was a statistically significant difference between the arithmetic mean values for the age groups 25-40 years (M=13.75) and over 55 years (M=9.86).&#x0D; Conclusion: Based on the statistical differences for depersonalisation among pharmacists in different age groups, it can be concluded that young pharmacists do not express empathy while providing pharmaceutical care services.

NLP-Based Bi-Directional Recommendation System: Towards Recommending Jobs to Job Seekers and Resumes to Recruiters

For more than ten years, online job boards have provided their services to both job seekers and employers who want to hire potential candidates. The provided services are generally based on traditional information retrieval techniques, which may not be appropriate for both job seekers and employers. The reason is that the number of produced results for job seekers may be enormous. Therefore, they are required to spend time reading and reviewing their finding criteria. Reciprocally, recruitment is a crucial process for every organization. Identifying potential candidates and matching them with job offers requires a wide range of expertise and knowledge. This article proposes a reciprocal recommendation based on bi-directional correspondence as a way to support both recruiters’ and job seekers’ work. Recruiters can find the best-fit candidates for every job position in their job postings, and job seekers can find the best-match jobs to match their resumes. We show how machine learning can solve problems in natural language processing of text content and similarity scores depending on job offers in major Saudi cities scraped from Indeed. For bi-directional matching, a similarity calculation based on the integration of explicit and implicit job information from two sides (recruiters and job seekers) has been used. The proposed system is evaluated using a resume/job offer dataset. The performance of generated recommendations is evaluated using decision support measures. Obtained results confirm that the proposed system can not only solve the problem of bi-directional recommendation, but also improve the prediction accuracy.

THE REGULATIONS FOR JUDGES USING SOCIAL MEDIA AND THE MEDIA: COMPARATIVE STUDY

This article analyses the regulations for judges using social media and the Media, studies international resolutions and some of the most important Arab experiences. The purpose of this article is to identify social media that judges can access, in addition to legislative opinions for and against the participation of judges on social media, while the dilemma of the study lies in the extent to which judges are given freedom to use social media and the extent to which judges publish their professional achievements, disclose their job information, comment on public opinion cases published on social media, and participate in the analysis and discussion. The findings proved that the competent authorities should periodically review the judicial code of conduct and make amendments to it in line with international standards to ensure the safe use of social media for judges, and judges must perform caution when dealing with the social media, and when they post on social media, and any comment or statement published by the judge must renew citizens’ confidence in the judiciary and not conflict with the prestige of his position or with the independence and impartiality of this authority.

Career Choice of The Students, What is Important?

This study aims to determine the factors considered by students in choosing a career after graduating from college. The research population is students of the 4 largest universities in Malang, they are Brawijaya University, State University of Malang, University of Muhammadiyah Malang and State Islamic University of Malang. The sample of the study was 200 respondents with details of each university 50 respondents, respondents who filled out the complete questionnaire were 134. The analysis tool was factor analysis with 24 variables originating from the voice of respondents. From the data analysis process, 16 variables were worthy of analysis and grouped into 6 factors. The results showed that there were 6 factors for student career choice are: financial, job information, opportunity, social environment, challenge and interest, with financial being the dominant factor.

Impact of occupational SARS-CoV-2 risk on the mental distress of non-healthcare employees in Germany

Abstract Background The SARS-CoV-2 pandemic posed major challenges to employees and companies. Differences between industries and professions in occupational SARS-CoV-2 infection risk (OSIR) became apparent early on. This study examines the mental distress in terms of depression and anxiety symptoms of non-healthcare workers during the first and second Corona waves in Germany. Methods We conducted an online survey from December 2020 to June 2021 among employees from industrial enterprises, local public transport, the public and the financial sector. High and potential OSIR was defined based on job information. Depression and anxiety symptoms were rated with the 4-item Patient Health Questionnaire (PHQ-4). Categorized PHQ-4 scores were modeled with ordinal random-intercept logistic multinomial regression models and presented with odds ratios (OR) and 95% confidence intervals (95% CI). Results Overall, 516 of 1,545 participants (33.4%) were determined to be at increased OSIR. Anxiety and depression symptoms worsened during the pandemic in all OSIR groups. Risks for more severe depressive and anxiety symptoms were higher among employees with high (OR 2.35; 95% CI 1.33-4.16) and potential OSIR (OR 1.70; 95% CI 1.19-2.43) compared to employees without OSIR. Severity of mental distress differed also by the extent of perceived job protection, interactions with colleagues, work-privacy conflicts, and overcommitment. Conclusions OSIR had a negative impact on employee’s depressive symptoms and anxiety. Reducing SARS-CoV-2 exposure through workplace protective measures, strengthening interactions among colleagues, and supporting employees with work-privacy conflicts could help better protect employees’ mental health. Key messages • The risk for more severe depressive and anxiety symptoms increases with higher occupational SARS-CoV-2 infection risk. • Improvements in SARS-CoV-2 exposure at work, colleague interactions, and work-privacy conflicts could help better protect mental health.

A Preliminary Analysis between Available and Shared Resources for the Lebanese Job Crisis

ABSTRACTThe Port of Beirut, Lebanon explosion on August 4, 2020, forced increased emigration of the Lebanese people, as they sought stability outside of their economically and politically volatile country (Farhat, 2021). Thus, families remaining in Lebanon are more dependent than ever on remittances. However, the ability of families to receive remittances is contingent on the success of those emigrating from Lebanon to gain employment. Job seekers often turn to the internet for career information from communication platforms like Reddit and dedicated job forums, like Jobs for Lebanon, among other institutional resources. This study focuses on these online institutional resources' hyperlink network, as hyperlink networks can represent a collective public good (Fu &amp; Shumate, 2016). Data collection resulted in a network of 148 institutions. Yet with the plethora of resources available, the Lebanese community is still undergoing ineffective job searching. This preliminary research explores thse barrier between institutionally presented job resources and Lebanese job seekers by comparing the structure of the existing institutional resource network to resources shared in a leading online community. A network analysis of 148 institutions reveals: (1) unproductive job information exchange between major institutions, and (2) an opportunity for institutions to better support Lebanese job seekers.

Accelerating model synchronization for distributed machine learning in an optical wide area network

Geo-distributed machine learning (Geo-DML) adopts a hierarchical training architecture that includes local model synchronization within the data center and global model synchronization (GMS) across data centers. However, the scarce and heterogeneous wide area network (WAN) bandwidth can become the bottleneck of training performance. An intelligent optical device (i.e., reconfigurable optical all-drop multiplexer) makes the modern WAN topology reconfigurable, which has been ignored by most approaches to speed up Geo-DML training. Therefore, in this paper, we study scheduling algorithms to accelerate model synchronization for Geo-DML training with consideration of the reconfigurable optical WAN topology. Specifically, we use an aggregation tree for each Geo-DML training job, which helps to reduce model synchronization communication overhead across the WAN, and propose two efficient algorithms to accelerate GMS for Geo-DML: MOptree, a model-based algorithm for single job scheduling, and MMOptree for multiple job scheduling, aiming to reconfigure the WAN topology and trees by reassigning wavelengths on each fiber. Based on the current WAN topology and job information, mathematical models are built to guide the topology reconstruction, wavelength, and bandwidth allocation for each edge of the trees. The simulation results show that MOptree completes the GMS stage up to 56.16% on average faster than the traditional tree without optical-layer reconfiguration, and MMOptree achieves up to 54.6% less weighted GMS time.

Job Matching Analysis Based on Text Mining and Multicriteria Decision-Making

The postmanagement of an enterprise is to rationally and scientifically arrange the talents who meet the postrequirements and are suitable for the development of the enterprise in their suitable posts through testing, selection, and employment. The job recruitment information published on the recruitment website is the data that best reflects the market demand for data analysis talents. However, as online recruitment information is mostly presented in the form of text, this paper explores the information contained in the recruitment information through text mining technology, which has reference value for data analysis job seekers. Based on text mining and multicriteria decision-making, this paper studies job matching. The technical jobs under text mining are mainly distributed in the Internet industry, and the number of recruits accounts for 66.45%. Other industries have less demand for technical data analysis, all of which are below 15%. However, the market of business data analysis is scattered and extensive, and education still occupies the largest demand provider, accounting for 30.53%. However, real estate and media are closely followed, accounting for 24.28% and 22.34%, respectively. Besides these three industries, compared with technical talents, service industries will also need business data analysis talents more. In the stage of data collection, through the data analysis of the recruitment website, the job recruitment information is crawled, as well as other job information such as job salary, job qualifications, work experience requirements, and the number of recruits. This paper carries out the data collection stage and crawls the job recruitment information through the data analysis of the recruitment website. And, other position information, such as position salary, position qualification, and work experience requirements, as well as the number of recruits. The research of this paper can help the data released by the recruitment website to analyze the recruitment information and quickly reflect the market demand data for data analysis talents.

Percutaneous achilles tendon repair with absorbable suture: Outcomes and complications

ObjectiveThe purpose of this study is to evaluate the clinical outcomes and complications of percutaneous achilles tendon repair with absorbable sutures. Material and methodsA prospective cohort study including patients treated for an achilles tendon rupture from January 2016 to March 2019 was conducted. Inclusion criteria≥18 years of age, non-insertional (2–8cm proximal to insertion) achilles tendon ruptures. Open or partial ruptures were excluded. The diagnosis was based on clinical criteria and confirmed by ultrasonography in all patients. Epidemiological data, rupture and healing risk factors, previous diagnosis of tendinopathy, pre-rupture sport activity, job information, mechanism of rupture and the time in days between lesion and surgery were collected.Patients were assessed using visual analogue scale (VAS) at the 1, 3, 6 and 12-month follow-up. The achilles tendon rupture score (ATRS) were assessed at the 6 and 12 month follow-up. Ultrasound was performed at the 6-month follow-up. The re-rupture rate and postoperative complications were also collected. ConclusionsIn our experience, percutaneous achilles tendon repair with absorbable sutures in patients with an acute achilles tendon rupture has shown good functional results but with a high incidence of complications. Although most complications were transitory sural nerve symptoms, this complication would be avoided in patients treated conservatively. For this reason, conservative treatment associated with an early weightbearing rehabilitation protocol should be considered a viable option for patients with achilles tendon ruptures, mainly in cooperative young patients.

Perceptions of experience of online coaching for college students’ career planning

This study examined college students’ perceptions of online coaching for career planning and preparing a plan to promote online coaching. We used the Q-methodology with 40 Q-sets and 31 P-samples. As a result of the study, we derived a total of six types of perceptions: Type 1, improves career competencies through experts; Type 2, demands continuous and systematic processes; Type 3, appreciates psychological support through relationships; Type 4, regards convenient environments as important and emphasizes ease of access; Type 5, demands personalized coaching using e-portfolios; and Type 6, appreciates practical job information. We investigated the perceptions of online coaching based on the individual types discovered in this study and discussed ways to promote online coaching.

Machine Learning-Driven Enterprise Human Resource Management Optimization and Its Application.

With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resource information, such as talent information and job information, has also increased unprecedentedly, which makes human resource services face information overload. Especially with the gradual increase of the amount of information, this method is not enough for the acquisition and classification of massive data. After that, experts developed search engines to deal with the retrieval problem, and the first ones were Google and Baidu. As long as the search engine is clear about the direction of the search, it is indeed very convenient for the retrieval of massive data. However, in many cases, most users cannot clearly recognize the content they need or how to accurately express their needs. Faced with this problem, people propose recommender systems to solve the problem of obtaining preference information, which can better increase the user's experience and meet their own needs more easily. Based on the main workflow of the recommender system, this paper designs the overall architecture of the human resources recommendation system and implements a human resources recommendation prototype system based on deep learning. The system can better overcome the cold start problem and provide real-time recommendation results, improving the quality of HR personalized recommendation results.

Unfolding Emerging Relationship of AI-enabled Job Characteristics on High-Tech Employees IWB

The relevance of AI-enabled systems has occupied the central space of innovation research for a decade. Research works have highlighted adoption of AI-enabled systems (AIS) in firms but there is scant empirical research on AI-enabled job characteristics of employees of high-tech firms pertaining to innovative work behavior (IWB). Therefore, the present attempt to address this research gap, by adopting the job design theory. Authors espouse a model that explains the roles AI- enabled task and knowledge characteristics demonstrate in employee’s IWB. Furthermore, the impact of AIS on human workforce replacement is currently a debatable area. Drawing on prospect theory, we tested the moderating role of perceived substitution crisis (PSC) triggered by AIS on IWB. The results indicated that AI-enabled task characteristics (job autonomy and skill variety) and knowledge characteristics (job complexity, specialization, and information processing) impact IWB and that AI-enabled job characteristics are strongly associated with IWB under differential effects of PSC. The implications of this study could be used by academicians and practitioners to design AI-enabled job characteristics.

Research on Data Analysis and Visualization of Recruitment Positions Based on Text Mining

With the rapid development of the Internet and the impact of COVID-19, online recruitment has gradually become the mainstream form of recruitment. However, existing online recruitment platforms fail to fully combine the job seekers’ demands for salary, region, benefits, and other aspects, which cloud not display the information related to recruitment positions in a multidimensional way. To solve this problem, this paper firstly uses a web crawler to collect job information from recruitment websites based on keywords retrieved by users, then extracts job information using regular expressions, and cleans and processes the extracted job information using third-party libraries such as Pandas and NumPy. Finally, through the probabilistic theme model of text mining, the topic model of job description content in the recruitment information is modeled. Combining with the django development framework and related visualization technology, the relationship among education requirement, experience requirement, job location, salary, and other aspects in the recruitment information is visually displayed in a multidimensional way. At the same time, the GM model is used to realize the gray prediction of the number of employment personnel in related industries, which provides employment reference for the majority of job seekers and enterprises.

Penerapan Metode SAW dan TOPSIS dalam Sistem Rekomendasi Lowongan Kerja Bali

Job information is important for humans, because almost every human being needs a job. The more unemployed human resources in an area, the higher the demand for job vacancies. Bali is one of the areas that has increased unemployment since the pandemic, Bali has a workforce of 2.6 million who need work. To help Bali job seekers find jobs that match their criteria. In this study, a Bali job vacancy recommendation system was built based on the preferences of respondents residing in Bali. This system will apply the SAW-TOPSIS method in providing recommendations. For the development method, the Waterfall Model will be used. Job vacancies data will be collected on a secondary basis via the internet. The criteria and weighting in the recommendations are based on the results of the respondent's survey. The results of this study note that the application of the SAW-TOPSIS method in the Bali job vacancy recommendation system is in accordance with manual calculations and for direct testing to users using usability testing with 20 respondents the results are 83.39% which shows the system has a very good usability value.