Online Recruitment Platform Research Articles

A Comparative Analysis on Recruitment and Selection Process at Chennai Shoes and Industrial Supplies LLP, T Nagar Chennai

This study investigates the recruitment and selection practices at Chennai Shoes and Industrial Supplies LLP, located in T. Nagar, Chennai, with a focus on evaluating the effectiveness of their current strategies and identifying areas for improvement. The research combines both qualitative and quantitative methods to assess the organization's recruitment sources, selection techniques, and overall efficiency. A t-test is employed to compare the effectiveness of two key recruitment strategies: Traditional interviews and Online recruitment platforms. The objective is to determine whether there is a significant difference in employee satisfaction between candidates hired through these two methods. The results of the t-test reveal that there is no significant difference (p-value > 0.05) in employee satisfaction between the two recruitment methods, suggesting that both approaches yield similar levels of candidate satisfaction. The findings also highlight key areas for improvement, including the integration of technology in recruitment processes, the need for standardized interview techniques, and the importance of enhancing employee retention strategies. The study concludes by offering actionable recommendations, such as adopting an Applicant Tracking System (ATS) and improving employee development programs, to optimize recruitment, selection, and retention processes, thereby enhancing the company's competitive advantage. Keywords: Recruitment and Selection, T-test, Employee Satisfaction, Traditional Interviews, Online Recruitment Platforms, Chennai Shoes and Industrial Supplies LLP.

Exploration of Factors Influencing the Effectiveness of Online Recruitment Service Ratings

Background information & problem statement: Online recruitment has brought great benefits to both enterprises and candidates in terms of reducing recruitment costs, expanding the scope of recruitment advertisements, publishing recruitment information quickly, and effectively interacting in the recruitment process, as well as promoting the flow of talent and economic development in the region. As a result, online recruitment has become the first choice for businesses and candidates. However, there are still problems in the practical application of online recruitment, such as excessive amount of information, low pass rate, and unsatisfactory recruitment effect, which is not conducive to the implementation of enterprise human resource management. Motivation: This study studies the influencing factors of online recruitment and the measures to improve the effectiveness of online recruitment, so as to provide theoretical guidance and reference for enterprises to take effective countermeasures. Research method: This study aims to assist in improving the service functions of online recruitment platforms through the Analytic Hierarchy Process (AHP) to meet more user needs. By understanding the demand orientation of job seekers, the study provides a more suitable evaluation of recruitment effectiveness and serves as a reference for improving the quality of recruitment services on various online platforms. Findings & conclusion: Through the relationship matrix, online recruitment platforms can understand whether the services they provide can meet the real needs of users, so as to serve as a basis for understanding more user-friendly needs, as shown in Figure 3. As can be seen from the figure, the top five in the total points ranking are value-added services, design beautification, platform management, dispute resolution and reasonable compensation. Therefore, under the premise of limited resources, enterprises can give priority to increasing value-added services, optimizing the system interface, strengthening the privacy review management of the platform, timely handling of disputes between applicants and enterprises, and improving the claim system. Enterprises can consider projects with low total points such as strengthening customer service training, improving training skills, and simplifying platform operations when they have spare resources to invest capital.

Management Research on Online Recruitment CompaniesBased on Case Analysis of SEEK

This paper explores the study of management research on online recruitment companies, especially to provide a deeper understanding of the management practices of SEEK, Australias largest online recruitment company, through a case analysis focusing on challenges related to corporate governance, cost of capital, and company structure. With the development of the internet and information technology, online recruitment platforms offer efficient and convenient hiring methods for businesses and job seekers, but they also bring unique management challenges. By analysing SEEKs corporate governance structure, including its board composition and compensation policies, this study reveals key issues online recruitment companies face in governance and capital management. Furthermore, the paper examines SEEKs capital structure and its impact on its operations and growth. Through this case sing of the complexities of managing online recruitment platforms. It highlights both the opportunities created by technological advancements and the governance and financial challenges they face.

Validation of the English-language version of the Morningness-Eveningness-Stability-Scale-improved (MESSi), and comparison with a measure of sleep inertia

ABSTRACT The Morningness-Eveningness-Stability-Scale-improved (MESSi) assesses three components of circadian functioning: Morning Affect (time to fully awaken), Eveningness (orientation/preference for evening activity), and Distinctness (amplitude of diurnal variations in functioning). Following the original German version, translations of the MESSi (including Spanish, Turkish, and Chinese) have been validated, but validity evidence for the English-language version has been lacking. The current study tested the factor structure, internal consistency, and predicted correlations of the English-language MESSi. A sample of 600 adults from an online recruitment platform (aged 18–78, mean = 41.31, SD = 13.149) completed an online survey including the MESSi, reduced Morningness-Eveningness Questionnaire (rMEQ), Sleep Inertia Questionnaire (SIQ), and measures of personality and depressive symptoms. Exploratory factor analysis exactly reproduced the three-component structure of Morning Affect (MA), Eveningness, and Distinctness, with all items loading strongly on their respective component. Confirmatory factor analysis of this structure showed acceptable fit. The three subscales showed good internal consistency and replicated previously reported correlations with depressive symptoms, sleep inertia, sleep quality, and personality. Further factor analysis combining the items of the MESSi, rMEQ, and SIQ replicated a previously found seven-factor structure: Cognitive, Emotional, and Physiological sleep inertia (SI), Responses to SI (including one MA item); Duration of SI (one SIQ item, 3/5 MA items); Morningness-Eveningness (MESSi Eveningness items, plus 3/5 rMEQ items); Distinctness (5/5 MESSi items). In conclusion, the English-language MESSi shows sound psychometric properties, but Morning Affect may be more suitably characterised as a measure of sleep inertia duration, rather than morningness preference.

Minimum wage and internal labor migration: Evidence from China

We study the geographical allocation consequences of minimum wage, using individual-level migration data and the substantial variation over time in minimum wages across 262 counties in China. Our preferred specification shows that a 10% increase in the local minimum wage decreases the probability of outmigration by 1.52 percentage points. Using data from a major online recruitment platform, we further corroborate the main finding by showing that workers are less likely to search for a job in other cities when their city of residence experiences an increase in minimum wage.

Big Five personality traits, BIS/BAS dimensions and meteoropathy: a mediation model

BackgroundThe main goal of this cross-sectional study was to assess the relationship between Big Five personality traits and meteor-opathy and the role of the behavioural activation system (BAS) and the behavioural inhibition system (BIS) as mediators in this relationship.Participants and procedureThe sample consisted of 1,336 women and men gathered via the online recruitment platform from the general population. The participants’ ages ranged from 18 to 76 years (M = 29.52, SD = 11.67). The Polish version of the Ten Item Personality Inventory was used to assess Big Five personality traits. The BIS and BAS were diagnosed using the Polish version of the original BIS/BAS scales. Meteoropathy was assessed using the Polish adaptation of the METEO-Q questionnaire.ResultsMeteoropathy correlated negatively with emotional stability and positively with the BIS level and BAS Reward Responsive-ness. BIS negatively correlated with emotional stability. The correlations between meteoropathy and other personality vari-ables were nonsignificant. The analysis indicated a significant indirect relationship between emotional stability and meteor-opathy through BIS. We demonstrated the importance of neuroticism (low emotional stability) and the BIS dimension for the development of meteoropathy symptoms.ConclusionsNeuroticism and the BIS dimension were important for the development of meteoropathy symptoms. The results may be helpful in assessing the risk of developing meteoropathy in a healthy population.

Therapeutic relationship elements and therapy session outcomes: Protocol for a longitudinal study of the patient's perspective.

There is a growing recognition of the key role of the therapeutic relationship in the outcomes of psychotherapy. However, current understanding of its specific components, their interplay and related patient-therapist dynamics is limited. (a) To validate two self-report measures to assess subjective affective reactions of patients toward their psychotherapists during specific therapy sessions, and (b) to explore the relationships and dynamics among four elements of the therapeutic relationship: patient reactions toward the therapist, working alliance, alliance ruptures and repairs, and the real relationship. This study uses a nonrandomized, two-time point longitudinal design. The target population is adult patients currently engaged in individual psychotherapy for heterogeneous mental conditions. Participants are recruited through two online recruitment platforms: Research for Me and ResearchMatch. Data collection involves administering two surveys through the Qualtrics online survey platform. The baseline survey assesses information about the most recent therapy session and the preceding week, while the follow-up survey collects data on the subsequent therapy session and the days leading up to it. This research offers three main contributions: (a) it furthers evidence-based assessment in psychotherapy by creating and validating two novel, succinct self-report tools; (b) it enhances theoretical understanding within therapeutic relationship research by exploring the significant impact of patients' perceptions of relationship elements on session outcomes variability; and (c) it will identify therapeutic relationship elements that can either enhance or hinder the overall relationship quality and session outcomes. The study protocol was approved by the Institutional Review Board of the University of North Carolina at Chapel Hill. The results will be published in indexed peer-reviewed journals and presented at relevant psychology and psychiatry conferences.

Evaluating the Problem of Fraudulent Participants in Health Care Research: Multimethod Pilot Study.

The shift toward online recruitment methods, accelerated by the COVID-19 pandemic, has brought to the forefront the growing concern of encountering fraudulent participants in health care research. The increasing prevalence of this issue poses a serious threat to the reliability and integrity of research data and subsequent findings. This study aims to explore the experiences of health care researchers (HCRs) who have encountered fraudulent participants while using online recruitment methods and platforms. The primary objective was to gain insights into how researchers detect and mitigate fraudulent behavior in their work and provide prevention recommendations. A multimethod sequential design was used for this pilot study, comprising a quantitative arm involving a web-based survey followed by a qualitative arm featuring semistructured interviews. The qualitative description approach framed the qualitative arm of the study. Sample sizes for the quantitative and qualitative arms were based on pragmatic considerations that in part stemmed from encountering fraudulent participants in a concurrent study. Content analysis was used to analyze open-ended survey questions and interview data. A total of 37 HCRs participated, with 35% (13/37) of them engaging in qualitative interviews. Online platforms such as Facebook, email, Twitter (subsequently rebranded X), and newsletters were the most used methods for recruitment. A total of 84% (31/37) of participants indicated that fraudulent participation occurred in studies that mentioned incentives in their recruitment communications, with 71% (26/37) of HCRs offering physical or electronic gift cards as incentives. Researchers identified several indicators of suspicious behavior, including email surges, discrepancies in contact or personal information, geographical inconsistencies, and suspicious responses to survey questions. HCRs emphasized the need for a comprehensive screening protocol that extends beyond eligibility checks and is seamlessly integrated into the study protocol, grant applications, and research ethics board submissions. This study sheds light on the intricate and pervasive problem of fraudulent participation in health care research using online recruitment methods. The findings underscore the importance of vigilance and proactivity among HCRs in identifying, preventing, and addressing fraudulent behavior. To effectively tackle this challenge, researchers are encouraged to develop a comprehensive prevention strategy and establish a community of practice, facilitating real-time access to solutions and support and the promotion of ethical research practices. This collaborative approach will enable researchers to effectively address the issue of fraudulent participation, ensuring the conduct of high-quality and ethically sound research in the digital age.

Your Career Path Matters in Person-Job Fit

We are again confronted with one of the most vexing aspects of the advancement of technology: automation and AI technology cause the devaluation of human labor, resulting in unemployment. With this background, automatic person-job fit systems are promising solutions to promote the employment rate. The purpose of person-job fit is to calculate a matching score between the job seeker's resume and the job posting, determining whether the job seeker is suitable for the position. In this paper, we propose a new approach to person-job fit that characterizes the hidden preference derived from the job seeker's career path. We categorize and utilize three types of preferences in the career path: consistency, likeness, and continuity. We prove that understanding the career path enables us to provide more appropriate career suggestions to job seekers. To demonstrate the practical value of our proposed model, we conduct extensive experiments on real-world data extracted from an online recruitment platform and then present detailed cases to show how the career path matter in person-job fit.

Enhancing Job Recommendation through LLM-Based Generative Adversarial Networks

Recommending suitable jobs to users is a critical task in online recruitment platforms. While existing job recommendation methods encounter challenges such as the low quality of users' resumes, which hampers their accuracy and practical effectiveness.With the rapid development of large language models (LLMs), utilizing the rich external knowledge encapsulated within them, as well as their powerful reasoning capabilities, is a promising way to complete users' resumes for more accurate recommendations. However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion. In this paper, we propose a novel LLM-based approach for job recommendation. To alleviate the limitation of fabricated generation for LLMs, we extract accurate and valuable information beyond users' self-description, which helps the LLMs better profile users for resume completion. Specifically, we not only extract users' explicit properties (e.g., skills, interests) from their self-description but also infer users' implicit characteristics from their behaviors for more accurate and meaningful resume completion. Nevertheless, some users still suffer from few-shot problems, which arise due to scarce interaction records, leading to limited guidance for high-quality resume generation. To address this issue, we propose aligning unpaired low-quality with high-quality generated resumes by Generative Adversarial Networks (GANs), which can refine the resume representations for better recommendation results. Extensive experiments on three large real-world recruitment datasets demonstrate the effectiveness of our proposed method.

Abstract P219: Moderated-Mediated Effects of Environmental Barriers and Income Level on Exercise Determinants Among Black Americans With Hypertension

Introduction: Black individuals are disproportionately affected by hypertension and due to racial disparities have fewer opportunities to engage in preventive measures such as exercise compared to their White counterparts. There is a dearth of research employing a theory-driven approach to comprehensively investigate the factors contributing to disparities in exercise participation among racial groups, both at the individual and environmental levels. The I-Change Model is a contemporary health behavior model comprising three primary pillars hypothesized to influence health behavior: Awareness, Motivation, and Action/Planning. What sets this model apart is its unique emphasis on the proximity of the barrier construct to predicting behavior execution, a feature that may contribute to disparities in exercise participation, including racial disparities. Hence, this observational study aimed to apply the ‘I-Change model’ to identify factors that contribute to exercise behaviors among Black and White individuals with Hypertension. Methods: We used the I-Change model, which includes mediating behavioral determinants, along with moderators that include environmental barriers (e.g., access and transportation to a supportive exercise environment), and income (low vs. moderate and above) to gain a better understanding of racial disparities in exercise participation. Individuals with hypertension (n= 370) that comprised of Black (n= 142) and White (n= 228) adults who were recruited via an online recruitment platform and completed a survey that included validated theoretical constructs. Hayes PROCESS 4.0 Macro (model #16) for SPSS was used to construct the theoretical model and analyze the data. The model included race (x), exercise time (y), behavioral determinants (mediators), environmental behaviors, and income (moderators between mediators and exercise time). Results: I-Change determinants (risk of heart disease, planning, exercise knowledge, exercise partner/social support) mediated the relationship between race and exercise time (β= .24, 95% CI .098, .394) in favor of White participants. Moderated-mediation effects were found on barriers that interacted between planning and exercise time (β= .03, 95% CI .0018, .0674), and income which interacted between social support and exercise time, (β= .05, 95% CI .0004, .1056). Conclusion: Intervention work at the community level for low-income Black individuals with hypertension supported by a theoretical framework is needed. Specifically, an exercise promotion program that supports the development of I-Change determinants, which provides an accessible and supportive environment, would be helpful in providing equitable exercise opportunities for Black individuals with hypertension.

The Regional Labor Market in the Field of Tourism and the Hotel Industry in the Context of the Transformation of the Russian Economy

The relevance. The departure of foreign companies, combined with the growth of domestic demand, has created positive prerequisites for the development of domestic hotel chains and domestic tourism. These objective processes have led to the need to assess the labor market in the tourism and hotel industry in order to identify problems and ways to solve them with the increasing demand of enterprises in this field for qualified specialists. The purpose of the study is to study and analyze the current situation and problems in the labor market in the tourism and hotel industry of the Kursk region. The objectives were to assess the job market in the region, requests and offers from employers, the structure of vacancies and the activity of applicants. Methodology. The research methodology is based on the analysis of statistical information contained in the all-Russian federal database of vacancies and resumes of specialists in the tourism and hotel industry. Results. The data of Russian online recruitment platforms are analyzed and systematized. Based on the results of the analysis of the composition and structure of the labor market of specialists in the hospitality sector, the comparison of supply and demand indicators in it, the analysis of wages in the field under consideration and the dynamics of its change, measures were proposed, the implementation of which will ensure the sustainable development of the industry. Conclusions. Despite some problems and shortcomings in the hospitality sector of the Kursk region, this industry continues to develop and grow. With the growing number of tourists and an increase in the volume of services provided, the labor market in this area is becoming more competitive and attractive for qualified specialists. Regular monitoring of the labor market and analysis of changes in personnel qualification requirements will improve the quality of services provided and meet the growing demand for hospitality workers.

The Role of the Ask Gap in Gender Pay Inequality

Abstract The gender ask gap measures the extent to which women ask for lower salaries than comparable men. This article studies its role in generating wage inequality, using novel data from an online recruitment platform for full-time engineering jobs: Hired.com. To use the platform, job candidates must post an ask salary, stating how much they want to make in their next job. Firms then apply to candidates by offering them a bid salary, solely based on the candidate’s résumé and ask salary. If the candidate is hired, a final salary is recorded. After adjusting for résumé characteristics, the ask gap is 2.9%, the bid gap is 2.2%, and the final offer gap is 1.4%. Further controlling for the ask salary explains the entirety of the residual gender gaps in bid and final salaries. To further provide evidence of the causal effect of the ask salary on the bid salary, I exploit an unanticipated change in how candidates were prompted to provide their ask. For some candidates in mid-2018, the answer box used to solicit the ask salary was changed from an empty field to an entry prefilled with the median bid salary for similar candidates. I find that this change drove the ask, bid, and final offer gaps to zero. In addition, women did not receive fewer bids or final offers than men did due to the change, suggesting they faced little penalty for demanding comparable wages.

ОЦЕНКА УРОВНЯ ЦИФРОВОЙ ЗРЕЛОСТИ МЕДИАИНДУСТРИИ РОССИИ

This work assesses the digital maturity of the Russian media industry, as a necessary stage for creating a digital transformation strategy for media companies. The research hypothesis is that there is a lack of relevant methods for assessing the digital maturity of the media industry in Russia. The research method-ology is based on two aspects: 1) the digital maturity of the media industry is understood as an element of the digital economy; 2) the digital maturity of the media industry is formed taking into account ap-proaches to assessing the digital maturity of other sectors of the economy. The study uses methods found in the literature, consulting and audit reports, and the reports of economic organizations. The pa-per proposes an approach to assessing the digital maturity of the media industry based on open data and a survey of media companies. This approach consists of two parts: the first part involves a rapid assess-ment of the digital maturity of the media industry using open data; the second part involves a more de-tailed assessment based on a survey of media company representatives. The statistical base was com-piled from HSE University reports and the online recruitment platform Headhunter (HH.ru). The result of the study is an assessment of four segments of the media industry: publishing, broadcasting, content production, and other services. Based on the results, we conclude that broadcasting is the most devel-oped segment by digital maturity indicator; the publishing segment is the least developed. The practical significance of the study lies in the possibility of media companies using the results to compare their own indicators with industry averages, which will help determine the need for their digital transfor-mation.

ResuméAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models

The increasing reliance on online recruitment platforms coupled with the adoption of AI technologies has highlighted the critical need for efficient resume classification methods. However, challenges such as small datasets, lack of standardized resume templates, and privacy concerns hinder the accuracy and effectiveness of existing classification models. In this work, we address these challenges by presenting a comprehensive approach to resume classification. We curated a large-scale dataset of 13,389 resumes from diverse sources and employed Large Language Models (LLMs) such as BERT and Gemma1.1 2B for classification. Our results demonstrate significant improvements over traditional machine learning approaches, with our best model achieving a top-1 accuracy of 92% and a top-5 accuracy of 97.5%. These findings underscore the importance of dataset quality and advanced model architectures in enhancing the accuracy and robustness of resume classification systems, thus advancing the field of online recruitment practices. Our models, code, and dataset are available as open-source resources.123

Pitfalls of Digitalization: Teacher Recruitment in Malaysia International Schools

Human resource practices, particularly teacher recruitment, are evolving due to digitalisation. There is a critical need for international schools to attract and select quality teaching staff to ensure the quality of learning for students and maintain their competitiveness within a dynamic market. This paper reported the findings of a study that investigated the challenges that school administrators faced during the digitalisation of teacher recruitment practices at international schools in Malaysia. A series of semi-structured interviews involving fourteen management personnel from eleven international schools revealed seven challenges highlighted by the participants: 1) a rise in competition, 2) attracting irrelevant or underqualified applicants, 3) time-consuming preparation of detailed job ads and recruitment booklets, 4) escalating recruitment costs, 5) student privacy issues, 6) managing a large number of applications, and 7) loss of important information during virtual interview. To address these challenges, the participants took four actions: 1) advanced planning before the new school term, 2) building rapport with potential candidates, 3) selecting a suitable online recruitment platform, and 4) integrating online and offline recruitment. The findings of the study contribute to the growing literature on talent acquisition at international schools by providing empirical evidence of the existing challenges faced at a sample of international schools in Malaysia.

Information and Communication Technology (ICT) and Recruitment Process in Public Organizations: A Study of Nigeria Police Force, Ebonyi State Command

The importance of Information and Communication Technology (ICT) in the recruitment process cannot be underestimated. That explains why organizational human resources managers, the Nigeria Police Force inclusive, have integrated it into the all-important exercise. Therefore, this study examined the effect of application of ICT on recruitment process in the public sector organizations with particular reference to the Nigeria Police Force, Ebonyi State Command. The study was anchored on Technological Determinism theory espoused by Thorstein Veblen (1857-1929). The study adopted descriptive survey design with a sample size of 400. Data were generated through structured questionnaire designed in 5 Likert scale. Chi-square inferential statistics were used to test the formulated hypotheses. The study revealed among others – that the use of online recruitment platform has significantly facilitated teeming qualified citizens’ access to job openings in the Nigeria Police Force, Ebonyi State Command; that the use of online recruitment portal has significantly enhanced appointment of competent staff in the Nigeria Police Force, Ebonyi State Command. The implication of the findings is that the adoption of ICT without human manipulations is key to institutionalization of meritocracy in the exercise. The study recommended as follows among others – that the Nigeria Police Force should ensure sustenance of the use of ICT in the recruitment process so as to attract qualified candidates to the security outfit; that the use of ICT should be given free hand during the recruitment exercise in order to facilitate emergence of most competent and skillful candidates.

FRI147 A Direct-To-Patient Primary Aldosteronism Screening Program In The United States: Implementing Guidelines Nationwide

Abstract Disclosure: J.M. Brown: Consulting Fee; Self; Bayer, Inc. B. Bacare: None. Y.M. Niebuhr: None. L.C. Tsai: None. E.E. Abel: None. B. Honzel: None. A. Vaidya: Consulting Fee; Self; Corcept Therapeutics, HRA Pharmaceuticals, Mineralys. Context: Primary aldosteronism (PA) is a prevalent but grossly underrecognized contributor to the global burden of hypertension, excess cardiovascular and renal disease, and death. Though Endocrine Society guidelines recommend high risk populations who should be screened for PA, fewer than 2% of these at-risk patients are ever tested. Given the poor rates of routine screening within guideline-eligible populations, we designed and implemented a direct-to-patient virtual-based PA screening intervention. Methods: Participants with one or more guideline indications for PA screening were recruited using social media, online recruitment platforms, and online patient portals to participate in a video visit to review medical history and to arrange a local reference lab draw for plasma renin activity (PRA), aldosterone (via LC-MS/MS), and a basic metabolic panel, without adjustment of medications. Screening results were categorized as “Likely Confirmed PA”, “Possible PA”, or “Unlikely PA” based on established diagnostic frameworks. Test results, their interpretations, and possible next clinical steps (i.e., further testing, referral, empiric treatment) were communicated directly to patients and their primary care providers, with annual reminders and follow-up. Results: To date, 3220 participants from 12 states in the eastern USA had been contacted, with 2504 eligible. At the time of submission, 212 participants had completed laboratory assessment, with the remainder pending. 79 (37.3%) participants had resistant hypertension, 95 (44.8%) had hypertension with hypokalemia, 75 (35.4%) had hypertension with sleep apnea, 11 (5.2%) had hypertension with adrenal mass, and 90 (42.5%) had hypertension with a family history of early hypertension and/or stroke. Indicated PA screening had not previously been performed despite access to specialist care: 55% had an endocrinologist, cardiologist, and/or nephrologist. 27.4% of the participants had screening results consistent with either “likely confirmed PA” (7.1%) or “possible PA” (20.3%). Of those with both resistant hypertension and hypokalemia, 35.0% met criteria for “likely confirmed” or “possible” PA. A “likely confirmed” diagnosis of PA was associated with a median aldosterone-to-renin ratio of 33 ng/dL per ng/mL/h [IQR: 24, 76] and prevalent cardiometabolic comorbidities (33.3% had diabetes, 20.0% coronary artery disease, 13.3% heart failure). Conclusions: This ongoing, prospective, virtual-based, direct-to-patient PA screening program identified confirmed or possible PA in up to 27.4% of guideline-eligible participants. By reaching patients directly, rather than relying on routine clinical screening that is known to be dramatically underutilized, this study highlights the unrecognized prevalence of PA and demonstrates a novel approach to identifying this common, morbid, and treatable condition across the USA. Presentation: Friday, June 16, 2023

Employment Strategies in Applied Universities: A Case Study of Shanxi Technology And Business College in the New Era


 
 
 
 The rapid expansion of higher education in China has led to a transformation in the employment landscape for college graduates. In response, the government has established a new employment market mechanism that poses challenges for employment guidance departments. The employment outcomes of college students are closely tied to the development and competitiveness of universities. To address these challenges, applied universities must enhance the comprehensive qualities and employability of their students through long-term planning and coordination. This paper examines the employment work in applied universities in the new era, focusing on the case of Shanxi Technology and Business College. The study highlights the requirements for effective employment strategies, including leadership involvement, coordination, and engagement of all staff members. It also discusses the employment statistics of the 2022 graduates from Shanxi Technology and Business College. The paper outlines the employment strategies employed by the college, such as employment guidance classes, improving the quality of employment education, and establishing on-campus and online recruitment platforms. It emphasizes the coordinated efforts and responsibilities of the college leadership, active participation from various departments, transformation of educational guidance, and the implementation of multiple measures for employment success. The college also emphasizes the accurate collection and statistical verification of graduates' employment data.This study provides insights into the employment work in applied universities, offering valuable strategies and practices for navigating the new era of college graduate employment.
 
 
 
 

An improved heterogeneous graph convolutional network for job recommendation

Job recommendation is crucial in online recruitment platforms due to the overwhelming number of job postings. Job seekers spend considerable time and effort searching for suitable employment. With millions of job seekers browsing job postings daily, the demand for accurate and effective job recommendations is more pressing than ever. To address this challenge, we propose IHGCN, an improved semi-supervised heterogeneous graph convolutional network model for job recommendation. IHGCN aims to provide job recommendations for early job seekers based on their resumes. Firstly, we introduce a novel labeling classification standard specifically tailored to early job seeker resumes. Secondly, we construct a heterogeneous resume graph where each resume is represented as a node. Job recommendation is treated as a multi-classification problem. Thirdly, our IHGCN model learns a node representation from the graph to perform effective job recommendations. To evaluate our model, we conduct experiments using a real-world resume dataset obtained from LinkedIn. The results demonstrate that IHGCN outperforms the baselines by around 10%. This study highlights the benefits of leveraging meta-paths within the Graph Convolutional Network model to address the sparsity problem caused by the one-hot representation of nodes.