Data science techniques are powerful tools for extracting knowledge from large datasets. Analyzing the job market by classifying online job advertisements (ads) has recently received much attention. ...Various approaches for multi-label classification (e.g., self-supervised learning and clustering) have been developed to identify the occupation from a job advertisement and have achieved a satisfying performance. However, these approaches require labeled datasets with hundreds of thousands of examples and focus on specific databases such as the Occupational Information Network (O*NET) that are more adapted to the US job market. In this paper, we present a two-stage job title identification methodology to address the case of small datasets. We use Bidirectional Encoder Representations from Transformers (BERT) to first classify the job ads according to their corresponding sector (e.g., Information Technology, Agriculture). Then, we use unsupervised machine learning algorithms and some similarity measures to find the closest matching job title from the list of occupations within the predicted sector. We also propose a novel document embedding strategy to address the issues of processing and classifying job ads. Our experimental results show that the proposed two-stage approach improves the job title identification accuracy by 14% to achieve more than 85% in some sectors. Moreover, we found that incorporating document embedding-based approaches such as weighting strategies and noise removal improves the classification accuracy by 23.5% compared to approaches based on the Bag of words model. Further evaluations verify that the proposed methodology either outperforms or performs at least as well as the state-of-the-art methods. Applying the proposed methodology to Moroccan job market data has helped identify emerging and high-demand occupations in Morocco.
Long working hours and mental health problems among teachers are a concern in Japan. More specifically, it has been reported that junior high school teachers tend to work overtime. In this study, ...examined the working hours of junior high school teachers in public schools and investigated the association between overtime work and stress responses across job titles.
From June to December 2018, 54,772 teachers in public junior high schools completed a web-based nationwide survey regarding occupational stress and submitted self-evaluated working hours per day of the previous month. Psychological and physical stress responses were assessed using the Brief Job Stress Questionnaire.
Results showed that 59.6% of the participants worked 11 h or more per day. Additionally, the length of working hours significantly differed across job titles (χ
(30) = 5295.8,
< 0.001, Cramér's
= 0.14). With respect to tenured teachers, sex (female), age, taking charge of the class, number of working years in the same school, working hours of 10 to 11 h, 11 to 12 h, 12 to 13 h, and 13 h or more were significantly associated with high stress, compared to those who worked less than 9 h per day. Moreover, for fixed-term teachers, sex (female), age, working hours of 9 to 10 h, 10 to 11 h, 11 to 12 h, 12 to 13 h, and 13 h or more were related with more stress as compared to those who worked less than 9 h per day. On the other hand, there was no significant relationship between long working hours and stress response among vice-principals, even though they tended to work the longest hours.
We verified that Japanese junior high school teachers work long hours. Long working hours were associated with stress responses in both tenured and fixed-term teachers, but not in vice-principals. However, vice-principals work the longest hours among teachers, and we suggest that these long working hours may be a hidden problem that is often overlooked.
Introduction: Covid-19 was first reported as a viral disease in China in 2019, and it soon became a global pandemic. In addition, the types of occupations people have and the environments in which ...they work may increase the likelihood of being exposed to the virus. Thus, this study was conducted to investigate the prevalence of Covid-19 disease in various occupations and its relationship with some effective parameters in Shahroud City.
Materials and Methods: This research is a cross-sectional, descriptive-analytical study that was conducted in 2021. The required information was extracted from the database provided by Shahroud University of Medical Sciences. These files contain the results of the comprehensive study of COVID-19 in Shahroud. All the jobs that were asked of the person were classified based on the International Standard Classification of Occupations and suspicious and definite cases were examined in different occupations. The information was analyzed using SPSS software version 22.
Results: According to the results, the highest percentage of cases (45%) was in healthcare workers, such as doctors, nurses, and operating room staff—in other words, all employees working in the healthcare system. The next highest percentages were in household and home nursing workers (44.5%), retirees (43.2%), and construction workers (43%). The relationship between variables such as age, smoking status, presence of comorbidities, and presence of a high body mass index (BMI) associated with Covid-19 disease was examined by the regression test. It was found that the relationship was significant and that these variables affected the prevalence of the disease. Based on the odds ratio in the age variable, with each year of age, the chance of getting infected increased by 1%. A current smoker had a reduced chance of getting the disease by 57.2%. Having a comorbidity increased the chance of getting the disease by 14%, and with each increase in BMI, the chance of getting the disease increased by 3.8%.
Conclusion: The study found that in some occupations, such as healthcare workers, the prevalence of the disease was higher because workers were in direct contact with patients and people infected with the virus. In general, it can be said that the prevalence of the disease was low in workers who could telework and remain at home rather than go to a job or in the community. Factors such as age, the presence of comorbidities, status as a current smoker, and having a high BMI affected contracting Covid-19 disease.
The job market is evolving continuously due to changes in economic landscapes, technological improvements, and skill requirements. In the era of digitalization, a wealth of data is becoming ...available, opening up new opportunities for labor market analysis. Many stakeholders can make informed decisions if they benefit from accurate and timely insights about the job market. However, traditional data sources and methods used for labor market analysis often fall short of capturing the diversity and trends of the evolving job market. Recently, researchers started exploring various data sources by leveraging data science techniques, which makes information extraction achievable. This survey reviews recent research published between 2015 and 2022 on labor market analytics through data science techniques and discusses future research directions. 101 primary studies were classified and evaluated to identify the data sources utilized for job market analysis; the skill extraction methods and their type; the occupation and sector identification methods; and the application of the study conducted. Finally, we explore potential avenues for future research in this area.
•Review of current data science techniques in labor market analysis (LMA).•Understanding the role and significance of different data sources for LMA.•Analyzing skill identification techniques in labor market studies.•Normalizing the job title using hybrid methods shows promising results.•Examines the key advancements and applications of LMA by various stakeholders.
The literature review has clearly indicated that the scale and characteristics of demand for Geography Earth and Environmental Sciences experts across different countries is unknown. Therefore, there ...is an urgent need to investigate this issue. This paper presents the results of research on the real demand for GEES specialists. In the paper, real demand is expressed by job vacancies (N = 17 378) published in six European countries over a period of 18 months. To analyse such an extensive body of text data, we used data mining techniques such as: SVD, inter-factor correlation analysis, word frequency analysis and word significance indicators, which allowed us to recognise similarities and differences in the size and structure of demand for these specialists in specific groups of countries. Employers from the UK and Ireland offered the most comprehensive range of positions whereas the expectations of Polish employers were the least diverse. Word frequency analyses for each occupation group demonstrated which components of GEES experts' knowledge and skills are considered universal on the labour market and which are subject to substantial regional variations. Moreover, word significance analyses allowed us to identify the occupations where a wider range of general skill areas was required and the positions for which primarily geographic skills were in demand.
Background
In many research studies, the identification of social determinants is an important activity, in particular, information about occupations is frequently added to existing patient data. ...Such information is usually solicited during interviews with open-ended questions such as “What is your job?” and “What industry sector do you work in?” Before being able to use this information for further analysis, the responses need to be categorized using a coding system, such as the Canadian National Occupational Classification (NOC). Manual coding is the usual method, which is a time-consuming and error-prone activity, suitable for automation.
Objective
This study aims to facilitate automated coding by introducing a rigorous algorithm that will be able to identify the NOC (2016) codes using only job title and industry information as input. Using manually coded data sets, we sought to benchmark and iteratively improve the performance of the algorithm.
Methods
We developed the ACA-NOC algorithm based on the NOC (2016), which allowed users to match NOC codes with job and industry titles. We employed several different search strategies in the ACA-NOC algorithm to find the best match, including exact search, minor exact search, like search, near (same order) search, near (different order) search, any search, and weak match search. In addition, a filtering step based on the hierarchical structure of the NOC data was applied to the algorithm to select the best matching codes.
Results
The ACA-NOC was applied to over 500 manually coded job and industry titles. The accuracy rate at the four-digit NOC code level was 58.7% (332/566) and improved when broader job categories were considered (65.0% at the three-digit NOC code level, 72.3% at the two-digit NOC code level, and 81.6% at the one-digit NOC code level).
Conclusions
The ACA-NOC is a rigorous algorithm for automatically coding the Canadian NOC system and has been evaluated using real-world data. It allows researchers to code moderate-sized data sets with occupation in a timely and cost-efficient manner such that further analytics are possible. Initial assessments indicate that it has state-of-the-art performance and is readily extensible upon further benchmarking on larger data sets.
Perceived organizational support has been linked to employee commitment and job satisfaction. Understanding the effects of perceived organizational support on employees allows leaders to improve ...employees' performance and the success of their organizations. The purpose of this study was to identify the perceived organizational support across different respiratory care education programs in the United States.
All chairs and program directors of bachelor's of science and master's of science degree respiratory care education programs in the United States were surveyed (
= 97). The Survey of Perceived Organizational Support was modified after written approval, and the final instrument included 31 items with a Likert scale (1 = strongly disagree, 7 = strongly agree). Descriptive statistics, multiple regression, and topic modeling were used for data analysis (
< .05).
A total of 67 respondents responded to the perceived organizational support survey; a 69% response rate. They were satisfied with their job and committed to their institutions. They also reported that faculty salaries were equitable relative to the national average, and their institutions encouraged teamwork among faculty. The respondents' titles, total years of administrative experience, students' scores on the national credentialing therapist multiple choice examination (TMC), and institutions that offer both bachelor's of science and master's of science degree programs had a direct relationship with perceived organizational support in respiratory care education programs. Age and sex were inversely related to perceived organizational support. A topic modeling analysis based on the respondents' opinions about perceived organizational support showed that the respondents frequently mentioned the words support, institution, budget, year, nursing, and experience. The respondents emphasized the importance of support, institution marketing, their years of experience, and the program budget. They also mentioned that nursing programs overshadowed respiratory care education programs at their institutions.
Age, sex, job title, years of administrative experience, students' TMC scores, and the type of programs offered impacted perceived organizational support by respiratory care directors. Student-, program- and participant-related factors can be used to improve perceived organizational support in respiratory care education.
We examine the association between union density and wages in Portugal where just 10 percent of all workers are union members but nine-tenths of them are covered by collective agreements. Using a ...unique dataset on workers, firms, and collective bargaining agreements, we examine the union density wage gap in total monthly wages and its sources – namely, worker, firm, and job-title or ‘occupational’ heterogeneity – using the Gelbach decomposition. The most important source of the mark-up associated with union density is the firm fixed effect, reflecting the differing wage policies of more and less unionized workplaces, which explains two-thirds of the wage gap. Next in importance is the job-title fixed effect, capturing occupational heterogeneity across industries. It makes up one-third of the gap, the inference being that the unobserved skills of workers contribute at most only trivially to the union density wage gap. In a separate analysis based on disaggregations of the total wage, it is also found that employers can in part offset the impact of the bargaining power of unions on wages through firm-specific wage arrangements in the form of the wage cushion. Finally, union density is shown to be associated with a modest reduction in wage inequality as the union density wage gap is highest among low-wage workers. This result is driven by the job-title fixed effect, low-wage workers benefiting more from being placed in higher paying ‘occupations.’
An increased risk of mesothelioma has been reported in various countries for construction workers. The Italian National Mesothelioma Registry, from 1993 to 2018, reported exposure exclusively in the ...construction sector in 2310 cases. We describe the characteristics of these cases according to job title.
We converted into 18 groups the original jobs (N=338) as reported by ISTAT codes ('ATECO 91'). The exposure level was attributed at certain, probable and possible in accordance with the qualitative classification of exposure as reported in the Registry guidelines. Descriptive analysis by jobs highlights the total number of subjects for each single job and certain exposure, in descending order, insulator, plumbing, carpenter, mechanic, bricklayer, electrician, machine operator, plasterer, building contractor, painter and labourer.
The cases grow for plumbing in the incidence periods 1993-2018, while, as expected, it decreases for insulator. Within each period considered the most numerous cases are always among bricklayers and labourers, these data confirm the prevalence of non-specialised "interchangeable" jobs in Italian construction sector in the past.
Despite the 1992 ban, the construction sector still presents an occupational health prevention challenge, circumstances of exposure to asbestos may still occur due to incomplete compliance with prevention and protection measures.
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