Occupational Categories Research Articles

Analysis of the international classification of occupations for the health informatics and information management workforce.

With the increasing adoption of digital health, health informatics and information management (HIIM) professionals are more essential than ever before. This paper explores the difficulties encountered when trying to classify and track the HIIM workforce internationally. A historic document analysis method to explore how the International Standard of Classifications (ISCO) and the available national standards from countries using an alternative classification represent the HIIM workforce. There is little recognition of the HIIM workforce in the ISCO and national standards. Where an entry did exist, more than 90% were in a non-health major group. At the major group level, 16.7% (7/42) of entries specified an administrative term, 11.9% (5/42) a professional term, 9.5% (4/42) a technician term and 61.9% (26) including both professional and technician as the description. Most country-level classification systems classified HIIM as a technical occupation rather than a professional occupation. The term 'digital health' did not appear in any classification reviewed. As the requirement for formal training increases globally in the HIIM occupation, classification systems will need to include or reclassify HIIM to reflect it as a professional occupation, with a clear structure and requiring specific education and specialised skills.

Remote sensing applied to biophysical parameters and land cover to identify urban heat islands in Recife (PE), Brazil

Urban growth results in several changes, mainly related to demographic, social, economic, and environmental aspects, leading to a new connotation in the use and occupation of land. This new scenario impacts the local energy balance, creating what is called an “urban heat island”. This study aims to investigate the occurrence of urban heat islands in Recife city, the capital of Pernambuco, Brazil, based on the processing of biophysical parameters, the classification of land use and occupation, and surface temperature. The orbital images of the study region were obtained and processed using the Google Earth Engine cloud processing platform from 2013 to 2021. The results showed an increase in areas with greater urban density and a reduction in areas with vegetation. It was realized that in regions with greater urban density, the surface temperature observed was up to 5.20°C higher than in the area with vegetation.

Development of Korean CARcinogen EXposure: occupation-based exposure matrix.

Information systems focusing on occupational carcinogen exposure, whether categorized by industry or occupation, play a pivotal role in the prevention of occupational cancers. Recently, the Korean CARcinogen EXposure (K-CAREX) system was developed to assess carcinogen exposure by industry. However, corresponding information categorized by occupation has yet to be developed. This study aimed to develop an occupation-based exposure matrix for occupational carcinogens as an extension of the K-CAREX framework. The Work Environment Measurement Database (WEMD) and the Special Health Examination Database (SHED) were combined to estimate exposure intensity by occupation. The WEMD supplied exposure levels, whereas the SHED provided occupation information. Additionally, the SHED served as the primary data source for constructing an indicator of exposure prevalence by occupation, utilizing standard occupational classification. A total of 22 carcinogens were selected for evaluating exposure intensity, and 20 carcinogens were selected for assessing exposure prevalence. Exposure intensity and prevalence were assigned to 156 occupational groups based on these carcinogens. For instance, in terms of welding fume exposure, welders were assigned an exposure intensity rating of 3 and exhibited exposure prevalence of 26%. Our findings complement the previously developed K-CAREX, which offered an industry-based exposure matrix. The resultant comprehensive K-CAREX, incorporating both industry- and occupation-based matrices, can be utilized for occupational cancer prevention and epidemiological studies.

Comparing countries, exporting classifications, surpassing methodological nationalism: Class, gender, and education gaps in and between France and Portugal

The ‘globalization turn’ in the sociology of class has led to the resurgence of studies comparing social classes in Europe over the past 20 years and to question the methodological nationalism of the class analysis. But it has also paid little attention to the selection of the most appropriate empirical tools for quantifying class in a comparative approach. This article explores the links between occupations, class structures, and countries by applying the French and the Portuguese occupational classifications to both countries. France and Portugal are an especially suitable pairing for comparative study because social class and inequalities are central to sociological research in both countries, and their divergent intellectual histories produced different tools for quantifying social stratification. The Portuguese classification, which attaches great importance to the ownership of capital, helps to understand the diversity of the class position of the self-employed and the structure of the economic elites. The French classification, based on employment status and qualifications, highlights the role of diplomas and the public/private divide. Ultimately, the Portuguese social structure appears to be more polarized than the French one. This cross-national and cross-classification comparison brings to light features of the social structures of the two countries and the contrasts between them that would have remained a blind spot if we had used an international classification. Paradoxically, these national classifications help to overcome methodological nationalism and ethnocentrism.

Gendered Job Search: An Analysis of Gender Differences in Reservation Wages and Job Applications

Using a weekly survey of unemployed workers, this study examines gender differences in reservation wages and applied-for occupational categories. The analysis shows that a large portion of the gender difference in reservation wages is attributable to the fact that women and men search for different occupations. Findings further demonstrate that women are more likely to apply to occupations with higher percentages of female incumbents, and they target these occupations from the earlier stages of their job search, rather than adjusting their preferences over the course of unemployment. Further analysis shows that women are more likely to apply for occupational categories that offer greater flexibility in working hours, more opportunities for interpersonal interactions, less critical decision-making, and a less competitive environment. Finally, the analysis reveals that household responsibilities, particularly the number of children, and willingness to take risks are important factors influencing women’s decisions to pursue occupations with less demanding work hours.

Carpal tunnel syndrome severity and work: a case-control study.

Carpal tunnel syndrome (CTS) is a socially relevant condition. Risk factors associated with CTS severity and work have not been explored. This case-control study aims to investigate the association between CTS severity and occupational biomechanical overload considering personal anthropometric risk factors. We consecutively enrolled one CTS case for two controls. CTS cases were grouped into three classes of progressive clinical and electrophysiological severity according to two validated scales. Job titles were coded according to the International Standard Classification of Occupations (ISCO 88) and grouped into two broad socio-occupational categories: blue-collar and white-collar workers.The associations between CTS (or CTS severity) and blue-collar status were assessed using unconditional (or multinomial) logistic regression models adjusted for age, gender, centre and two anthropometric indexes: wrist-palm ratio and waist-stature ratio. Odds ratios (OR) or relative risk ratio and 95% confidence intervals (95% CI) were calculated, as appropriate. We included 183 cases and 445 controls. Blue-collar status was a risk factor for CTS (OR 2.4; 95% CI 1.5-3.8). Among job titles, vine and/or olive tree growers (OR 6.0; 95% CI 2.0-17.9) and food processing workers (OR 4.8; 95% CI 1.5-15.2) were at higher risk. At multinomial logistic regression analysis, blue-collar status and the two anthropometric indexes were associated with moderate/severe CTS, after mutual adjustment. Blue-collar workers showed a higher risk of CTS than white-collar workers, adjusting for anthropometric and body measures as well. Preventive interventions should be addressed to decrease the biomechanical overload of the upper limbs and limit the overweight.

DISP-05. SUPERVISED MACHINE LEARNING PREDICTION OF THERAPEUTIC CLINICAL TRIAL ENROLLMENT FOR MINORITY PATIENTS AND WOMEN IN NEURO-ONCOLOGY

Abstract BACKGROUND Despite changes in national policy, women and minority patients remain under-accrued in glioma clinical trials. Supervised machine learning may serve as a novel approach to understanding which variables and their complex interplay most influence enrollment for underrepresented patients. We aimed to design and externally validate machine learning models to predict therapeutic clinical trial enrollment for a) all patients, b) women, and c) NIH-designated minority patients with low- and high-grade glioma. METHODS In a 20-year retrospective cohort of 1042 adult patients with low- and high-grade glioma, boosted neural network (BNN) models for the whole, women, and minority cohorts were used to analyze the combined effects of demographic, socioeconomic, and oncologic variables on clinical trial enrollment. These models were validated using an external cohort (n=103). RESULTS In the development cohort (445 [42.7%] women; 151 [14.5%] minorities; median age, 51.5 [interquartile range: 39.4-62.5] years), 350 patients (33.6%) enrolled in a therapeutic clinical trial. The whole cohort BNN (AUC: 0.827, misclassification rate (MR): 0.175), the women cohort BNN (AUC: 0.895, MR: 0.125), and the minority cohort BNN (AUC: 0.983, MR: 0.053) models accurately predicted trial enrollment on external validation. For the whole cohort and women models, several demographic (race, ethnicity, minority status, sex), socioeconomic (insurance, employment status), and oncologic variables (seizure, chemotherapy, KPS, WHO grade, tumor lobe) were equally influential (Main effect (ME): 1.000, Total effect (TE): 1.000) on enrollment. For the minority model, socioeconomic variables including insurance status (ME: 0.089, TE: 0.417) and occupation classification (ME: 0.087, TE: 0.381) drove enrollment. CONCLUSION We designed and externally validated the first machine learning model for predicting therapeutic clinical trial enrollment for patients with diffuse glioma. Model features driving trial enrollment varied for underrepresented patients. These results may help prioritize personalized patient-specific initiatives to address barriers to equitable patient accrual.

Case Study of Transforming and Scaling Entrepreneurial Internship Access in IHE

This case study explores innovative approaches to expand access to high-quality internships and improve equitable student outcomes. It focuses on Lehigh University’s virtual Innovation Internship program, partnered with the Nasdaq Entrepreneurial Center and Epixego Inc. The program aims to address challenges in traditional internships, such as hidden curriculum, false narratives, and unequal social environments. By incorporating self-assessment of learning competencies and enhancing student agency, the program doubled its capacity to 40 students. The study measured student self-efficacy, occupational identity choice, and outcome expectations based on various factors. Results showed that students changed internship preferences across occupation categories and experienced increases in entrepreneurial self-efficacy (3%), occupational identity (7%), and perception of entrepreneurship education (4%). This multifaceted approach demonstrates the potential for innovative internship designs to improve accessibility and student development outcomes.

Unboxing Occupational Bias: Debiasing LLMs with U.S. Labor Data

Large Language Models (LLMs) are prone to inheriting and amplifying societal biases embedded within their training data, potentially reinforcing harmful stereotypes related to gender, occupation, and other sensitive categories. This issue becomes particularly problematic as biased LLMs can have far-reaching consequences, leading to unfair practices and exacerbating social inequalities across various domains, such as recruitment, online content moderation, or even the criminal justice system. Although prior research has focused on detecting bias in LLMs using specialized datasets designed to highlight intrinsic biases, there has been a notable lack of investigation into how these findings correlate with authoritative datasets, such as those from the U.S. National Bureau of Labor Statistics (NBLS). To address this gap, we conduct empirical research that evaluates LLMs in a “bias-out-of-the-box” setting, analyzing how the generated outputs compare with the distributions found in NBLS data. Furthermore, we propose a straightforward yet effective debiasing mechanism that directly incorporates NBLS instances to mitigate bias within LLMs. Our study spans seven different LLMs, including instructable, base, and mixture-of-expert models, and reveals significant levels of bias that are often overlooked by existing bias detection techniques. Importantly, our debiasing method, which does not rely on external datasets, demonstrates a substantial reduction in bias scores, highlighting the efficacy of our approach in creating fairer and more reliable LLMs.

NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP Classifiers

AI regulations are expected to prohibit machine learning models from using sensitive attributes during training. However, the latest Natural Language Processing (NLP) classifiers, which rely on deep learning, operate as black-box systems, complicating the detection and remediation of such misuse. Traditional bias mitigation methods in NLP aim for comparable performance across different groups based on attributes like gender or race but fail to address the underlying issue of reliance on protected attributes. To partly fix that, we introduce NLPGuard, a framework for mitigating the reliance on protected attributes in NLP classifiers. NLPGuard takes an unlabeled dataset, an existing NLP classifier, and its training data as input, producing a modified training dataset that significantly reduces dependence on protected attributes without compromising accuracy. NLPGuard is applied to three classification tasks: identifying toxic language, sentiment analysis, and occupation classification. Our evaluation shows that current NLP classifiers heavily depend on protected attributes, with up to 23% of the most predictive words associated with these attributes. However, NLPGuard effectively reduces this reliance by up to 79%, while slightly improving accuracy.

What drives inequalities in Low Emission Zones’ impacts on job accessibility?

Low-emission zones (LEZs) aim to improve urban air quality and reduce emissions but often face public opposition due to their regressive impacts on accessibility. However, the causes of these regressive impacts remain poorly understood. This study investigates the factors driving inequalities in the impacts of LEZs on job accessibility across occupational categories in eight French cities. Using ex-ante open-source data, it computes expected job accessibility losses due to LEZs per occupational category. Additionally, it provides a counterfactual decomposition of the disparities in LEZs’ impacts between six drivers: ownership of polluting vehicles, workers’ residences and workplaces within the LEZ, accessibility of workers’ homes and workplaces via public transportation, and feasibility of active transportation modes for commuting between homes and workplaces. The findings reveal that LEZs are predominantly regressive in six out of the eight cities examined. Despite a higher concentration of high-income workers and jobs within LEZs, resulting in significant accessibility losses for this group, low-income workers bear a greater burden due to the limited availability of public transportation near their residences and workplaces, longer commutes to work, and higher shares of polluting vehicles. These findings help inform potential complementary policies to address the regressive effects of LEZs.

Analisis Pengaruh Penempatan Shearwall Pada Gedung Hotel Shafira

Hotel Shafira is a flat-shaped building with 10 floors, 12 meters wide and 72 meters long. These structures are prone to significant horizontal irregularities, reducing the overall efficiency of the building. The irregularity is partly due to the uneven distribution of the mass, as seen from the difference in the span of the portal between the back and front of the building which produces large cavities. Analysis with the ETABS program revealed that the eccentricity between the center of self and the center of mass reached 5 meters in the direction of the strong axis. This study overcomes the uneven load distribution by repositioning the shearwall through a trial and error approach, reducing eccentricity from 4 meters to 1.3 meters. This change not only increases the building's capacity, but also reduces the need for reclamation by 13.9% and shows the results of the pushover analysis in the Immediate Occupancy category. The placement of shearwalls has a significant influence on the capacity and stability of the building structure.

Long-term trends of occupational physical activity intensity in Japan

Objectives Although physical inactivity is a global concern, quantitative data on its long-term trends in physical activity (PA) are limited. This study aimed to estimate long-term trends in the intensity of occupational PA, constituting the largest portion of PA in Japan.Methods Data on the number of workers in Japan by occupational category were obtained from the Labour Force Survey. PA intensity (metabolic equivalents [METs]) was assigned to 329 occupations in the Japanese Standard Occupational Classification based on the methods and data of Tudor-Locke et al. (2011), and representative intensity values for 11 occupational categories were calculated. Based on intensity, occupational categories were reclassified into sedentary (≤1.5 METs), light (1.6-2.9 METs), and moderate (≥3 METs). Trends in the prevalence of workers in each category were analyzed, along with the annual mean occupation-related METs from 1953 to 2022. The mean occupation-related METs represented the weighted average of PA intensity, calculated based on the annual worker population for each occupational category. Results From 1953-2022, the prevalence of moderate-intensity occupations significantly declined, whereas sedentary and light-intensity occupations increased. The mean occupation-related METs decreased continuously for 70 years. During the 48-year period from 1962 to 2010, when there were no major changes in occupational classification methods, there was a decline of 0.25 METs, representing a 9.6% decrease, from 2.60 to 2.35 METs.Conclusion Over the past 70 years, occupations in Japan have transitioned to ones with lower PA intensity. Consequently, the average PA intensity across all occupations decreased by at least one-tenth. Since this study assumes a constant intensity for each occupation, the actual decline in occupational PA intensity may be even more significant because of societal automation.

Education and women in high-skill occupations in India

Purpose This paper aims to examine women’s likelihood of being employed in a high-skill occupation compared to men in India. It also analyses the impact of education on women’s likelihood of entering a high-skill occupation. Design/methodology/approach This study uses data from Periodic Labour Force Survey 2018–19. National Classification of Occupations 2004 is used to classify the occupations in three categories, i.e. high-skill, medium-skill and low-skill. The paper uses ordered probit with Heckman’s sample selection method to estimate the impact of gender identity and education on women’s likelihood of entering high-skill occupations. Findings The results show that female workers are more likely to be employed in high-skill occupations than male workers. Formal years of schooling and technical education further improve women’s probability of working in high-skill occupations compared to men. However, an increase in monthly per capita expenditure of the households improves men’s chances of being employed in high-skill occupations in comparison to women. It suggests that gender norms are still relevant. Originality/value Studies have found occupational segregation in India with a large share of women being employed in low-wage work. This study examines the issue by focusing on the skill level of occupations. It found that women have a higher likelihood of entering high-skill occupations in India and education is playing an important role in it.

Excessive occupational sitting increases risk of cardiovascular events among working individuals with type 1 diabetes in the prospective Finnish Diabetic Nephropathy Study

BackgroundSedentary behavior, such as excessive sitting, increases risk of cardiovascular disease and premature mortality in the general population, but this has not been assessed in type 1 diabetes. Occupational sitting is increasingly ubiquitous and often constitutes the largest portion of daily sitting time. Our aim was to identify clinical factors associated with excessive occupational sitting in type 1 diabetes and, in a prospective setting, to explore its association with cardiovascular events and all-cause mortality, independent of leisure-time physical activity.MethodsAn observational follow-up study of 1,704 individuals (mean age 38.9 ± 10.1 years) from the Finnish Diabetic Nephropathy Study. Excessive occupational sitting, defined as ≥ 6 h of daily workplace sitting, was assessed using a validated self-report questionnaire. Data on cardiovascular events and mortality were retrieved from national registries. Multivariable logistic regression identified independently associated factors, while Kaplan-Meier curves and Cox proportional hazard models were used for prospective analyses.ResultsFactors independently and positively associated with excessive occupational sitting included a high occupational category [OR 6.53, 95% CI (4.09‒10.40)] and older age [1.02 (1.00‒1.03)], whereas negatively associated factors included current smoking [0.68 (0.50‒0.92)], moderate albuminuria [0.55 (0.38‒0.80)], and high leisure-time physical activity [0.52 (0.36‒0.74)]. During a median follow-up of 12.5 (6.5–16.4) years, 163 individuals (9.6%) suffered cardiovascular events, and during a median follow-up of 13.7 (9.4–16.6) years, 108 (6.3%) deaths occurred. Excessive occupational sitting increased cardiovascular event risk (hazard ratio [HR] 1.55 [95% CI 1.10‒2.18]) after adjustment for confounders and other covariates. Furthermore, in a stratified multivariable analysis among current smokers, excessive occupational sitting increased the risk of all-cause mortality (2.06 [1.02‒4.20]).ConclusionsExcessive occupational sitting is associated with a higher risk of cardiovascular events and all-cause mortality in individuals with type 1 diabetes. This association persists regardless of leisure-time physical activity, after adjusting for independently associated variables identified in our cross-sectional analyses. These findings underscore the need to update physical activity guidelines to better address sedentary behavior and improve outcomes for individuals with type 1 diabetes. Targeting occupational sitting should be considered a key focus for interventions aimed at reducing overall sedentary time.

Workplace Aggression and Workaholism: Exploring Perceived Occupational Category Differences

Workplace Aggression and Workaholism: Exploring Perceived Occupational Category Differences

The role of health literacy in adherence to the Mediterranean diet

Abstract Background Health literacy has emerged as a crucial concept in public health. It is considered a modifiable determinant influencing health-related decision. One such decision is the adoption of specific dietary patterns. The Mediterranean diet has garnered recognition as one of the healthiest diets. This study aims to explore the association between health literacy levels among adults in socially vulnerable contexts and their adherence to the Mediterranean diet. Methods This study is part of a cluster-randomized trial conducted in primary schools from socially vulnerable contexts. A total of 469 parents of school-aged children agreed to participate. Health literacy was assessed using the Health Literacy Survey Questionnaire. Adherence to the Mediterranean diet was evaluated using the MEDAS questionnaire. Anthropometric and sociodemographic variables were self-reported via questionnaire. Parental BMI was calculated using weight/height^2. Education level was categorized into two groups (less than higher education and higher education), while professional status was classified based on the occupational categories of collar color. Generalized linear models were utilized for the analysis. Results Individuals with higher levels of health literacy present a greater odd to adhere to the Mediterranean diet (B = 0.022, 95% CI 0.010-0.035), even after adjusting for potential confounders (B = 0.019, 95% CI 0.006-0.032). Conclusions Health literacy emerges as a crucial factor influencing individuals’ dietary choices, as evidenced by its association with higher adherence to the Mediterranean diet. These findings underscore the importance of implementing policies and initiatives aimed at promoting health literacy, particularly among individuals from socially vulnerable contexts. Enhancing public health interventions to bolster health literacy should be prioritized to foster better dietary decision-making. Key messages • Health literacy plays an important role in the adoption of Mediterranean diet among adults from socially vulnerable contexts. • Implementing policies to aim the promotion of health literacy, particularly among individuals from socially vulnerable contexts are necessary.

Circumstantial Factors Among Kentucky Nurse Suicide Decedents, 2005 to 2019.

Background: Nurse suicide, a complex occupational health concern, is urgently in need of research due to the personal and occupational suicide risk factors experienced by nurses, namely mental health problems (e.g., depression), job problems, and substance misuse. Therefore, the study aims were to determine the contextual characteristics and circumstantial factors associated with nurse suicide in Kentucky. Methods: Secondary suicide data were obtained from the Kentucky Violent Death Reporting System (KYVDRS) from 2005 to 2019. Nurse suicide cases were identified using the Bureau of Labor Statistics Standard Occupational Classification. A mixed-methods analysis using descriptive statistics and qualitative evaluation was conducted to determine the distributions of demographic, injury, and weapon characteristics, followed by a qualitative analysis of the KYVDRS incident narrative text of nurse suicide decedents. Results: There were 88 decedents identified with a nursing occupation. The predominant means of death for male (59%) and female (45%) nurses involved firearms. Thematic analysis showed nurse decedents experienced a mental health problem (51%) and premeditated (50%) suicide preceding death. Incidentally, 51% of all cases experienced multiple circumstantial factors (e.g., relationship problems and premeditation) preceding their death by suicide. Discussion/Application to Practice: Nurse suicide decedents experienced multifactorial risk factors preceding their death by suicide, namely depression, premeditated suicide, and relationship problems. Occupational health practitioners and health care organizations can implement individual and organizational prevention efforts to help prevent nurse suicide.

Personality profiles of 263 occupations.

While personality trait assessments are widely used in candidate selection, coaching, and occupational counseling, little published research has systematically compared occupations in personality traits. Using a comprehensive personality assessment, we mapped 263 occupations in self-reported Big Five domains and various personality nuances in a sample of 68,540 individuals and cross-validated the findings in informant ratings of 19,989 individuals. Controlling for age and gender, occupations accounted for 2%-7% of Big Five variance in both self-reports and informant reports. Most occupations' average Big Five levels were intuitive, replicated across rating methods, and were consistent with those previously obtained with a brief assessment in a different sociocultural context. Often, they also tracked the Occupational Information Network database's work style ratings and clustered along the International Standard Classification of Occupation's hierarchical framework. Finally, occupations with higher average levels of the personality domains typically linked to better job performance tended to be more homogeneous in these domains, suggesting that jobs with higher performing incumbents are often more selective for personality traits. Several personality nuances had intuitive occupational differences that were larger than those of the Big Five domains (explaining up to 12% variance) and replicated well across rating methods, providing more detailed insights into how job incumbents vary in personality. We provide an interactive application for exploring the results (https://apps.psych.ut.ee/JobProfiles/) and discuss the findings' theoretical and practical implications. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Contemporary Chinese vocational interest scale in the digital age: development and validation

Holland’s Realistic, Investigative, Artistic, Social, Enterprising, and Conventional (RIASEC) model has been widely used for career counseling, vocational assessment, and educational choice. However, vocational interest types can be affected by culture, and the fourth industrial revolution has led to the emergence of many new occupations, rendering many occupational titles and work tasks in Holland’s RIASEC model outdated. The present study proposed nine interest types in contemporary China: Artistic (A), Natural (N), Enterprising (E), Conventional (C), Technological (T), Investigative (I), Powerful (P), Social (S), and Operational (O), and developed a vocational interest scale with nine subscales. We generated items by drawing from a literature review, occupational categories, and expert reviews, used exploratory factor analysis (EFA, N = 628, Mage = 12.75 years; 47% girls) to reduce the number of items, and confirmatory factor analysis (CFA, N = 627, Mage = 12.76 years; 48% girls) to validate the factor structure. This study clarified vocational interest types in contemporary China, developed and validated a vocational interest scale for the digital age, and provided a measurement tool for educational choice and career counseling.