Employment History Research Articles

Career progression for autistic people: A scoping review.

Lots of autistic people are unemployed. Even when they are employed, autistic people might be given fewer opportunities than non-autistic people to progress in their careers. For example, assumptions about autistic people's differences in social communication might mean they are not given as many promotions. Indeed, we know that many autistic people are in jobs lower than their abilities (known as 'underemployment'). We reviewed 33 studies that tell us something about career progression for autistic people. Our review found that lots of autistic people want to progress in their careers, but there are many barriers in their way. For example, when they told their employer about being autistic, some people were given fewer opportunities. Research has also shown that autistic people do not get enough support to progress and that gaps in their employment history can make it difficult to progress. Our review suggested that good employment support (e.g. mentors) might help autistic people to progress in their careers. However, not much research has evaluated employment support for autistic people, which means we do not know how useful it is. Future research should find the best support that allows autistic people to live and work in ways that are meaningful to them.

Updated Results from the Retrospective CREST Study on the Safety and Effectiveness of 8-Week Glecaprevir/Pibrentasvir in HCV-Infected Treatment-Naïve Patients with Compensated Cirrhosis.

This brief report presents updated findings from the previously published CREST study evaluating the safety and effectiveness of 8-week glecaprevir/pibrentasvir (GLE/PIB) in treatment-naïve patients with chronic hepatitisC virus (HCV) infection and compensated cirrhosis. The current study includes an additional 51 patients, presents effectiveness data stratified by additional comorbidities and comedications, and offers insights into healthcare resource utilization. Analysis of treatment-naïve patients with HCV infection and compensated cirrhosis enrolled in the CREST study, a real-world, observational multicenter study. All enrolled patients were included in the full analysis set (FAS); the modified analysis set (MAS) excluded patients with missing SVR12 data, or who discontinued GLE/PIB for nonvirologic failure. The primary endpoint was sustained virologic response at posttreatment week12 (SVR12) in the MAS. Safety and healthcare resource utilization were also assessed. The FAS included 437 patients, and the MAS 375. Overall, the results were consistent with the previously published study, with 98.9% of patients in the MAS achieving SVR12. Patients with comorbidities such as alcoholism, diabetes, and hyperlipidemia achieved SVR12 rates > 94%. High SVR12 rates were also achieved by patients receiving comedications such as anxiolytics, antidepressants, and opioid agonists. Of the 26.8% of patients with an adverse event, 1.1% had a serious adverse event, none of which were deemed related to GLE/PIB. Healthcare resource utilization varied by employment status and history of drug use. Active drug users had more physician and nurse visits than specialist visits compared with former drug users. This study provides further evidence on the safety and effectiveness of 8-week GLE/PIB, supporting the use of shorter treatment in treatment-naïve patients with Child-PughA cirrhosis including subgroups of interest, regardless of comorbidities and comedications observed in this population. The variable healthcare resource utilization in different patient types can help plan and resource linkage to care better, thus supporting HCV elimination efforts.

Understanding labour exploitation as a social determinant of health in migrant populations – insights from the Danish population-based registers

Abstract Despite recent global events such as the COVID-19 pandemic and the Qatar world cup drawing attention to the significant loss of human lives of labour migrants in precarious working conditions, evidence on the comparative mortality of migrant workers remains sparse and inconsistent. Studying the comparative mortality of migrant workers with local workers is challenging for several reasons. Data on migrants’ employment conditions and health outcomes are often incomplete, and when available captured in separate databases. In addition, there is a need to disentangle the healthy migrant effect from the healthy worker effect, which presents challenges in the selection of reference populations and temporality when studying mortality of migrants. In view of such challenges, Denmark provides an ideal context to study labour migration and mortality. The Danish population-based registers offer linked longitudinal data on migrants’ employment history and mortality outcomes. Denmark’s migrant integration policy also has a history of weaving immigration and labour policies, and migrants in Denmark continue to be prone to labour exploitation in high-risk industries such as construction and agriculture. This presentation shall use Denmark as a case study to compare the mortality outcomes of migrant workers with Danish workers to better understand labour exploitation as a social determinant of health.

Dietary Guidelines for Pregnant and Lactating Women, Adherence Levels, and Associated Factors: a Scoping Review.

This scoping review addresses gaps in the existing literature on dietary guidelines for pregnant and lactating women globally. The study delves into adherence levels, identifies influencing factors, and examines outcomes associated with these guidelines. Analysing food-based dietary guidelines (FBDGs) from around the world, the review reveals that half of the countries lack FBDGs, with only 15% providing tailored advice for pregnant and lactating women. Utilising data extracted from 47 articles across MEDLINE and EMBASE, the study highlights a scarcity of adherence studies, particularly in low- and middle-income countries (LMICs) and emphasises the lack of research during lactation. Overall adherence to dietary guidelines is low, with disparities in fruit, vegetable, whole grain, and fish consumption. Positive correlations with adherence include age, education, employment, social class, and certain medical histories, while negative correlations involve smoking, alcohol consumption, metropolitan residence, and elevated BMI. The study documented significant associations between adherence and reduced risks of gestational complications but calls for further exploration of intermediate nutritional outcomes like micronutrient deficiencies and child growth. Emphasising the urgency for globally standardised guidelines, especially in LMICs, this review provides a foundational call for prioritised studies and strategies to enhance dietary practices for pregnant and lactating women worldwide.

Examining factors that hinder the efficacy of responsible gambling messages: modeling predictors and effects of responsible gambling message fatigue

ABSTRACT Message fatigue is an aversive psychological state that is characterized as feelings of boredom resulting from repeated exposure to messages promoting the same health behavior. Because responsible gambling messages are widely distributed, it is possible that feelings of fatigue could reduce their efficacy. This cross-sectional study of N = 822 U.S. adults recruited from an online panel (i.e. Amazon’s Mechanical Turk) explores potential predictors of fatigue and examines the effects of fatigue on gambling related outcomes through reactance and inattention using a structural equation modeling approach. The results indicated that industry employment history, problem gambling status, and gambling frequency emerged as significant predictors of fatigue. Fatigue was significantly associated with participants’ psychological reactance and inattention to responsible gambling messages. Both reactance and inattention were associated with participants’ gambling intentions and intentions to share responsible gambling messages with others. Responsible gambling messages could be improved by using strategies that can help to mitigate feelings of fatigue.

Evaluation of exposure to volatile organic compounds (BTEX) and Polycyclic Aromatic Hydrocarbons (PAHs) in gas station workers and oxidative stress assessment in Karaj city

Gas stations are one of the sources of benzene, toluene, ethylbenzene and xylene (BTEX) and polyromantic hydrocarbons (PAHs). The present study was conducted with the aim of evaluating the level of breathing exposure of gas station workers to BTEX, PAHs and oxidative stress caused by exposure to these compounds in Karaj city. Oxidative stress and reactive oxygen species (ROS) is one of the mechanisms involved in the toxicity caused by exposure to gas vapors. In this study, all 25 gas stations in the city of Karaj were investigated. Personal sampling and analysis of BTEX and PAHs was done according to National Institute for Occupational Safety and Health (NIOSH) 1501 and 5515 methods, respectively. Finally, oxidative stress markers were investigated in 25 gas station workers and 25 control group. The results showed that the mean age and employment history of gas station workers are 39.96 ± 9.55 and 12.84 ± 6.92, respectively. Also, no significant difference was reported between gas station workers and control subjects in terms of oxidative stress parameters including the level of ROS, oxidized glutathione (GSSG) content, malondialdehyde (MDA) and reduced glutathione (GSH) content. The concentration values of personal exposure of gas station workers to BTEX and PAHs are lower than the occupational exposure limits (OEL). Although the level of oxidative stress parameters in gas station workers is higher than the control group, this difference is not statistically significant (p>0.05). It is recommended to take personal protection measures in case of chronic exposure.

The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market

Generative artificial intelligence (AI) holds the potential to either complement workers by enhancing their productivity or substitute them. We examine the short-term effects of the recently released generative AI models (ChatGPT, DALL-E 2, and Midjourney) on the employment outcomes of freelancers on a large online platform. We find that freelancers in highly affected occupations suffer from the introduction of generative AI, experiencing reductions in both employment and earnings. We find similar effects studying the release of other image-based generative AI models. Exploring the heterogeneity by freelancers’ employment history, we do not find evidence that high-quality service, measured by their past performance and employment, moderates the adverse effects on employment. In fact, we find suggestive evidence that top freelancers are disproportionately affected by AI. These results suggest that generative AI may transform the role of human capital in the organization and reduce overall demand for workers. Supplemental Material: The online appendices are available at https://doi.org/10.1287/orsc.2023.18441 .

Greener pastures: why Indian international students leave the US labor market

ABSTRACT This paper revisits conventional wisdom on US degree premiums for immigrant workers and shows that, despite the benefits of a US degree, migration policies and substandard labor conditions contribute to international student motivations to return home. Using two original datasets, I find that about a third of Indian-born US graduates leave the US, seeking employment opportunities abroad and a respite from US work visa restrictions. I draw on 105 in-depth interviews and 7,177 employment histories constructed from LinkedIn, and the analysis demonstrates the use of digital data to shed new light on under-studied patterns of return migration in institutional perspective. I find that US work visas are related to the underemployment of immigrant workers, and gaps in visa availability are associated with US labor market departure for Indian international students. At the same time, foreign employers reward skills and credentials developed in the US, and the results suggest that US degrees carry a higher premium in foreign labor markets. The paper emphasizes the role of institutions in the skilled migration system and identifies disjunctures in US migration policy. I identify opportunities for policy reform to improve immigrant labor conditions and increase the retention of US-educated migrants in the US labor market.

On the up and up: the job mobility of skilled return migrants

Abstract This paper examines the labor market incorporation of skilled Indian migrants returning home after working in the United States. I analyze a novel dataset of 7,177 time-varying transnational employment histories from LinkedIn using logistic regression and survival analysis. I find that skilled Indian migrants experience occupational upgrades when re-entering their home labor market, buoyed by foreign human capital and compensating for US legal status restrictions. While immigrants often face initial occupational downgrades and job mobility constraints in foreign labor markets, return migrants experience stability, promotions and accelerated rates of job mobility at home. The paper contributes to the literature on return migration, a significant but understudied dimension of international migration due to data constraints. I offer LinkedIn employment histories as digital data source to address these limitations and leverage their rich spatio-temporal information. These data help expand prior research on return migrant wages to broader questions about transnational migrant career trajectories. The findings shed light into where high-demand migrants might maximize their career growth, and have implications for understanding the policies shaping the recruitment and retention of skilled workers in the global labor market.

The absence of formal work experience may affect the rate of cognitive decline in older adult women: findings from the health and retirement study.

This study investigated the relationship between years of employment and cognitive health among older non-Latinx Black, Latinx, and non-Latinx White women. We hypothesized that women who had never been formally employed (i.e., zero years of formal work experience) would exhibit a pronounced cognitive decline. Our study included 5,664 older adult women from the Health and Retirement Study (2010-2016) aged 65-101 (M = 75.41). Out of 5,664 participants, 850 identified as non-Latinx Black, 475 identified as Latinx, and 4,339 identified as non-Latinx White. Furthermore, 5,292 women indicated having a professional employment history of at least one year, whereas 372 women reported no formal work experience. The Telephone Interview for Cognitive Status-27 (TICS-27) was used to assess cognitive performance. Linear mixed effects models were conducted to assess whether employment history was associated with the rate of cognitive decline. In all three racial and ethnic groups, lower age, higher education, greater number of years worked, fewer chronic conditions, and greater household income were associated with better cognitive performance at baseline (p < .05). Additionally, women who had not worked in any formal capacity had a lower baseline cognitive performance (p < .001) and a more extreme decline in cognitive performance over time (p = .04). In conclusion, we found that women without any formal work experience performed lower at baseline and experienced a steeper cognitive decline over time. These findings underscore the need to further explore the complex interrelationships between employment duration and cognitive trajectories, especially among older women and those from different racial and ethnic backgrounds.

Empirical Monte Carlo evidence on estimation of timing-of-events models

This article builds on the Empirical Monte Carlo simulation approach to study the estimation of Timing-of-Events (ToE) models. We exploit rich Swedish data of unemployed job seekers with information on participation in a training program to simulate placebo treatment durations. We first use these simulations to examine which covariates are key confounders to be included in dynamic selection models for training participation. The joint inclusion of specific short-term employment history indicators (notably, the share of time spent in employment), together with baseline socio-economic characteristics, regional and inflow timing information, is important to deal with selection bias. Next, we omit subsets of explanatory variables and estimate ToE models with discrete distributions for the ensuing systematic unobserved heterogeneity. In many cases, the ToE approach provides accurate effect estimates, especially if time-varying variation in the unemployment rate of the local labor market is taken into account. However, assuming too many or too few support points for unobserved heterogeneity may lead to large biases. Information criteria, in particular those penalizing parameter abundance, are useful to select the number of support points. A comparison with other duration models shows that a Stratified Cox model performs well with abundant multiple spells but less well when multiple spells are uncommon. The standard Cox regression model performs poorly in all configurations as it is unable to account for unobserved heterogeneity.

Lifetime employment trajectories and cancer

Working life is associated with lifestyle, screening uptake, and occupational health risks that may explain differences in cancer onset. To better understand the association between working life and cancer risk, we need to account for the entire employment history. We investigated whether lifetime employment trajectories are associated with cancer risk. We used data from 6809 women and 5716 men, average age 70 years, from the Survey of Health, Ageing, and Retirement in Europe. Employment history from age 16 to 65 was collected retrospectively using a life calendar and trajectories were constructed using sequence analysis. Associations between employment trajectories and self-reported cancer were assessed using logistic regression. We identified eight employment trajectories for women and two for men. Among women, the risk of cancer was higher in the trajectories “Mainly full-time to home/family”, “Full-time or home/family to part-time”, “Mainly full-time”, and “Other” compared with the “Mainly home/family” trajectory. Among men, the risk of cancer was lower in the “Mainly self-employment” trajectory compared with “Mainly full-time”. We could show how employment trajectories were associated with cancer risk, underlining the potential of sequence analysis for life course epidemiology. More research is needed to understand these associations and determine if causal relationships exist.

Brexit and the EU Settlement Scheme: Continuity and rupture with the European Union

ABSTRACT The EU Settlement Scheme, a product of Brexit, both continues and breaks with the EU legacy, offering dismantlement for the vulnerable and continuity for the rest. In its attempt to keep the rights of EEA and Swiss citizens as close to their EU format as possible, it borrows from Directive 2004/38 and, in doing so, manages to enhance the conditionality ingrained in the Directive, discriminating against the most vulnerable. At the same time, by breaking with the EU legacy, it also moves the most vulnerable even farther away from the ideal of an unconditional right to return. Through an exploratory opt-in online survey, I observe the Scheme’s intrinsic tension of offering both continuity and dismantlement and the emergence of some vulnerability traits. Non-linear employment histories, older age, and lower levels of educational attainment enhance the difficulty of the process and create the prospects for disentitlement.

Must we lose our biological connection to nature to endure changing times?

AbstractEarth has &gt;8 billion people. Scholarly publications number nearly 7 million annually with &gt;1 million in the life and biomedical sciences, and ≥52 professional journals specializing in conservation, ecology, or related disciplines. The challenges of applying ecological data to conservation and wildlife management can easily become overwhelming. Herein we offer reflective perspectives about the changing face of applied knowledge and engagement from our personal employment histories as ecologists working in agency, university, and non‐governmental organization (NGO) biologist positions. We suggest natural history will always be nature's glue, but knowledge steeped mostly in muddy boots and field biology are no longer the soup du jour of our profession. In many ways, new technologies have changed data collection and the scientific questions asked. Arguably, such change is not welcomed by all, but a change in overlap across decades is needed to sustain and improve upon how the planet's biological diversity can coexist with increasingly difficult human conditions. Given that 80% of the people in the United States live in urban areas, with similar numbers internationally, a future possibility may be an even greater divide between wild nature, ecological services, and enjoyment in the field. This is disturbing. Despite fundamental scientific insights that help understand critical components of the natural world, once society loses touch with nature, what will remain?

Racial-Ethnic Poverty Gaps in Later Life: A Role for Late Career Employment Quality?

Abstract Racial-ethnic disparities in poverty are an important form of inequality in older age. Recent scholarship on racial-ethnic poverty gaps demonstrates that, beyond individual characteristics and behaviors, racialized structural factors like employment contribute to such gaps. Yet surprisingly little is known about the role of employment quality, despite observed racial-ethnic disparities in employment quality and the role of employment history in shaping later life well-being. Using data from the 2002–2018 waves of the Health and Retirement Study (HRS) and three poverty measures, we decompose the proportions of the Black-white and Hispanic-white poverty gaps among households led by 65-year-olds that are attributable to disparities in late career employment quality. We find that racial-ethnic disparities in late career employment quality account for 17–28 percent of the observed Black-white and 18–32 percent of the observed Hispanic-white poverty gaps, thus explaining a greater proportion of such gaps than many common individual or behavioral explanations. Disaggregating employment quality into its component measures, we find racial-ethnic disparities in access to employer-provided health insurance and hourly wages account for the largest proportion of racial-ethnic poverty gaps. Our findings suggest that employment quality captures important racialized dimensions of labor market context that help account for racial-ethnic inequalities in later life poverty in the United States.

Motherhood in academia: A novel dataset of UK academic women with an application to maternity leave uptake

AbstractMotherhood is widely believed to be an important factor slowing down the career progression of women. We present a novel database that combines an original survey of women and mothers working in the UK Higher Education sector with data on the occupational maternity benefits offered by academic employers. This allows users to track, at the individual level, child‐bearing experiences and employment histories simultaneously. We describe several aspects of mothers' experiences in academia and how those evolve over the years. We also conduct an empirical analysis of the determinants of maternity leave uptake, showing that women's employment status and family arrangements significantly impact the types of maternity leave – paid or unpaid – taken.

Recommending Career Transitions to Job Seekers Using Earnings Estimates, Skills Similarity, and Occupational Demand

This paper describes a career recommendation algorithm that uses government administrative data to help job seekers discover new careers that similar job seekers have successfully switched to in the past. Algorithm development was motivated by workers and contractors who were displaced by the COVID-19 economic crisis and by workers in declining industries seeking new careers in growing ones. Traditional job boards available through state government websites list all available jobs but do little to remove uncertainty associated with moving to a new industry or occupation. Our recommendation algorithm can lower this uncertainty. It uses causal machine learning techniques and administrative data on the universe of individual-level employment histories and earnings to identify career transitions that have resulted in increased earnings and employment for previous job seekers. We combine these estimates with measures of skill similarity across occupations, derived from natural-language processing of millions of full-text job descriptions, and with occupational demand, as measured by nightly job posting volume. The algorithm parses applicant resumes and returns recommended careers that use similar skills, have available jobs, and are estimated to lead to higher earnings and employment. We have implemented our algorithm in production workforce development systems in five U.S. states.

The psychological impact of diagnosis of sinonasal, nasopharyngeal, and laryngeal cancers: a systematic review.

High risk of degraded quality of life and psychological distress is observed in patients diagnosed with sinonasal, nasopharyngeal, and laryngeal cancers, which could be caused by exposure to carcinogens in workplaces. This review aimed to investigate the psychological impact of diagnosis associated with the possible occupational etiology of such neoplasms and to explore the instruments that evaluate the quality of life (QoL), anxiety, and depression in these patients. Studies were considered for the review only if they described aspects of the psychological impact of the diagnosis of sinonasal, nasopharyngeal, and laryngeal cancers and reported results distinguished by the tumor site. The psychological impact was assessed in terms of health-related QoL, anxiety, and depression using reliable psychometric questionnaires administered at the time of diagnosis and 1 year later. In more than 5,900 records identified, 442 studies fulfilled the eligibility criteria and 436 were excluded after full-text screening, resulting in six studies to be finally included in the review. The EORTC Core Quality of Life questionnaire (EORTC QLQ-C30), EORTC QLQ-H&N35, and Functional Assessment of Cancer Therapy (FACT) were used to evaluate the QoL, whereas the Hospital Anxiety and Depression Scale (HADS) and Center for Epidemiologic Studies Depression Scale (CES-D) questionnaires were employed to assess anxiety and depression. QoL scores were similar to those of the general population at the time of diagnosis and remained unchanged or slightly improved at 1 year since diagnosis. In contrast, a higher prevalence of anxiety and depression was observed compared with the general population, although the results were inconsistent across the very few studies identified. No study investigating the association between the potential occupational etiology and QoL or distress was found. Exploring the existing scientific literature on emotional distress in sinonasal, nasopharyngeal, and laryngeal cancer patients was prompted by concerns over the disfiguring nature of treatment and the additional emotional burden resulting from their occupational etiology. Unfortunately, neither a crucial element nor other risk factors (lifestyle, smoking, drinking, etc.) were examined in any study. Patients' employment history should be considered in order to assess the possible impact of believing they are affected by an occupational exposure disease in the evaluation of their psychological distress. This history would support evidence-based recommendations about dedicated psychological interventions.

Exploration of Skillification and Its Use in Awarding Credit for Prior Learning

In applications for prior learning credit, learners with professional experience describe competencies gained during their employment history or past study, explaining how these competencies correspond to what is taught in a course sufficiently well to award them credit for prior learning. Comparing skills that are extracted algorithmically from artifacts describing a learner’s prior experience to skills similarly extracted from syllabi offers a potential solution. Current research indicates that algorithmically extracted skills can be used to identify relevance between artifacts and groups of courses but that connecting an artifact to a specific course requires guidance to learners on the artifact creation, careful use of analytical techniques, and faculty involvement. The guidance includes setting expectations for language volume and creating focused text, like an essay, rather than more general descriptive artifacts, like résumés. Effective analytical techniques accommodate observed variation in skill volume between different artifacts, optimizing the potential identification of appropriate relationships. Basing decisions on the degree of overlap in the total artifact-course skill set appears more effective than considering the number of matches to program skills. Deterministic matching is insufficient for identifying course-level correspondence, while approaches that capture information about the covariation of skills among courses offer promise.

Prediction of COVID-19 patients’ participation in financing informal care using machine learning methods: willingness to pay and willingness to accept approaches

BackgroundInformal care plays an essential role in managing the COVID-19 pandemic. Expanding health insurance packages that reimburse caregivers' services through cost-sharing policies could increase financial resources. Predicting payers' willingness to contribute financially accurately is essential for implementing such a policy. This study aimed to identify the key variables related to WTP/WTA of COVID-19 patients for informal care in Sanandaj city, Iran.MethodsThis cross-sectional study involved 425 COVID-19 patients in Sanandaj city, Iran, and 23 potential risk factors. We compared the performance of three classifiers based on total accuracy, specificity, sensitivity, negative likelihood ratio, and positive likelihood ratio.ResultsFindings showed that the average total accuracy of all models was over 70%. Random trees had the most incredible total accuracy for both patient WTA and patient WTP(0.95 and 0.92). Also, the most significant specificity (0.93 and 0.94), sensitivity (0.91 and 0.87), and the lowest negative likelihood ratio (0.193 and 0.19) belonged to this model. According to the random tree model, the most critical factor in patient WTA were patient difficulty in personal activities, dependency on the caregiver, number of caregivers, patient employment, and education, caregiver employment and patient hospitalization history. Also, for WTP were history of COVID-19 death of patient's relatives, and patient employment status.ConclusionImplementing of a more flexible work schedule, encouraging employer to support employee to provide informal care, implementing educational programs to increase patients' efficacy, and providing accurate information could lead to increased patients' willingness to contribute and finally promote health outcomes in the population.