“There Is Never a Break”: The Hidden Curriculum of Professionalization for Engineering Faculty

The purpose of this exploratory special issue study was to understand the hidden curriculum (HC), or the unwritten, unofficial, or unintended lessons, around the professionalization of engineering faculty across institutions of higher education. Additionally, how engineering faculty connected the role of HC awareness, emotions, self-efficacy, and self-advocacy concepts was studied. A mixed-method survey was disseminated to 55 engineering faculties across 54 institutions of higher education in the United States. Quantitative questions, which centered around the influences that gender, race, faculty rank, and institutional type played in participants’ responses was analyzed using a combination of decision tree analysis with chi-square and correlational analysis. Qualitative questions were analyzed by a combination of tone-, open-, and focused-coding. The findings pointed to the primary roles that gender and institutional type (e.g., Tier 1) played in issues of fulfilling the professional expectations of the field. Furthermore, it was found that HC awareness and emotions and HC awareness and self-efficacy had moderate positive correlations, whereas, compared to self-advocacy, it had weak, negative correlations. Together, the findings point to the complex understandings and intersectional lived realities of many engineering faculty and hopes that through its findings can create awareness of the challenges and obstacles present in these professional environments

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Spatiotemporal Mereotopology For Designer Systems

The design of new products and their development is a long process, which incepts with a very simple novel concept. Hence, the idea of product development needs to be nurtured properly to successfully develop the product. The use of the virtual platform to represent the conceptual design is popular in today’s world. Traditional product modeler systems are widely used to represent the conceptual products. However, there is a limitation of the traditional designer system of its inability to capture the dynamic nature of the products. The real-world’s product and processes are time dependent. Products have spatial (time-independent) parts and temporal (time-dependent) parts. The traditional designer/modeler systems are usually used to capture the time-independent design processes. This research work aims to bridge this gap in a knowledge base approach. The primary focus of this research work is to understand the behavior of products by developing mathematical formalisms. In that case, RCC8 (Region Connection Calculus with 8 relational entity) and mereotopology are utilized. The spatial behavior is analyzed first and then the temporal behavior is analyzed. After understanding the behavior of products, next aim is to develop the knowledge base to represent the behavior in a structured manner. Ontology is used for that purpose. Two ontology is developed; the OCAD (Ontological CAD) ontology to represent the spatial products nature, and STM (SpatioTemporal Mereotopological) ontology to represent the dynamic nature of the product. After developing the knowledge base a framework is developed to represent the dynamic product behavior in a visualization interface. To reach this goal, the macro data of the designer system is integrated with the .owl data of the ontology firstly. Afterward, the .owl ontology data, which contains the spatiotemporal knowledge is integrated with a visualization interface. X3DOM is used for this purpose since it is widely used in a web platform to visualize spatial and dynamic models. This way the dynamic design intents of the designer is captured. Different dynamic products are tested for various motion cases to check the validity of the framework. This framework is later enhanced by introducing a di-directional CAD to CAD integration. A real-world simplified engine piston is utilized to check the validation. Last, the developed STM knowledge base is also validated in the manufacturing domain for a Resistance Spot Welding’s (RSW) predicted nugget size visualization case

The Possibility and Peril of using multimodal physiological approaches to measure Academic Emotion, Race and Gender Bias, and Motivation.

In 2017, our research team presented a workshop at SCIPIE that introduced physiological research tools and techniques, affording researchers additional methods to study existing models of emotion. After two years of utilizing these tools, we aim to update some of the practical and theoretical issues we have encountered and overcome (Villanueva et al., In Press, Husman et al., 2019)

Effects of the COVID-19 pandemic on caregiver mental health and the child caregiving environment in a low-resource, rural context.

Early child development has been influenced directly and indirectly by the COVID-19 pandemic, and these effects are exacerbated in contexts of poverty. This study estimates effects of the pandemic and subsequent population lockdowns on mental health, caregiving practices, and freedom of movement among female caregivers of children 6-27 months (50% female), in rural Bangladesh. A cohort (N = 517) was assessed before and during the pandemic (May-June, 2019 and July-September, 2020). Caregivers who experienced more food insecurity and financial loss during the pandemic reported larger increases in depressive symptoms (0.26 SD, 95% CI 0.08-0.44; 0.21 SD, 0.04-0.40) compared to less affected caregivers. Stimulating caregiving and freedom of movement results were inconsistent. Increases in depressive symptoms during the pandemic may have consequences for child development

Success Factors for Community Health Workers in Implementing an Integrated Group-Based Child Development Intervention in Rural Bangladesh.

Community Health Workers (CHWs) can effectively implement maternal and child health interventions, but there is paucity of evidence on how to integrate child stimulation into these interventions, and their delivery at scale. In rural Bangladesh, CHWs implemented an intervention integrating psychosocial stimulation, nutrition, maternal mental health, water, sanitation, hygiene (WASH) and lead exposure prevention. In each of 16 intervention villages, one CHW worked with 20 households. CHWs bi-weekly held group meetings or alternated group meetings and home visits with pregnant women and lactating mothers. We assessed the intervention through five focus groups, four interviews and one group discussion with CHWs and their supervisors to explore success factors of implementation. CHWs' training, one-on-one supervision and introduction by staff to their own community, and adoption of tablet computers as job aids, enabled successful session delivery to convey behavioral recommendations. CHWs reported difficulties delivering session due to the complexity of behavioral recommendations and struggled with age-specific intervention material. Young children's attendance in group sessions generated distractions that undermined content delivery. We identified ways to minimize the difficulties to strengthen intervention-delivery during implementation, and scale-up. Iterative revisions of similarly integrated interventions based on qualitative evaluation findings could be delivered feasibly by CHWs and allow for implementation at scale

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundRegular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations.MethodsThe Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds.FindingsThe leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles.InterpretationLong-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere