Ordinary Least-squares Regression Models Research Articles

Spatiotemporal Differentiation of Carbon Emissions from Logistics Industry at Provincial Scale in China Under the Background of High-quality Economic Development

Since 2010, the Chinese economy has transitioned from a high-speed growth model to a high-quality development model. During this period, the logistics industry has witnessed rapid growth, leading to significant carbon emissions and posing severe threats to the ecological environment. To investigate the spatiotemporal variations in carbon emissions in China's logistics industry, we conducted a correlation analysis using Moran's I index and a bivariate spatial autocorrelation model from 2010 to 2021. Additionally, we employed a geographically and temporally weighted regression model （GTWR） to examine the spatial heterogeneity of factors influencing provincial-level logistics-related carbon emissions. The results indicated that over the study period, there was a shift from insignificant spatial relationships to significant positive spatial correlations among provincial-level logistics carbon emissions in China. Furthermore, varying degrees of spatial clustering were observed. The findings regarding factor heterogeneity revealed that freight turnover volume, per capita GDP of the logistics industry, and infrastructure level exhibited positive spatial correlations with logistics-related carbon emissions, whereas energy intensity showed negative spatial correlations with such emissions. Comparing the results from the geographically weighted regression （GWR） and ordinary least squares regression （OLS）, it was evident that the adjusted R-squared values for the OLS, GWR, and GTWR models were 0.541, 0.567, and 0.838, respectively. This suggests that our adopted GTWR model provided a superior fit and offered better explanations for spatiotemporal heterogeneity between various influencing factors and logistics-related carbon emissions. These research findings can serve as valuable references for formulating province-specific strategies to reduce carbon emissions within China's economy under its high-quality development context.

Minorities' Diminished Psychedelic Returns: Income and Educations Impact on Whites, Blacks, Hispanics, and Asians.

Growing evidence suggests that the race and ethnic minority population may experience fewer protective effects of psychedelics on mental health. The minority diminished psychedelic returns theory proposes that racism, manifested in socioeconomic inequality, could partially account for the smaller health gains observed. Therefore, it is important to investigate whether socioeconomic inequality reduces the impact of psychedelics on health outcomes for minority populations. Additionally, despite having higher socioeconomic status, it remains unclear whether psychedelic use among minorities is associated with the same level of health benefits as observed in non-Hispanic whites. This study utilizes data from the National Survey of Drug Use (N = 2008 to 2019), which involved 458,372 participants aged 18 and above. The objective is to examine the impact of various psychedelics (MDMA, psilocybin, DMT, ayahuasca, peyote/mescaline, and LSD), as well as lifetime classic psychedelics use (LCPU), on psychological distress in the past month, taking into account socioeconomic factors (education level and family income) and race/ethnic differences (White, Black, Hispanic, and Asian). The analysis employed a series of nested ordinary least-square regression models using Stata 18. The results indicate that, after controlling for socioeconomic status, there is no association between Black and Hispanic psychedelic use and distress. However, white psychedelic use remains associated with lower levels of distress. Additionally, despite having higher levels of education and income, psychedelic use among minority groups does not appear to be linked to reduced stress. In fact, for Asians with higher education and income, certain psychedelic use is associated with increased distress.

MONETARY POLICY DYNAMICS AND ECONOMIC PERFORMANCE IN BANGLADESH: AN ARIMA MODEL APPROACH

This study delves into the impact of monetary policy on the economic performance of Bangladesh, utilizing data derived from audited annual reports of the Bangladesh Bank covering the years 2014 to 2022. The central focus of the analysis revolves around Gross Domestic Product (GDP) as the dependent variable, with money supply growth, real interest rate, exchange rate, inflation rate, and repo rate considered as independent variables. Employing two regression models, namely Ordinary Least Squares (OLS) and Autoregressive Integrated Moving Average (ARIMA), the research aims to explore the relationships among these variables. In the OLS regression model, significant associations are identified between GDP and real interest rates, inflation rate, and the repo rate. However, no statistically significant relationships are observed between GDP and money supply growth or the exchange rate. On the contrary, the ARIMA regression model indicates statistical significance for all variables, except the exchange rate, concerning their impact on GDP. These findings suggest that monetary policy plays a substantial role in shaping the economic growth of Bangladesh, with real interest rates, inflation rates, and repo rates exerting particular influence. The lack of a significant relationship between the exchange rate and GDP in the ARIMA model implies that other factors, potentially external to the country, may have a more dominant influence on exchange rate dynamics during the study period. This research contributes to a deeper understanding of the complex interplay between monetary policy and economic performance in Bangladesh, offering valuable insights for policymakers and stakeholders.

TOWARD SPATIAL AND SOCIAL EQUALITY: INVESTIGATING AND MODELING THE LINK BETWEEN OPPORTUNITY AND URBAN INTENSITY IN METRO MANILA, PHILIPPINES

Abstract. This paper employed an interdisciplinary mixed-method approach combining geomatics, geography, and sociology to investigate the link between opportunity and urban intensity across the 32 legislative districts of Metro Manila. Eighteen opportunity indicators, weighted within their opportunity dimensions using Analytic Hierarchy Process (AHP) were used to output Opportunity Scores, while annual VIIRS DNB nighttime light was used as proxy data to approximate the region’s Urban Intensity Score. Two regression models were developed with different sets of explanatory variables: an AHP-based model that used the top two weighted indicators from each opportunity dimension, and a model that used Exploratory Regression Analysis (ERA) to determine the appropriate variable combination. Modeling via Ordinary Least Squares Regression (OLS) produced a properly specified global model, while OLS modeling of the ERA-based model exhibited non-stationarity between variables which necessitated performing Geographically Weighted Regression (GWR). Our quantitative findings, combined with insights from our mini focus group discussion (FGD), provided evidence of the unequal distribution of opportunities across Metro Manila and the spatially varying relationship between urban intensity and opportunity, which have significant implications for urban framework as well as housing choice.

The impact of social-emotional skills on mental well-being among Chinese underprivileged-background students: Empirical evidence from SSES 2019

The present study investigated the impact of social-emotional skills on the mental well-being of Chinese underprivileged-background students (N = 1,811) based on the 2019 Survey on Social and Emotional Skills (SSES 2019) data, separately using the ordinary least squares (OLS) regression and the quantile regression (QR) methods. The OLS regression model indicated that the mental well-being of Chinese underprivileged-background students is significantly and positively affected by self-control, optimism, stress resilience, trust, curiosity, energy, and sociability, but significantly and negatively influenced by tolerance. Whereas, persistence, responsibility, emotional control, cooperation, empathy, creativity, and assertiveness had no significant impact on the students’ mental well-being. Further, the QR model revealed that the impact of social-emotional skills on the mental well-being of Chinese underprivileged-background students can be significant when the mental well-being is at a relatively moderate level, whereas being insignificantly related to the lowest and the highest level of the mental well-being. The results were discussed generally based on the characters of underprivileged-background students in Chinese educational context. Overall, the findings may provide several practical implications for policy application in social-emotional education and the improvement of mental well-being for Chinese underprivileged-background students.

The Effect of Corporate Governance on the Degree of Agency Cost in the Korean Market

This study examines the relationship between corporate governance (CG) and agency costs using Korean market data, particularly for chaebol firms. The final sample includes 660 firm-year observations between 2016 and 2020 for Korean non-financial firms listed on the Korean Composite Stock Price Index (KOSPI). This study employs an ordinary least-squares panel data regression model using two proxies for agency costs, namely, asset utilization ratio and operating expense ratio, and six CG individual metrics as independent variables (CG score, protection of shareholder rights, board structure, disclosure, audit organization, and managerial discretion and error management). We find that firms with high CG experience lower agency costs than those with low CG. Moreover, our evidence suggests that firms can decrease agency costs by improving the quality of CG. The results of our regression model also support the idea that CG is effective in reducing agency costs for chaebol firms but not for non-chaebol firms. Finally, our findings suggest that the implementation of effective CG mechanisms in firms might improve managerial behavior through better decision-making to maximize the value of firms.

Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia

Accurately simulating glacier mass balance (GMB) data is crucial for assessing the impacts of climate change on glacier dynamics. Since physical models often face challenges in comprehensively accounting for factors influencing glacial melt and uncertainties in inputs, machine learning (ML) offers a viable alternative due to its robust flexibility and nonlinear fitting capability. However, the effectiveness of ML in modeling GMB data across diverse glacier types within High Mountain Asia has not yet been thoroughly explored. This study addresses this research gap by evaluating ML models used for the simulation of annual glacier-wide GMB data, with a specific focus on comparing maritime glaciers in the Niyang River basin and continental glaciers in the Manas River basin. For this purpose, meteorological predictive factors derived from monthly ERA5-Land datasets, and topographical predictive factors obtained from the Randolph Glacier Inventory, along with target GMB data rooted in geodetic mass balance observations, were employed to drive four selective ML models: the random forest model, the gradient boosting decision tree (GBDT) model, the deep neural network model, and the ordinary least-square linear regression model. The results highlighted that ML models generally exhibit superior performance in the simulation of GMB data for continental glaciers compared to maritime ones. Moreover, among the four ML models, the GBDT model was found to consistently exhibit superior performance with coefficient of determination (R2) values of 0.72 and 0.67 and root mean squared error (RMSE) values of 0.21 m w.e. and 0.30 m w.e. for glaciers within Manas and Niyang river basins, respectively. Furthermore, this study reveals that topographical and climatic factors differentially influence GMB simulations in maritime and continental glaciers, providing key insights into glacier dynamics in response to climate change. In summary, ML, particularly the GBDT model, demonstrates significant potential in GMB simulation. Moreover, the application of ML can enhance the accuracy of GMB modeling, providing a promising approach to assess the impacts of climate change on glacier dynamics.

Spatial characterization of forest ecosystem services and human-induced complexities in Himalayan biodiversity hotspot area.

The forest ecosystem of Indian Himalayan Region offers various ecosystem services (ESs) that are crucial for the sustenance of human beings. However, the rapid expansion of human activities (HA) poses a significant threat to the provision of the forest ecosystem services (FES). For simple and definitive assessments of FES and HA, the use of indicators has become an indispensable approach. In the present study, we performed: (i) indicator-based mapping of FES and HA, and (ii) evaluated the impact of HA on FES with the aid of geospatial techniques. Village-level analysis was conducted for FES and HA in the Aglar watershed of Uttarakhand, India for 2015. Four dominant forest types in the watershed-Quercus mixed, Pinus roxburghii, Cedrus deodara, and mixed forest were considered. For spatial characterization of FES, indicators such as forest carbon stock, net primary productivity, total water retention, and sediment yield were assessed, whereas human activity index (HAI) was evaluated using indicators of HAs, namely population density, road density, farmland, and habitation ratio. The integration of normalized values of FES indicators generated multiple ecosystem services indicator (MESI), and HAI was contructed using analytical hierarchical process based assignment of weights to HA indicators. Spatial analysis techniques such as ordinary least-square regression (OLS) and geographically weighted regression (GWR) models were used to derive the spatial relationship between them. The adjusted R2 and AIC were utilized to evaluate the effectiveness of the model. The GWR model had a better fit with an adjusted R2 of 0.68 and a lower AIC of 42.940, compared to the OLS model with an adjusted R2 of 0.21 and an AIC of 60.52. The statistics showed that GWR performed better than OLS and ably captured the heteroscedasticity of the phenomena. An inverse relation was observed between MESI and HAI. The findings of the study highlight the close link between the supply of FES and the impact of human-induced disturbances over the provision of FES, which has the potential to increase over time. The study provides a scientific basis for structuring policy dialogues to coordinate the long-term regional sustainability of FES provisioned from the Himalayan regions.

Dorsal subthalamic deep brain stimulation improves pain in Parkinson's disease.

Inconsistent effects of subthalamic deep brain stimulation (STN DBS) on pain, a common non-motor symptom of Parkinson's disease (PD), may be due to variations in active contact location relative to some pain-reducing locus of stimulation. This study models and compares the loci of maximal effect for pain reduction and motor improvement in STN DBS. We measured Movement Disorder Society Unified PD Rating Scale (MDS-UPDRS) Part I pain score (item-9), and MDS-UPDRS Part III motor score, preoperatively and 6-12 months after STN DBS. An ordinary least-squares regression model was used to examine active contact location as a predictor of follow-up pain score while controlling for baseline pain, age, dopaminergic medication, and motor improvement. An atlas-independent isotropic electric field model was applied to distinguish sites of maximally effective stimulation for pain and motor improvement. In 74 PD patients, mean pain score significantly improved after STN DBS (p = 0.01). In a regression model, more dorsal active contact location was the only significant predictor of pain improvement (R2 = 0.17, p = 0.03). The stimulation locus for maximal pain improvement was lateral, anterior, and dorsal to that for maximal motor improvement. STN stimulation, dorsal to the site of optimal motor improvement, improves pain. This region contains the zona incerta, which is known to modulate pain in humans, and may explain this observation.

Disparities in extreme contexts: The impact of gender and mental health status on the criminal justice outcomes of extremists

PurposeWhile disparities in the treatment of individuals in the criminal justice system have been well-documented, due to varying political and legal contexts it is likely that disparities may differ for perpetrators of extremism. This research examines the effects of gender and mental health status on criminal justice outcomes of individuals who have been accused of committing ideologically motivated crimes in the United States. MethodsUsing the Extremist Crime Database (ECDB) and logistic and ordinary least-squares (OLS) regression models, this study estimates the effects of these individual characteristics on adjudication method and sentence length, and isolates pre/post-2001 time-periods. ResultsThe findings suggest that women are more likely to plead guilty and receive shorter sentences, but time-period and the status of ‘terrorist’ is an important factor for the influence of gender. Additionally, mental health can lead to contrasting outcomes, depending on the circumstances. ConclusionsThese findings highlight the divergent treatment of extremist perpetrators, specifically noting that gender and mental health can be influential factors after controlling for ideology and other associated factors. Importantly, the findings suggest that the role of gender and mental health on legal outcomes maybe vary based on contextual factors that are shaped by policies specific to this crime type.

Emergency Department and Health Care System Factors Associated with Telehealth Innovation During the COVID-19 Pandemic.

Objective: Telehealth capacity may be an important component of pandemic response infrastructure. We aimed to examine changes in the telehealth use by the US emergency departments (EDs) during COVID-19, and to determine whether existing telehealth infrastructure or increased system integration were associated with increased likelihood of use. Methods: We analyzed 2016-2020 National ED Inventory (NEDI)-USA data, including ED characteristics and nature of telehealth use for all US EDs. American Hospital Association data characterized EDs' system integration. An ordinary least-squares regression model obtained one-step-ahead forecast of the expected proportion of EDs using telehealth in 2020 based on growth observed from 2016 to 2019. Among EDs without telehealth in 2019, we used logistic regression models to examine whether system membership or existing telehealth infrastructure were associated with odds of innovation in telehealth use in 2020, accounting for ED characteristics. Results: Of 4,038 EDs responding to telehealth questions in 2019 and 2020 (73% response rate), 3,015 used telehealth in 2020. Telehealth use by US EDs increased more than expected in 2020 (2016: 58%, 2017: 61%, 2018: 65%, 2019: 67%, 2020: 74%, greater than predicted 71%, p = 0.004). Existing telehealth infrastructure was associated with increased telehealth innovation (OR = 1.88, 95% CI: 1.49-2.36), whereas hospital system membership was not (odds ratio [OR] = 1.00, 95% confidence interval [CI]: 0.80-1.25). Conclusions: Telehealth use by US EDs in 2020 grew more than expected and preexisting telehealth infrastructure was associated with increased innovation in its use. Preparation for future pandemic responses may benefit from considering strategies to invest in local infrastructure to facilitate technology adoption and innovation.

Impacts of Pit latrines on Groundwater Quality in Squatter Settlements in Zanzibar

Groundwater is the major source of drinking water in Zanzibar. Most of the people in squatter settlement rely on shallow groundwater due to insufficient supply of treated water. In these areas, shallow wells are located close to pit latrines, the condition which increasing the risk of contamination. This study aimed to assess the impact of pit latrines on groundwater quality in five sub-urban areas of squatter settlements in Unguja Island. Water samples were collected from twenty shallow wells and analyzed for the microbiological and physio-chemical parameters. The Ordinary Least Square (OLS) regression model was used to assess the influence of distance from pit latrine to the groundwater sources. The data revealed that nitrate and chloride concentrations increases when the distance between the shallow wells and the nearest pit latrine decreases. In contrary, faecal coliform counts shows that for every unit increase in the distance from pit latrine to the shallow wells the counts decreased by 42 CFU/100 ml. However, OLS regression model did not show significant relationship of total coliform and distance from the well. This study suggests that there are other factors that may accelerate the total coliform bacteria in water wells. These results illustrate that public health risks associated with waterborne diseases could be ranked high at densely populated squatter settlement. Thus, the study recommends that routine monitoring of shallow wells is required as well as application of low-cost technologies such as raised and lined pit latrines in order to minimize potential risk of groundwater pollution.

Veteran perspectives of epilepsy care: Impact of Veteran satisfaction, knowledge, and proactivity

ObjectiveVeterans are at elevated risk of epilepsy due to higher rates of traumatic brain injury (TBI). However, little work has examined the extent to which quality of care is associated with key outcomes for Veterans with epilepsy (VWE). This study aimed to examine the impact of quality of care on three outcomes: patients’ knowledge of epilepsy self-care, proactive epilepsy self-management, and satisfaction with care. MethodWe conducted a cross-sectional study of Post-9/11 Veterans with validated active epilepsy who received VA care (n = 441). Veterans were surveyed on care processes using American Academy of Neurology epilepsy quality measures, and a patient-generated measure related to the use of emergency care. Outcome measures included epilepsy self-care knowledge, proactive epilepsy self-management, and satisfaction with epilepsy care. Covariates included sociodemographic and health status variables and a measure of patient-provider communication. An ordinary least-squares (OLS) regression model was used to determine if the quality of care was associated with the outcomes adjusting for multiple comparisons. ResultsSelf-reported measures of quality of care were broadly associated with satisfaction with care and epilepsy knowledge. OLS modeling indicated that healthcare provider guidance on when to seek emergency care was significantly associated with higher Veteran satisfaction with care (p < 0.01). Veterans who were asked about seizure frequency at every visit by their provider also reported higher satisfaction with care (p < 0.01) and increased epilepsy knowledge (p < 0.01). Veteran-provider communication was positively associated with epilepsy knowledge and proactive epilepsy self-management. Veterans with epilepsy with drug resistance epilepsy were significantly less satisfied with their care and reported lower proactivity compared to epilepsy controlled with medications. Further analysis indicated Black VWEs reported lower scores on epilepsy self-care knowledge compared to Whites (p < 0.001). ConclusionsThis study found that quality measures were associated with satisfaction and epilepsy knowledge but not associated with proactive self-management in multivariable models. The finding that better communication between providers and Veterans suggests that in addition to technical quality, interpersonal quality is important for patient outcomes. The secondary analysis identified racial disparities in epilepsy knowledge. This work offers opportunities to improve the quality of epilepsy care through the practice of patient-centered care models that reflect Veteran priorities and perceptions.

Examination of Elements Influencing Mothers’ Dynamic Capacity and Versatility: A Household-level Analysis

The study aims at exploring the degree of dynamic capacities and versatility of mothers at the household level and to recognise its general determinants. The sample includes 280 households where mothers with school-going children are the respondents. Principal component analysis is applied to estimate a mother’s dynamic capacities and versatility level. The ordinary least-square regression model is developed to identify the factors that influence the mother’s dynamic capacities and versatility level. The outcome shows that the dynamic capacities and versatility of mothers shift fundamentally across families and in general, it is tolerably high. The further result shows that the higher the level of education and the higher the age at marriage the higher the mother’s capacity to taking decisions and move autonomously. Different elements that add to this are a place of residence, religion and housing conditions.

Organization capital and firm risks

PurposeThis paper aims to investigate how organization capital influences different forms of corporate risk. It also explores how the relationship between organization capital and risks varies in the cross-section of firms.Design/methodology/approachTo test the hypothesis, this study employs the ordinary least squares (OLS) regression model using a large sample of the United States (US) data over the 1981–2019 period. It also uses an instrumental variable approach and an errors-in-variables panel regression approach to mitigate endogeneity problems.FindingsThe empirical results show that organization capital is positively related to both idiosyncratic risk and total risk but negatively related to systematic risk. The cross-sectional analysis shows that the positive relationship between organization capital and idiosyncratic risk is significantly more pronounced for the subsample of firms with high information asymmetry and human capital. Moreover, the negative relationship between organization capital and systematic risk is significantly more pronounced for firms with greater efficiency and firms facing higher industry- and economy-wide risks.Practical implicationsThe findings have important implications for investors and policymakers. For example, since organization capital increases idiosyncratic risk and total risk but reduces systematic risk, investors should take organization capital into account in portfolio formation and risk management. Moreover, the findings lend support to the argument on the recognition of intangible assets in financial statements. In particular, the study suggests that standard-setting bodies should consider corporate reporting frameworks to incorporate the disclosure of intangible assets into financial statements, particularly given the recent surge of corporate intangible assets and their critical impact on corporate risks.Originality/valueTo the best of the authors' knowledge, this is the first study to adopt a large sample to provide systematic evidence on the relationship between organization capital and a wide range of risks at the firm level. The authors show that the effect of organization capital on firm risks differs remarkably depending on the kind of firm risk a particular risk measure captures. This study thus makes an original contribution to resolving competing views on the effect of organization capital on firm risks.

Social Class and Private-Sphere Green Behavior in China: The Mediating Effects of Perceived Status and Environmental Concern

Green behavior is traditionally considered as an effective way to ameliorate environmental degradation and requires an individual to make sacrifices of their social resources. However, few studies have focused on its status signaling. In this study, we draw on the theory of social class and the concept of status signaling theory to empirically investigate the effect of objective social class and perceived social status on private-sphere green behavior in China. Using national comprehensive survey data from China General Survey Data (CGSS) in 2021 subjected to ordinary least-square regression model and step regression models, we present the following results: (1) higher-class individuals, both objectively and subjectively, tend to engage in more private-sphere green behavior than their lower-class counterparts; (2) the effect of objective social class on private-sphere green behavior is mediated by individual's perceived social status in the class hierarchy; (3) environmental concern significantly correlates with private-sphere green behavior, and it also mediates the effect between objective social class and private-sphere green behavior. The present research provides insights into how social class and its psychological manifestations (i.e., perceptions of status) correlate with private-green behavior in China. Our results suggest that more social context factors should be considered when identifying the factors promoting pro-environmental behavior in China.

Measurement Method and Influencing Mechanism of Urban Subdistrict Vitality in Shanghai Based on Multisource Data

Most previous studies on urban vitality focused on the analysis and evaluation of the overall vitality of urban agglomerations or single cities, while there are few related studies at the micro scale, such as subdistricts and even blocks. Based on multisource data and using the kernel density analysis and entropy methods, the economic vitality, social vitality, cultural vitality, ecological vitality and comprehensive vitality of each subdistrict in Shanghai were measured. Additionally, correlation analysis, the ordinary least squares (OLS) regression model, the spatial lag model (SLM) and the spatial error model (SEM) were used for fitting analysis to reveal the influencing mechanism of urban subdistrict vitality. The results showed that (1) the spatial distribution of economic vitality and social vitality in Shanghai showed the spatial characteristics of radiating outward from the center, and the types of social activity location corresponding to different levels of hotspot areas are different. Cultural vitality showed the spatial distribution characteristics of “gathering in the centre and dispersing around, with Puxi higher than Pudong”, but the cultural vitality values of different subdistricts vary greatly. Ecological vitality showed an increasing trend from the center to the surrounding areas. (2) The overall urban vitality of Shanghai also showed a decreasing circular structure from the center to the periphery. (3) Among the three regression models, i.e., the OLS regression model, SLM and SEM, the model with the best explanation of urban vitality is the SLM, which had an R2 of 0.6984, indicating that it can explain 69.84% of the spatial distribution pattern of urban vitality. (4) The factors that have significant effects on urban vitality are functional mix, metro station accessibility, metro station density, bus station density and intersection density, and all of them are positively correlated. The order of the strength of the effects is bus station density &gt; metro station density &gt; intersection density &gt; metro station accessibility &gt; functional mix.

College-Aged Males Experience Attenuated Sweet and Salty Taste with Modest Weight Gain

BackgroundHuman and animal studies report a blunted sense of taste in people who are overweight or obese, with heightened sensitivity also reported after weight loss. However, it is unknown if taste changes concurrently with weight gain. ObjectiveThis study investigated the association of weight gain with changes in suprathreshold taste intensity perception in a free-living population of young adults. MethodsTaste response, anthropometric measures, and diet changes were assessed with a longitudinal study design in first-year college students 3 times throughout the academic year. At baseline, 93 participants (30 males, 63 females) were an average of 18 y old, with a body mass index (in kg/m2) of 21.9. Sweet, umami, salty, sour, and bitter taste intensities were evaluated at 3 concentrations by using the general Labeled Magnitude Scale. Ordinary least-squares regression models assessed the association of weight gain and within-person taste change, adjusting for sex, race, and diet changes. ResultsParticipants gained an average of 3.9% in weight, ranging from −5.7% to +13.8%. With each 1% increase in body weight, males perceived sweet and salty as less intense, with taste responses decreasing by 11.0% (95% CI: −18.9%, −2.3%; P = 0.015) and 7.5% (95% CI: −13.1%, −1.5%; P = 0.015) from baseline, respectively. Meanwhile, females did not experience this decrement, and even perceived a 6.5% increase (95% CI: 2.6%, 10.5%; P = 0.007) in sour taste with similar amounts of weight gain. Changes in the consumption of meat and other umami-rich foods also negatively correlated with umami taste response (−39.1%; 95% CI: −56.3%, −15.0%; P = 0.004). ConclusionA modest weight gain is associated with concurrent taste changes in the first year of college, especially in males who experience a decrement in sweet and salty taste. This suggests that young-adult males may be susceptible to taste loss when gaining weight.

Parent-Child Contact during Incarceration: Predictors of Involvement among Resident and Nonresident Parents Following Release from Prison

Despite promising evidence that family contact during incarceration may reduce recidivism, less is understood about its role in postrelease parental involvement with children. Drawing on data from the randomized controlled trial of the Parent Child Study, this article examines in-prison parent-child contact in relation to postrelease parent-caregiver relationships, residence, and nonresidential contact. Predictors of contact include measures of visits, calls, and letters. We use ordinary least-squares regression, logistic regression, and multivariate negative binomial models to assess the outcomes. We show that in-prison visits and calls are associated with caregiver relationship quality and that more frequent visits increase the odds of both postrelease parent-child residence and nonresidential visits. Parent-child visits in prison predict parent involvement after release and may be vital to children and families over and above contact in general. Findings have implications for social workers, criminal justice practitioners, policy makers, and nonprofit agency staff working with incarcerated parents.

Spatial and Socioeconomic Inequalities in Accessibility to Healthcare Services in South Korea.

This study explored questions of (1) whether certain areas of South Korea experienced inequal accessibility to public health centers, private hospitals/clinics, and general hospitals by car and public transportation using gaussian mixture models (GMM) and (2) whether socially disadvantaged socioeconomic groups faced disproportionate burdens on accessibility to the multi-tier healthcare services employing ordinary least square regression models (OLS). This study used nationwide accessibility indicators in South Korea measured by Korea Transport Institute in 2019. The main findings were as follows: First, the results of the GMM indicate that the degree of accessibility to healthcare services was significantly lower in rural, mountainous, and seaside locations compared to metropolitan areas. Second, there was more considerable inequality in public transportation accessibility than car accessibility. Third, the findings of the OLS reveal a significant relationship between accessibility indicators and socioeconomic variables, such as age, gender, disability, and residential location, which indicates socioeconomic inequality in accessibility in South Korea. This study contributes to shedding light on understanding the spatial and socioeconomic inequality in accessibility across the nation and offering policy implications.