Discrimination Rate Research Articles

Response of Muslims in Indonesia, particularly the Council of Indonesian Ulama (Majelis Ulama Indonesia) as well as Nahdlatul Ulama and Muhammadiyah, to the social discrimination against Muslim communities in France

A report by a global human rights organization, found that violations against Muslim groups in France were widespread and all the acts committed were based on the name of state security. The Universal Declaration of Human Rights was prepared by representatives of countries with different legal and cultural backgrounds from all regions of the world which was proclaimed by the United Nations General Assembly on December 10, 1948 in Paris, France. This declaration then signifies that fundamental human rights must be universally protected. In this research, researchers aim to find out what social discrimination has occurred against Muslim groups in France, and researchers also want to know what are the reactions of Muslims in Indonesia in general and their responses, especially Council of Indonesian Ulama (MUI) and Nahdhatul Ulama and Muhammadiyah as representatives of Muslims in Indonesia against social discrimination against Muslim groups in France by using a qualitative approach method whose data collection techniques are through internet-based research, literature studies, and interviews. In their research results, researchers found that Indonesian society in general, especially MUI, NU, and Muhammadiyah continue to guard against Islamophobia in France and France as the country where the rules regarding international human rights were declared still have a high rate of discrimination, especially against Muslim minority groups.

Discrimination of smokers and nonsmokers based on the analysis of fingermarks for forensic purposes

For decades, the unique characteristic ridge pattern of a fingerprint (FP) has been used as a mainstay for human individualization. Most recently, vibrational spectroscopic analysis of fingermark (FM) has attracted a substantial amount of attention in providing intelligence on personal traits. Particularly, extracting phenotypical information such as age and sex can be significantly beneficial in criminal investigation. However, chemical profiling of FMs using vibrational spectroscopy has been limited to age and sex determination, while other significant traits can also aid in narrowing down the suspect pool. In the current proof-of-concept study, attenuated total reflection Fourier transform-infrared (ATR-FTIR) spectroscopy combined with partial least squares-discriminant analysis (PLS-DA) have been employed for determining smoker from non-smoker donors from their FM residues. Genetic algorithm (GA) was applied to improve the discrimination rate of the developed model by selecting regions that are significant for the distinguish between the two classes. A subject wise leave-one-out cross-validation (LOOCV) was initially used to evaluate the performance of the binary PLSDA classifier for each donor. The binary model showed 84% correct classification at spectral level and 92% correct classification at donor level in subject wise LOOCV. In addition, receiver operating characteristic (ROC) curve analysis was constructed to establish a threshold for differentiating between the smoker and non-smoker FMs, this resulted in 100% accuracy at a donor level for external validation test. This preliminary study shows a great promise for identifying smoker donors from non-smoker individuals based on chemical analysis of FM residues. After fully developed, we believe the method will offer significant potential for real crime scene investigation due to its simplicity, non-destructive nature and its prospective for in-field analysis.

Production regions discrimination of Huangguanyin oolong tea by using the content of chemical components and rare earth elements

Oolong tea is one of the most popular tea beverages in China. Tea cultivars, processing technology and origin of production affect the quality and price of oolong teas. To investigate the differences in Huangguanyin oolong tea from different production regions, the chemical components, mineral elements and rare earth elements of Huangguanyin oolong tea produced in Yunxiao (YX) and Wuyishan (WY) were analyzed by using spectrophotometry methods, targeted metabolomics and inductive plasma coupled mass spectrometry (ICP-MS). The results of spectrophotometry methods revealed that there were significant differences in thearubigin, tea polyphenols and water extract between Huangguanyin oolong teas from different production regions. Targeted metabolomics identified a total of 31 chemical components in Huangguanyin oolong teas from the two production regions, of which 14 chemical components were significantly different and contributed to the regional differentiation of Huangguanyin oolong tea. Yunxiao Huangguanyin had relatively higher contents of (-)-Epigallocatechin-3-O-(3-O-methylgallate) (EGCG3″Me), ornithine (Orn) and histidine (His), while Wuyishan Huangguanyin had relatively higher contents of glutamic acid (Glu), γ-aminobutyric acid (GABA), β-aminobutyric acid (β-ABA) and other components. Moreover, ICP-MS identified a total of 15 mineral elements and 15 rare earth elements in Huangguanyin oolong tea from the two production regions, of which 15 elements were significantly different between YX and WY, and contributed to the regional differentiation of Huangguanyin oolong tea. K had a relatively higher content in Yunxiao Huangguanyin, while rare earth elements had relatively higher contents in Wuyishan Huangguanyin. The classification results by the production region showed that the discrimination rate of the support vector machine (SVM) model based on the 14 different chemical components reached 88.89%, while the SVM model based on the 15 elements reached 100%. Therefore, we used targeted metabolomics and ICP-MS techniques to screen and explore the chemical components, mineral elements and rare earth elements differences among two production regions, which indicated the feasibility of Huangguanyin oolong tea classification by production regions in the study. The results will provide some reference for the distinction between the two production regions of Huangguanyin oolong tea.

Several ML Algorithms and Their Feature Vector Design for Gas Discrimination and Concentration Measurement with an Ultrasonically Catalyzed MOX Sensor.

Although gas-borne ultrasound catalysis has been developed as a new method to discriminate gas species and measure the concentration, applications of machine learning methods in gas analyses with a single metal oxide (MOX) gas sensor catalyzed by gas-borne ultrasound are still scarce. In this work, with an ultrasonically catalyzed MOX gas sensor, we explored the effectiveness of K-nearest neighbors (KNN), support vector machine (SVM), and single-hidden-layer BP-ANN (SHBP) in gas discrimination and the application of the SHBP in concentration measurement. The target gases in this work are ethanol, acetone, methanol, hydrogen, and n-butane in clean air, respectively, and the discrimination and concentration regression are implemented by two different ML models. With the properly designed feature vectors, the SHBP method has an acceptable capability of both of species discrimination and concentration regression (success rate of gas discrimination = 99.5%, relative error of concentration regression = 6.406%). The KNN and SVM methods have similar capabilities of gas discrimination as the SHBP. This work also demonstrates a method to design the feature vectors for the ultrasonically catalyzed MOX gas sensor and to choose the feature parameters.

The importance of using inflammatory biomarkers and scoring systems in early assessment of severity and outcome of acute pancreatitis treatment

Acute pancreatitis (AP) is an inflammatory disease of the pancreas that causes local damage and systemic inflammatory response. Some of the numerical scoring systems used in the intensive care unit for the assessment of critically ill patients such as APACHE II and MEWS score could be used for AP, beside the scoring systems specific to AP (Ranson score, Pancreas score, BISAP). Therefore, the aim of this study was to examine the significance of inflammatory biomarkers and scoring systems in the evaluation of the severity of AP and outcomes. The study was conducted as a cohort prospective study, examining patients with AP, of both sexes. Laboratory analyses, as well as the scoring systems: Ranson, Pancreas score, Bedside Index of Severity in Acute Pancreatitis (BISAP), and Acute Physiology and Chronic Health Evaluation II (APACHE II) were collected on day zero and 48h after admission. The study included 50 patients of whom 8 died. The most reliable score for predicting AP severity was APACHE II0 and 48AUROC (0.753; 0.768) in relation to the inflammatory biomarkers. For initial prediction of the treatment outcome, APACHE II0, BISAP0, and CRP0 AUROC (0.813; 0.807; 0.753) had the highest discrimination rates and after 48h, APACHE II48, Ranson48, BISAP48, and Pancreas48 AUROC (0.917; 0.856; 0.789; 0.729). There was a statistically significant correlation between CRP0 and BISAP0 (p=0.006) and between PCT and all day-zero scores. All tested screening systems showed reliability in predicting AP severity and treatment outcomes. The best predictive power was demonstrated by the APACHE II score.

Effects of weight stigma on BMI and inflammatory markers among people living with obesity

ObjectiveWeight stigma (WS) and prejudice are one of the most prevalent ways of discrimination among adults, comparable with rates of racial discrimination. Exposure to WS among patients with obesity (PWO) may make the adoption of healthy dietary patterns and regular physical activity even more challenging and, therefore, the achievement of weight loss. Additionally, WS could also induce physiological responses such as increased levels of inflammatory markers, due to stress exposure. MethodSubjects attending two obesity clinics were evaluated at baseline and after a minimum follow-up of six months. The weight Bias Internalization Scale (WBIS) and the Stigmatizing Situations Inventory (SSI) were administered to evaluate WS. Also, anthropometric and inflammatory markers, including cortisol, ferritin and C-reactive protein (CRP), were recorded at baseline. Results79 PWO (87.3%♀, 45.5 ± 1.3 years, 35.9 ± 6.3 kg/m2) were included. At baseline, 72.2% started liraglutide as anti-obesity drug. Baseline body mass index (BMI) correlated positively with both WBIS (r = 0.23; p = 0.03) and SSI (r = 0.25; p = 0.02) scores. Mean percentual weight loss after a mean follow-up of six months was -7.28%. However, there was a negative, but not statistically significant, correlation between weight loss and both WBIS (r=-0.14; p = 0.2) and SSI (r=-0.19; p = 0.08). Regarding inflammatory markers, plasma cortisol levels at baseline correlated positively with WBIS (p = 0.005) and SSI (p = 0.02). CRP at baseline also presented a positive correlation with SSI (p = 0.03). No significant correlations were found for stigma tests and ferritin levels. DiscussionAs weight increases among PWO, so does stigma. Despite we did not find a significant negative association between the presence of WS and weight loss outcomes, there was an increase in inflammatory markers among PWO who experienced higher levels of WS.

Gender and Racial Discrimination in the U.S. Music Industry

This study summarizes and analyzes the gender and racial discrimination that musicians may face in the music labor market of the United States, with a focus on exploring gender and race interactions’ effects on the economic return of musicians. This study is based on a survey of 1,227 musicians in the United States in 2018, which was conducted by the Music Industry Research Association (MIRA) and the Princeton University Survey Research Center (SRC), in partnership with MusiCares. The survey reveals that females, who make up about one-third of the population of musicians, report experiencing high rates of discrimination and sexual harassment. When considering female musicians, 72% report that they have been discriminated against because of their gender, and 67% report that they have been the victim of sexual harassment; corresponding figures for U.S. women more generally are 28% and 42%, respectively. For non-White musicians, 63% said they faced racial discrimination, as compared to 36% of on-White self-employed workers nationwide who said the same. Although the descriptive statistics suggest that musicians are suffering from both gender and racial discrimination, no initial empirical evidence has been found that this would have a negative impact on musicians’ music-related earnings. The interaction effects between gender and race in both general and specific forms on music-related earnings are studied to further explore the possible effect of gender/racial discrimination on the economic return to musicians’ music-related activities; however, although some race-associated differences have been found, the evidence of racial discrimination on the economic return is not strong. JEL Classifications: J15, J16, J44, J49

Medical Students’ Experiences of Mistreatment by Clinicians and Academics at a South African University

Phenomenon: Tertiary education in post-apartheid South Africa has faced many challenges regarding class, language, and race. Even though previously white Afrikaans-rooted universities now have a diverse student population, recent student protests have highlighted the ongoing need for decolonization in higher education. In addition, the majority of public hospitals in the country function under significant staffing, infrastructure, and equipment shortages. Although the mistreatment of medical students has been well described internationally, to date no South African data exists. The aim of this study was to identify experiences of mistreatment of medical students by clinicians and academics at a South African university and to describe the type of mistreatment experienced, the perceived mental health effects, and the influence on academic performance, resilience, and students’ knowledge of current reporting systems. Approach: A cross-sectional study was conducted through a locally developed online survey of 443 medical students at a South African university in May to June 2018, comprising of both open and closed ended questions. Levels of psychological distress (K10) and resilience (CD-RISC -10) were measured. Chi-square and student t-tests were used for the analysis of associations, and linear regressions were used to assess predictors of psychological distress. Qualitative data were analyzed thematically using the approach described by Braun and Clarke. Findings: Of 800 eligible medical students at Stellenbosch University, 443 students (55.4%) completed the survey. Mistreatment, comprising of being ignored/excluded (83.4%), offensive gestures (75.0%), verbal abuse (65.1%) and discrimination (64.4%), was prevalent and pervasive, and was perpetrated mainly by registrars (46.7%) and other medical staff (43.8%). Mistreatment was associated with psychological distress, which was generally high and more severe for females. Resilience, which was higher for males, moderated the effects of gender and perpetrator type on distress. Only 15% of students who had experienced mistreatment, either directly or indirectly, reported it, of which more than half (52.8%) were not happy with the outcome. Most students (80.9%) were not aware of the systems in place to report mistreatment. Insights: Student mistreatment is more highly prevalent among medical students at a South African university compared with studies conducted internationally. Despite over 20 years of democracy in South Africa, high rates of racial and gender discrimination were reported and descriptions of racial, language and gender discrimination were particularly concerning. Since the findings of this study, an anti-bullying poster-campaign has been initiated at the university as well as an online reporting system.

Photon number resolution without optical mode multiplication

Abstract Common methods to achieve photon number resolution rely on fast on-off single-photon detectors in conjunction with temporal or spatial mode multiplexing. Yet, these methods suffer from an inherent trade-off between the efficiency of photon number discrimination and photon detection rate. Here, we introduce a method of photon number resolving detection that overcomes these limitations by replacing mode multiplexing with coherent absorption of a single optical mode in a distributed detector array. Distributed coherent absorption ensures complete and uniform absorption of light among the constituent detectors, enabling fast and efficient photon number resolution. As a proof-of-concept, we consider the case of a distributed array of superconducting nanowire single-photon detectors with realistic parameters and show that deterministic absorption and arbitrarily high photon number discrimination efficiency can be achieved by increasing the number of detectors in the array. Photon number resolution without optical mode multiplication provides a simple yet effective method to discriminate an arbitrary number of photons in large arrays of on-off detectors or in smaller arrays of mode multiplexed detectors.

Trends in Racial Discrimination Experiences for Asian Americans During the COVID-19 Pandemic.

Asian Americans (AAs) are experiencing increased rates of anti-Asian racism during COVID-19. Experiences of racism, whether personal or collective, constitute stress and psychosocial trauma that negatively impact mental and physical health. Examine subgroup differences in rates of personal experience of discrimination and COVID-related collective racism and how each is associated with mental and physical health for AAs. Nationally representative data from the 2021 Asian American and Native Hawaiian/Pacific Islander COVID-19 Needs Assessment Project were used to estimate prevalence rates of discrimination and average COVID-related collective racism scores for AAs (unweighted N = 3478). We conducted logistic and linear regression models to examine subgroup differences by sociodemographic factors. We also conducted hierarchical logistic regression models to examine associations between racism and psychological distress and health decline. Twenty-four percent of AAs (95% CI: 21.6, 25.6) reported experiencing discrimination during the first year of the COVID-19 pandemic. Subgroup analyses revealed that Chinese, younger adults, and AAs who completed the survey in an Asian language were significantly more likely to experience discrimination compared to their counterparts. For COVID-related collective racism, subgroup analyses revealed that Chinese, women, and adults ages 25-44 were more likely to report experiences of collective racism compared to their counterparts. Both discrimination and collective racism were independently associated with negative mental and physical health. Discrimination and COVID-related collective racism are associated with negative mental and physical health outcomes for AAs. Results point to vulnerable AA subgroups and the need for targeted public health efforts to address racism in the context of COVID-19.

Rapid Characterization of Black Tea Taste Quality Using Miniature NIR Spectroscopy and Electronic Tongue Sensors.

The taste of tea is one of the key indicators in the evaluation of its quality and is a key factor in its grading and market pricing. To objectively and digitally evaluate the taste quality of tea leaves, miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors are considered effective sensor signals for the characterization of the taste quality of tea leaves. This study used micro-NIR spectroscopy and ET sensors in combination with data fusion strategies and chemometric tools for the taste quality assessment and prediction of multiple grades of black tea. Using NIR features and ET sensor signals as fused information, the data optimization based on grey wolf optimization, ant colony optimization (ACO), particle swarm optimization, and non-dominated sorting genetic algorithm II were employed as modeling features, combined with support vector machine (SVM), extreme learning machine and K-nearest neighbor algorithm to build the classification models. The results obtained showed that the ACO-SVM model had the highest classification accuracy with a discriminant rate of 93.56%. The overall results reveal that it is feasible to qualitatively distinguish black tea grades and categories by NIR spectroscopy and ET techniques.

Gender in mental health: Relationship of spirituality, social support, and COVID-19-related fear among heterosexual and LGBTQ+ youth.

The youth is a vulnerable population to COVID-19-related fear. Among them, those with LGBTQ+ identities are at higher risk. Given the posited protective effects of spirituality and social support on fear, this study explored their effects on COVID-19-related fear among heterosexual and LGBTQ+ youth. This cross-sectional study recruited 137 respondents Filipinos aged 18-30 years old. The respondents answered a survey containing a sociodemographic questionnaire, 12-items Multidimensional Scale of Perceived Social Support (MSPSS), 20-items Core Dimensions of Spirituality Questionnaire (CDSQ), and 7-items Fear of COVID-19 Scale (FCS). After, the data were analyzed using means, frequencies, Mann-Whitney U-tests, and correlation coefficients. Social support from friends was negatively correlated with COVID-19-related fear among heterosexual respondents, R = -0.219, p = 0.029. Search for meaning positively correlated with COVID-19-related fear among LGBTQ+ respondents, R = 0.395, p = 0.016. Heterosexual respondents also have a higher belief in God (U = 1,004, p < 0.001) and feelings of security (U = 1,110.5, p < 0.001) than their LGBTQ+ counterparts. These findings suggest that social support from friends is protective against COVID-19-related fear among heterosexual youth but not among LGBTQ+. In addition, a high search for meaning can heighten COVID-19-related fear among LGBTQ+. Finally, these findings can be explained by the higher rates of discrimination against LGBTQ+ than heterosexual youth. Thus, gender-responsive mental healthcare is needed to address the youth's fears as society recovers from the pandemic.

Application of Fuzzy algorithms in conjunction with 1 H-NMR spectroscopy for alcoholic beverages differentiation.

According to recent statistics elaborated at the European Union level, wine represents one of the most falsified commodities. In this context, the development of reliable classification models for alcoholic beverages differentiation requires, besides sensitive analytical tools, the use of the most suitable data processing methods like those based on advanced statistical tools or artificial intelligence. The present study aims to establish a new innovative approach for alcoholic beverages differentiation (wines and fruit distillates) able to increase the discrimination rate of the developed models. Thus, a data dimensionality reduction step, performed on the obtained 1 H-NMR profiles, was applied. This stage consisted in the application of Fuzzy Principal Component Analysis (FPCA) before themodel development through discriminant analysis. The enhancement of the classification potential given by the application of FPCA as compared to Principal Component Analysis (PCA) was discussed. The association between 1 H-NMR spectroscopy and an appropriate statistical approach proved to be a very effective tool for alcoholic beverages differentiation. In order to develop reliable metabolomic approaches for the differentiation of wines and fruit distillates, 1 H-NMR spectroscopic data were exploited in corroboration with fuzzy algorithms for data dimensionality reduction. The study proved the efficiency of using FPCA scores over those obtained through the widely applied PCA. Based on the proposed approach, it was proved that for wine classification a perfect separation was obtained according to the geographical origin, cultivar and vintage. A percentage of 100% correctly classified samples was also achieved for botanical and geographical differentiation of fruit distillates. This article is protected by copyright. All rights reserved.

Systematic audit of Australian specialty colleges' policies on bullying, discrimination, and harassment.

Objective To support policy makers and healthcare workers to reduce high rates of bullying, discrimination, and harassment (BDH) that affect doctors in Australian specialty training sites. Methods This audit assessed the quality of policies regarding BDH and associated complaints of the Australian Medical Council-accredited Australasian specialty training Colleges (Colleges). Policies were systematically identified and scored against a national standard BDH policy checklist. Results Fourteen of the 16 Colleges have each written and curated their own BDH policies for their members, with wide variation between Colleges regarding content and processes. This impairs the efficacy of BDH handling in specialty training sites. Conclusions Key areas for improvement were identified. A checklist is proposed that is specific to College BDH policies and processes involving specialty training sites.

Online Racism and Mental Health Among Black American Adolescents in 2020.

To determine whether rates of online racial discrimination changed over the course of 2020 and their longitudinal effects on Black youths' mental health. This longitudinal study collected 18,454 daily assessments from a nationally representative sample of 602 Black and White adolescents in the United States (58% Black, 42% White; mean age= 15.09 years, SD= 1.56 years) across 58 days during the heightened racial tensions between March and November2020. Black youths experienced increases in online racial discrimination, and these increases were not fully explained by time spent online or by general cybervictimization experiences. Online racial discrimination predicted poorer same-day and next-day mental health among Black youths but not among White youths. Black youths' mental health did not predict their online racial discrimination experiences. Online racial discrimination has implications for shaping mental health disparities that disadvantage Black youths relative to their White peers. Programs can be implemented to decrease online hate crimes, and health providers (eg, pediatricians, psychiatrists) should develop procedures that mitigate the negative mental health effects following online racial discrimination experiences.

*e-mail: jez@unb.br Editor handled this article: Ivo M. Raimundo Jr. (Associate) Diffuse Reflectance FTIR of Latent Fingerprints and Discriminant Analysis for Sex Identification in Humans

Latent fingerprint is an important crime scene evidence, but it is not always recoverable or technically suitable for analysis with fingerprint patterns. Forensic science has shown that other information can be explored from traces using chemical compounds. Infrared spectroscopy is a nondestructive technique that is widely applied to a variety of forensic evidence. In this work, infrared spectroscopy and partial least square discriminant analysis were used to determine the human sex based on latent fingermark analysis. Fingerprint samples were taken from 42 male and female donors, then kept in either dark or light storage conditions, and the Fourier transform infrared (FTIR) spectra were measured considering a period of up to 30 days from collection. The regions from 3000 to 2800 cm-1 and 1790 to 1150 cm-1 presented the greatest differences in the peak intensities among the two sex groups. The results showed a correct discrimination rate higher than 80%.

A survey on the public's discrimination rate of typical rumors during the coronavirus disease 2019 epidemic.

During the epidemic of coronavirus disease-2019 (COVID-19), the wide spread of rumors caused significant public hazards. This study aims to understand the situation of discrimination for typical COVID-19 rumors by the public and related factors. An anonymous online survey was carried out using Questionnaire Star. The contents included participants' gender, age, education level, the COVID-19 information sources, and the judgmental questions about 14 representative COVID-19 rumors. The discrimination rate and 95% confidence interval of 14 rumors were estimated, and the association of discrimination rate with gender, age, and education level was analyzed by binary logistic regression. A total of 2 087 valid questionnaires were collected. The participants were mainly female (62.7%) and below 35 years old (63.4%); the education level was predominantly college/bachelor's degree (47.3%) and master's degree or above (39.1%); the participants, who accessed to COVID-19 information included internet media, accounted for 91%. The participants with different gender, age, and education level had significant differences in the distribution of COVID-19 information sources (all P<0.01). The participants' discrimination rate for 14 rumors ranged from 67.4% to 98.6%, with 4 rumors less than 80%. Women's discrimination rate of 9 rumors was significantly higher than men's (all P<0.05). There was no significant difference in the discrimination rate of rumors among the different age groups (all P>0.05), but the differences in the discrimination rate of other rumors among the different age groups varied according to the rumor. Compared to those with high school or less education levels, the discrimination rates were also higher in the respondents with high education levels (P<0.05). A few publics are still unable to identify typical rumors during the COVID-19 epidemic. There are associations among genders, age, and the education levels with the discrimination of some rumors. The government authorities should strengthen the true information regarding COVID-19, and therefore enhance the public's ability to identify rumors.

Distinction of Different Colony Types by a Smart-Data-Driven Tool.

Colony morphology (size, color, edge, elevation, and texture), as observed on culture media, can be used to visually discriminate different microorganisms. This work introduces a hybrid method that combines standard pre-trained CNN keras models and classical machine-learning models for supporting colonies discrimination, developed in Petri-plates. In order to test and validate the system, images of three bacterial species (Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus) cultured in Petri plates were used. The system demonstrated the following Accuracy discrimination rates between pairs of study groups: 92% for Pseudomonas aeruginosa vs. Staphylococcus aureus, 91% for Escherichia coli vs. Staphylococcus aureus and 84% Escherichia coli vs. Pseudomonas aeruginosa. These results show that combining deep-learning models with classical machine-learning models can help to discriminate bacteria colonies with good accuracy ratios.

EXPLORING RACIAL DISPARITIES IN AD/ADRD RISK: EVERYDAY DISCRIMINATION AS A SOCIAL STRESSOR

Abstract Using evidence from the Health and Retirement Study (HRS), we explore racial disparities in Alzheimer’s Disease and Related Dementia (AD/ADRD) risk. From a stress process perspective, there is substantial evidence in the literature that everyday discrimination is a chronic strain for Black individuals that acts as a social determinant of illness. However, few studies have examined specific relationships between this social stressor, race and AD/ADRD risk. Using generalized linear regression and Cox proportional hazard models, we explored racial differences in exposure and vulnerability to everyday discrimination. Results suggest that Black individuals experience more frequent exposure to everyday discrimination than their counterparts, and this chronic strain predicts AD/ADRD risk. However, contrary to the stress process model, White respondents were more vulnerable to the effect of everyday discrimination on AD/ADRD risk. Racial bias from medical professionals during the diagnostic process as well as mortality selection bias may explain these unexpected findings, especially given that racial minorities who experience high rates of everyday discrimination may not live long enough to reach the average age of diagnosis. These results provide further evidence that discrimination is a key factor in explaining racial disparities in late-life disorders, while also taking into consideration that many racial minorities with high rates of this type of stress may not survive to late-life.

Chilean Trans Men: Healthcare Needs and Experiences at the Public Health System

Introduction: Research shows high rates of discrimination against the transgender population in healthcare centers, which has negative consequences for their quality of life. There are few studies in this area that focus upon the experiences of trans men, although, they tend to experience higher levels of violence and stigmatization in healthcare settings. Method: We explored the perceptions of 14 Chilean trans men regarding discrimination in healthcare centers from an ethnographic approach, with the aim of analyzing their experiences in these facilities, and identifying their needs in relation to healthcare. Results: The majority of trans men perceived health centers as a source of discrimination. The most important expressions of discrimination involve questioning their gender identity, and disregarding their social names and pronouns. In view of this, interviewees placed fear of discrimination as the main factor preventing them from being attended at healthcare centers. On the other hand, having access to health workers who are trained in providing care to the transgender population, being provided with information, and the possibility of receiving psychosocial support are the main factors promoting their attendance at these facilities. Discussion: The study has implications both for health professionals and public policies focused on the transmasculine community. Recommendations are made for professionals and administrative staff to provide respectful and sympathetic care in order to generate spaces free of discrimination that encourage attendance at healthcare centers, improving the welfare of trans men.