Search Results (34)

Currently, there are no validated instruments in Italian specifically designed to assess mental illness stigma or prejudice. Moreover, implicit measures, while insightful, are often resource-intensive and impractical for large-scale population studies of Italian speakers. The present study investigated the validity of the Italian version of the Prejudice towards People with Mental Illness scale (PPMI-IT) in measuring biases toward individuals with mental health issues. The original instrument by Kenny et al. was translated from English into Italian and vice versa. A sample of 455 Italian-speaking participants (65% female; M_age = 33.39; SD = 13.21) was utilized to conduct a confirmatory factor analysis, confirming a four-factor structure (fear/avoidance, malevolence, authoritarianism, unpredictability). Factor loadings indicated that each dimension was well represented, supporting the construct validity of the scale. Model fit indices, including chi-square (χ² = 782.54, df = 296.00, χ²/df = 2.64), RMSEA (0.06, 90% CI: 0.060–0.07), CFI (0.93), TLI (0.91), and SRMR (0.06), suggest an excellent model fit. Furthermore, the analysis of correlations and the heterotrait/monotrait (HTMT) ratio provides evidence supporting the discriminant validity of the PPMI scale compared with social desirability. These findings confirm that the PPMI scale is a valid and reliable tool for assessing biases toward individuals with mental health issues, making it suitable for academic research, clinical interventions, and public policy contexts. Full article

Weight stigma remains a pervasive issue in contemporary society, impacting individuals’ psychological well-being, social inclusion, and access to opportunities. This study explored the lived experiences of overweight women, focusing on body image, stigma, and engagement with dominant health and beauty norms. Using a qualitative, phenomenological approach, online in-depth interviews were conducted with 14 women aged 25 to 51, primarily residing in southern and eastern Europe (Greece, Cyprus, Albania, Romania, and Bulgaria), with three participants from the United Kingdom. Thematic analysis revealed four key themes: workplace discrimination, pressures during pregnancy and the postpartum period, ambivalence toward body positivity movements, and the emotional toll of stigma, including extreme coping strategies. Participants described being marginalized professionally, scrutinized publicly and within families, and caught between ideals of inclusivity and persistent societal rejection. The findings emphasize the psychological burden of weight-based discrimination and the superficial nature of many body acceptance campaigns. This study calls for structural changes in healthcare, media, and employment practices to support body diversity and dismantle entrenched biases. By centering the voices of overweight women, the research contributes to broader discussions on embodiment, social justice, and intersectionality within the field of body image scholarship. Full article

The increasing adoption of artificial intelligence algorithms is redefining decision-making across various industries. In the financial sector, where automated credit granting has undergone profound changes, this transformation raises concerns about biases perpetuated or introduced by AI systems. This study investigates the methods used to identify and mitigate biases in AI models applied to credit granting. We conducted a systematic literature review using the IEEE, Scopus, Web of Science, and Science Direct databases, covering the period from 1 January 2013 to 1 October 2024. From the 414 identified articles, 34 were selected for detailed analysis. Most studies are empirical and quantitative, focusing on fairness in outcomes and biases present in datasets. Preprocessing techniques dominated as the approach for bias mitigation, often relying on public academic datasets. Gender and race were the most studied sensitive attributes, with statistical parity being the most commonly used fairness metric. The findings reveal a maturing research landscape that prioritizes fairness in model outcomes and the mitigation of biases embedded in historical data. However, only a quarter of the papers report more than one fairness metric, limiting comparability across approaches. The literature remains largely focused on a narrow set of sensitive attributes, with little attention to intersectionality or alternative sources of bias. Furthermore, no study employed causal inference techniques to identify proxy discrimination. Despite some promising results—where fairness gains exceed 30% with minimal accuracy loss—significant methodological gaps persist, including the lack of standardized metrics, overreliance on legacy data, and insufficient transparency in model pipelines. Future work should prioritize developing advanced bias mitigation methods, exploring sensitive attributes, standardizing fairness metrics, improving model explainability, reducing computational complexity, enhancing synthetic data generation, and addressing the legal and ethical challenges of algorithms. Full article

Background: The quality of healthcare experiences for Indigenous communities is worse when compared to non-Indigenous patients. Bias and discrimination within healthcare systems relate to worsened care and worsened health outcomes for Indigenous patients. The purpose of this study was to learn about the experiences of Indigenous people within healthcare settings, as well as their viewpoints for improving healthcare delivery to this population. Methods: Indigenous research methods were employed in this study as clinic administrators and staff, elders, and Indigenous researchers collaborated on the study purpose, design, and analysis. Twenty Indigenous patients participated in one of four focus groups regarding their experiences with healthcare systems. Results: Seven main themes emerged, highlighting participants’ experiences during health encounters, in relation to healthcare systems, and Indigenous health beliefs. Participants discussed challenges and barriers in each area and offered recommendations for care delivery to this population. Conclusions: Participants in this study highlighted that biased care results in poor quality of healthcare delivery and that there are actionable steps that providers and systems of healthcare can take to reduce bias within healthcare systems. The provision of culturally congruent care is imperative in improving the health and well-being of Indigenous communities. Full article

The forced migration of tens of thousands of refugee doctors exacerbates a phenomenon referred to as “brain waste”. Based on the Arksey and O’Malley model, this scoping review conducted in SCOPUS, ProQuest, CINAHL, and ERIC via EBSCO examines three decades of peer-reviewed literature (1990–2022) on resettled Afghan and Arab refugee International Medical Graduates (rIMGs) attempting, most often unsuccessfully, relicensing/professional reentry in the USA, Canada, the EU, Australia, and New Zealand. The search identified 760 unique citations, of which only 16 met the inclusion/exclusion criteria. Included publications explored (1) systemic and personal barriers to rIMG professional reentry and (2) existing supporting reentry programs and policy recommendations. The findings point to inconsistencies in evaluating medical education credentials and to racial profiling, inequities, and discrimination in residency interviews. The support provided by some programs was perceived as inadequate, confusing, biased, and gendered. The rIMG personal barriers identified included refugees’ unique limitations and life adversities. The review grasps a collection of isolated support programs with widely varying learning performance, unclear buy-in from residency program directors, and weak policy impacts. This analysis highlights the need for legislated and standardized rIMG reentry support programs to reduce physician shortages, health disparities, and, ultimately, IMG brain waste. Full article

Introduction: Women experience more social barriers, gender stereotypes, biases, and discrimination than men, which can increase their vulnerability to mental health problems. Therefore, it is essential to adopt a gender perspective in research on nursing students, examining the impact of these factors on their well-being and psychological resources like resilience. This study aims to analyze the relationship between gender roles in resilience and positive or negative affect among female nursing students. Methods: A cross-sectional study was conducted with first- and fourth-year female nursing students at a public university in Madrid, Spain. Sociodemographic variables, positive and negative affect (PANAS scale), resilience (CD-RISC scale), and gender roles (BRSI inventory) were analyzed. ANOVA, correlation analysis, and linear regression models were used to study the relationships between variables. Results: The study included 338 students with a mean age of 21.43 years, of which 80.2% had a high level of resilience, with a positive affect score of 31.96 (SD: 7.34) and a negative affect score of 22.99 (SD: 7.35). Overall, 48.5% had undifferentiated roles, 23.7% feminine roles, 14.2% androgynous roles, and 13.6% masculine roles. Female students with masculine and androgynous roles showed higher resilience levels (93.48% and 97.92%) compared to those with feminine and undifferentiated roles (81.25% and 70.73%) (p < 0.001). Female students with androgynous and masculine roles showed higher positive affect levels compared to those with feminine and undifferentiated roles (p < 0.001), with no differences in negative affect. These results were observed in both first- and fourth-year students. A high correlation was found between masculine roles and positive affect and resilience in both first- and fourth-year students. Conclusions: Gender roles influence positive affect and resilience in females. Among female nursing students, androgynous and masculine roles are associated with higher levels of resilience and positive affect compared to feminine and undifferentiated roles. Differences in psychological well-being may be related to socially constructed gender roles rather than biological sex, with masculine roles enhancing resilience and feminine roles correlating with greater vulnerability. Full article

Traffic stops represent a crucial point of interaction between citizens and law enforcement, with potential implications for bias and discrimination. This study performs a rigorously validated comparative machine learning model analysis, creating artificial intelligence (AI) technologies to predict the results of traffic stops using a dataset sourced from the Montgomery County Maryland Data Centre, focusing on variables such as driver demographics, violation types, and stop outcomes. We repeated our rigorous validation of AI for the creation of models that predict outcomes with and without race and with and without gender informing the model. Feature selection employed regularly selects for gender and race as a predictor variable. We also observed correlations between model performance and both race and gender. While these findings imply the existence of discrimination based on race and gender, our large-scale analysis (>600,000 samples) demonstrates the ability to produce top performing models that are gender and race agnostic, implying the potential to create technology that can help mitigate bias in traffic stops. The findings encourage the need for unbiased data and robust algorithms to address biases in law enforcement practices and enhance public trust in AI technologies deployed in this domain. Full article

This study explores the impact of Industry 5.0 on discriminatory behaviors toward migrant employees within organizations. Through semi-structured qualitative interviews with 15 migrant workers in the UK, this research identifies key challenges faced by migrant employees amidst the integration of advanced technologies like AI and robotics in HRM systems. Thematic analysis reveals that while Industry 5.0 has the potential to mitigate human biases, it can also perpetuate existing prejudices if not managed effectively. This study highlights two main themes: the experiences of discrimination and challenges in the context of Industry 5.0, and the role of technology in HRM systems. The findings indicate that automated HR systems can both reduce and increase biases, highlighting the importance of inclusive practices and targeted support programs to help migrant workers adapt to a technologically advanced labor market. This research contributes to the literature by providing insights into the duality of technological advancements in reducing and reinforcing workplace discrimination. Full article

Drought stress is one of the biggest hardships in wheat cultivation because of the strong negative relationship between water deficit and crop yields owing to a lower grain weight, a shorter grain-filling period, a slower grain-filling rate, and reduced grain quality. Genotype–environment interaction (GEN:ENV) generates hardships in selecting wheat genotypes and ideotypes due to biased genetic estimates. Diverse strategies have been proposed to respond to the urgent need for concurrent improvements in yield performance and stability. This study’s purpose was to appraise genetic variation and GEN:ENV effects on yield and yield components to discover drought-stress-tolerant genotypes and ideotypes. This study evaluated 20 genotypes in three consecutive seasons under non-stressful and drought-stress conditions in a total of six ENVs. The broad-sense heritability ranged from 0.54 to 0.82 based on expected mean squares and ranged from 0.60 to 0.90 based on plot mean, but in the other three ways, it was usually greater than 0.90. The high values of (${\mathsf{\sigma }}_{\mathrm{gen}:\mathrm{e}\mathrm{n}\mathrm{v}}^2$) revealed the effect that broad-sense heritability has on the expression of traits. G01, G03, G06, G07, G08, G10, G12, G13, G16, G17, and G18 were stable genotypes for grain yield (GY), according to additive main effects and a multiplicative interaction biplot for the six ENVs. Based on scores in the weighted average of absolute scores biplot (WAASB), G02, G04, G05, G08, G10, and G18 were selected as stable and high-performance for GY, and they were all selected as the best genotype groups using the WAASB-GY superiority index. From the results obtained from principal component analysis and hierarchical clustering and from the tolerance discrimination indices, G02, G04, G05, G18, and G19 are genotypes that produce a suitable yield under non-stressful and drought-stress conditions. In essence, combining approaches that take into consideration stability and high performance can contribute significantly to enhancing the reliability of recommendations for novel wheat genotypes. Full article

Variations in the size of animals of the same species but of different sex are called sexual size dimorphism. The aim of this study was to compare the biometrics between males and females of the Sanmartinero creole bovine, of Colombia, to establish if sexual dimorphism appears in the breed. A total of 94 animals (16 uncastrated males and 78 females, average age of 4.3 ± 1.4 and 4.2 ± 2.3 years, respectively) from three different farms were measured. A total of 21 linear variables were obtained using standard morphometric methods and live weight. A one-way NPMANOVA was used to evaluate between sexes, ages, and farms, a Principal Component Analysis was used to detect the most discriminating variables, and a multivariate regression used age as an independent value. Statistically significant differences were reflected between sexes (p = 0.033) and not by age and farms. The variables that differentiated the most between males and females were those related to size (thoracic circumference, body length, dorso-sternal diameter, height at the withers, height at the rump, and horn length), variables that were biased toward males, although only the height at the withers and the rump were the ones that presented statistically significant differences. Full article

In South Korea, the rapid proliferation of smartphones has led to an uptick in messenger phishing attacks associated with electronic communication financial scams. In response to this, various phishing detection algorithms have been proposed. However, collecting messenger phishing data poses challenges due to concerns about its potential use in criminal activities. Consequently, a Korean phishing dataset can be composed of imbalanced data, where the number of general messages might outnumber the phishing ones. This class imbalance problem and data scarcity can lead to overfitting issues, making it difficult to achieve high performance. To solve this problem, this paper proposes a phishing messages classification method using Biased Discriminant Analysis without resorting to data augmentation techniques. In this paper, by optimizing the parameters for BDA, we achieved exceptionally high performances in the phishing messages classification experiment, with 95.45% for Recall and 96.85% for the BA metric. Moreover, when compared with other algorithms, the proposed method demonstrated robustness against overfitting due to the class imbalance problem and exhibited minimal performance disparity between training and testing datasets. Full article

Introduction: The widespread use of the internet has brought numerous benefits, but it has also raised concerns about its potential negative impact on mental health, particularly among university students. This study aims to investigate the relationship between internet addiction (IA) and mental health (MH) in university students, as well as explore the mediating effects of positive psychological capital (PsyCap) in this relationship. Objective: The main goal of this study was to examine the psychometric properties of the measures and to determine whether internet addiction could negatively predict university students’ mental health, mediated through PsyCap. Method: A cross-sectional design with an inferential approach was employed to address this objective. The data were collected using the Psychological Capital Questionnaire (PCQ-24), Internet Addiction Scale (IAS), and Keyes’ Mental Health Continuum-Short Form (MHC-SF). The total sample of this study comprised 850 students from two large public higher education institutions in Ethiopia, of whom 334 (39.3%) were female and 516 (60.7%) were male, with a mean age of 22.32 (SD = 4.04). Several analyses were performed to achieve the stated objectives, such as Cronbach’s alpha and composite reliabilities, bivariate correlation, discriminant validity, common method biases, and structural equation modeling (confirmatory factor analysis, path analysis, and mediation analysis). Confirmatory factor analysis was conducted to test the construct validity of IAS, PCQ-24, and MHC-SF. Additionally, the mediating model was examined using structural equation modeling with the corrected biased bootstrap method. Results: The preliminary study results found that the construct validity of IAS, PCQ-24, and MHC-SF was excellent and appropriate. Furthermore, the findings demonstrate that internet addiction had a negative and direct effect on PsyCap and MH. Moreover, PsyCap fully mediated the relationship between IA and MH. Additionally, this study confirmed that all the scales exhibited strong internal consistency and good psychometric properties. Conclusion: This study contributes to a better understanding of the complex interplay between IA, PsyCap, and MH among university students, confirming previous findings. Recommendation: The findings, discussed in relation to the recent and relevant literature, will be valuable for practitioners and researchers aiming to improve mental health and reduce internet addiction by utilizing positive psychological resources as protective factors for university students. Full article

Stitching methods allow one to measure a wider surface without the loss of resolution. The observation of small details with a better topographical representation is thus possible. However, it is not excluded that stitching methods generate some errors or aberrations on topography reconstruction. A device including confocal microscopy (CM), focus variation (FV), and coherence scanning interferometry (CSI) instrument modes was used to chronologically follow the drifts and the repositioning errors on stitching topographies. According to a complex measurement plan, a wide measurement campaign was performed on TA6V specimens that were ground with two neighboring SiC FEPA grit papers (P#80 and P#120). Thanks to four indicators (quality, drift, stability, and relevance indexes), no measurement drift in the system was found, indicating controlled stitching and repositioning processes for interferometry, confocal microscopy, and focus variation. Measurements show commendable stability, with interferometric microscopy being the most robust, followed by confocal microscopy, and then focus variation. Despite variations, robustness remains constant for each grinding grit, minimizing interpretation biases. A bootstrap analysis reveals time-dependent robustness for confocal microscopy, which is potentially linked to human presence. Despite Sa value discrepancies, all three metrologies consistently discriminate between grinding grits, highlighting the reliability of the proposed methodology. Full article

Machine learning (ML) has become increasingly prevalent in various domains. However, ML algorithms sometimes give unfair outcomes and discrimination against certain groups. Thereby, bias occurs when our results produce a decision that is systematically incorrect. At various phases of the ML pipeline, such as data collection, pre-processing, model selection, and evaluation, these biases appear. Bias reduction methods for ML have been suggested using a variety of techniques. By changing the data or the model itself, adding more fairness constraints, or both, these methods try to lessen bias. The best technique relies on the particular context and application because each technique has advantages and disadvantages. Therefore, in this paper, we present a comprehensive survey of bias mitigation techniques in machine learning (ML) with a focus on in-depth exploration of methods, including adversarial training. We examine the diverse types of bias that can afflict ML systems, elucidate current research trends, and address future challenges. Our discussion encompasses a detailed analysis of pre-processing, in-processing, and post-processing methods, including their respective pros and cons. Moreover, we go beyond qualitative assessments by quantifying the strategies for bias reduction and providing empirical evidence and performance metrics. This paper serves as an invaluable resource for researchers, practitioners, and policymakers seeking to navigate the intricate landscape of bias in ML, offering both a profound understanding of the issue and actionable insights for responsible and effective bias mitigation. Full article

This is a study on indicators related to marital dissatisfaction. The research was conducted by the psychology department of a reproductive health institution in Mexico City. The objective was to know the relation between marital satisfaction/dissatisfaction and gender roles, self-esteem, the types of coping strategies and the types of violence perceived from the partner. It was a nonexperimental, retrospective, cross-sectional study of two samples—one of women and one of men—classified by marital satisfaction or dissatisfaction. The nonprobability quota sampling included 208 participants: 104 women and 104 men. Comparisons, correlations and a discriminant analysis were made to identify the most significant variables. Women with marital dissatisfaction perceived blackmail, psychological violence and humiliation/devaluation from their partner; they preferably adopt a submissive gender role and use escape/avoidance as a coping strategy, and so do the men with marital dissatisfaction, who also perceived blackmail, control and psychological violence from their partner; they have low self-esteem, and they preferably adopt a submissive gender role. Isolating factors will allow for more specificity in terms of psychological care at health institutions as well as avoiding gender biases and preventing an increase of violence in couples. Full article