Search Results (12)

This paper addresses three research questions: (1) According to the pertinent literature, what are the main causes of the current gender gap in the construction industry, particularly for management and leadership roles? (2) Is there a significant difference between male and female high-school-age students regarding their level of knowledge of the construction industry? (3) Is there a significant difference between male and female high-school-age students in their perception of the construction industry and factors impacting their career choice? These research questions are addressed by a review of the literature followed by a discussion of precamp survey results from two consecutive years of a two-week high school summer camp, which is focused on construction science and management. The literature review reveals that the issues are deep-routed and complex but can in general be categorized into two groups: the recruitment and retention of women in construction management and related careers. While the literature review summary in this paper addresses both categories, the focus of the paper remains recruitment, particularly through a study of high school students’ level of knowledge and perceptions of construction management. The pre-camp surveys assessed the participants’ baseline knowledge and perceptions of construction management as a career. The results showed that the knowledge of construction management as a career is very low, with no statistically significant difference between genders. It must be noted here that the participants are biased positively toward the construction management and related careers, as they chose to attend the camp for a variety of reasons, from wanting an experience on the campus of this university to parents’ encouragement. Despite this baseline interest, however, the data show that the participants lacked clarity on what this career really entails. It is suggested, therefore that the general knowledge of this career path across larger and more randomized samples across U.S. will be even lower. The perceptions of the construction industry differed slightly between genders, with females perceiving it as more physically challenging and prioritizing family friendliness when considering career options. In conclusion, both the literature review and the survey data analysis show that the lack of knowledge of this career path, exacerbated by the negative perceptions of the construction industry, contribute to the problem of women not choosing to study construction management. The construction industry continues to make significant improvements in their diversity and inclusion efforts, and there are a variety of paths within the industry for graduates of construction science and management degrees. As such, significant work remains for both the industry and academia to overcome the lack of knowledge and negative perceptions of the industry through increased outreach to better inform high school students, parents, and counselors. Full article

Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward. Full article

A negative interpretation bias appears to depend on several depression-related state and trait characteristics, most notably depressive symptoms, negative mood, and negative cognitive schemas. While empirical findings for explicitly assessed interpretation bias are rather consistent, implicit measures have revealed heterogeneous results. In this context, we present two studies investigating the relationship between implicit and explicit interpretation bias and depression- and anxiety-related state and trait variables. In the first study, we conducted an implicit ambiguous cue-conditioning task (ACCT) with 113 young, healthy individuals. In the second study, we utilized an explicit ambiguous social situations task (DUCTUS) with 113 young, healthy individuals. Additionally, a subsample of 46 participants completed both the ACCT and DUCTUS tasks to directly relate the two bias scores obtained from the implicit and explicit assessment methods, respectively. In the first study, regression analysis revealed no significant predictors for the implicit interpretation bias. However, in the second study, the explicit negative interpretation bias was significantly predicted by female gender, depressive symptoms, and dysfunctional cognitive schemas. For the subsample that completed both tasks, we observed no significant correlation between the two bias scores obtained from the ACCT and DUCTUS. These results suggest that implicit and explicit interpretation biases are differently associated with depression-related trait and state characteristics, indicating that they represent different aspects of biased information processing. Full article

Children’s access and opportunities to play sports are influenced by categories of difference, such as gender, race, and socioeconomic status. In order to provide an inclusive community and facilitate the recruitment and retention of diverse youth, athletic organizations should be aware of implicit bias and how this can affect the relationship between volunteers and the children they serve. This paper presents a formative process evaluation of a diversity, equity, and inclusion (DEI) training program for a non-profit athletic organization. Training was implemented in person in a group setting with multiple opportunities for group discussion. Mixed methods were used to monitor (1) the implementation of training, (2) its effects on attitudes throughout training, and (3) impact one month after training. Findings demonstrate that the program increased participants’ ability to identify DEI initiatives and sense of belonging to the organization, but these effects had declined by the end of the first month after training. However, participants’ self-beliefs regarding DEI and overall willingness to engage in DEI activities remained enhanced one month after training. Qualitative data were helpful in providing insight into how training impacted participants and their interactions within and outside of the organization. Through this mixed methods approach, we can conclude that DEI training did in fact have a positive impact on the organization, but further evaluation and training may be necessary to address the decline in some effects seen one month after training. Full article

Background: Unconscious biases are one of the causes of health disparities. Health professionals have prejudices against patients due to their race, gender, or other factors without their conscious knowledge. This review aimed to provide an overview of research on unconscious bias among health professionals and to investigate the biases that exist in different regions of the world, the health professions that are considered, and the research gaps that still exist. Methods: We conducted a scoping review by systematically searching PubMed/MEDLINE, CINAHL, PsycINFO, PsycARTICLES, and AMED. All records were double-screened and included if they were published between 2011 and 2021. Results: A total of 5186 records were found. After removing duplicates (n = 300), screening titles and abstracts (n = 4210), and full-text screening (n = 695), 87 articles from 81 studies remained. Studies originated from North America (n = 60), Europe (n = 13), and the rest of the world (n = 6), and two studies were of global scope. Racial bias was investigated most frequently (n = 46), followed by gender bias (n = 11), weight bias (n = 10), socio-economic status bias (n = 9), and mental illness bias (n = 7). Most of the studies were conducted by physicians (n = 51) and nurses (n = 20). Other health care professionals were rarely included in these studies. Conclusions: Most studies show that health professionals have an implicit bias. Racial biases among physicians and nurses in the USA are well confirmed. Research is missing on other biases from other regions and other health professions. Full article

(1) Background: Women have become more influential and powerful; however, implicit bias continues to plague organizations when it comes to women in leadership positions. This study examines the implicit and explicit biases that favor men as leaders among Saudi Arabian primary healthcare professionals. (2) Methods: A secure, web-based survey was administered to primary healthcare professionals. The survey included questions about leadership as well as an Implicit Association Test (IAT) for implicit gender bias. (3) Results: Out of 690 eligible, 448 respondents completed the survey, representing a response rate of 65%. Male residents had a mean IAT score of 0.27 (SD 0.31) and females 0.12 (SD 0.29), both favoring males in leadership roles, and the difference was statistically significant. There was a significant association between gender and gender IAT. In the explicit bias, gender, education, gender of the current manager, and being manager were associated with the gender explicit bias. Explicit bias favoring males in leadership roles was associated with increased implicit bias favoring males in leadership roles. (4) Conclusions: This study found that explicit and implicit gender bias is present among primary healthcare professionals favoring men in leadership positions held by both men and women. Full article

Strategies that introduce students to unconscious bias and social determinants of health (SDOH) are critical to develop them as effective health care providers. We developed a semester-long activity that utilizes disease-relevant scientific literature to implement cultural humility training in a second-year rheumatology pharmacy course. Students were first re-introduced to implicit bias and then completed an anonymous survey at the beginning and conclusion of the course using a 5-point Likert scale to assess their perceptions of the role of biases and SDOH in patient care. Throughout the semester, five journal articles were assigned that relate to course material and focus on one characteristic (e.g., gout—gender). Students’ evolved perceptions of SDOH were compared to baseline data and characteristics of assigned articles indicate an improved understanding of SDOH including race/ethnicity (3.0 to 4.4, p < 0.0001); gender (2.8 to 4.0, p < 0.0001); and religion (2.3 to 2.9, p < 0.01). Among characteristics that were not directly discussed in the assignments, only education showed a significant increase (3.0 to 3.6, p < 0.01). Scientific articles that focus on health inequities relevant to course-specific diseases provide a strategy to integrate discussions that help students evaluate their biases and SDOH with the goal of improving patient care. Full article

Recent works in machine learning have focused on understanding and mitigating bias in data and algorithms. Because the pre-trained models are trained on large real-world data, they are known to learn implicit biases in a way that humans unconsciously constructed for a long time. However, there has been little discussion about social biases with pre-trained face recognition models. Thus, this study investigates the robustness of the models against racial, gender, age, and an intersectional bias. We also present the racial bias with a different ethnicity other than white and black: Asian. In detail, we introduce the Face Embedding Association Test (FEAT) to measure the social biases in image vectors of faces with different race, gender, and age. It measures social bias in the face recognition models under the hypothesis that a specific group is more likely to be associated with a particular attribute in a biased manner. The presence of these biases within DeepFace, DeepID, VGGFace, FaceNet, OpenFace, and ArcFace critically mitigate the fairness in our society. Full article

Recent U.S. elections have witnessed the Democrats nominating both black and female presidential candidates, as well as a black and female vice president. The increasing diversity of the U.S. political elite heightens the importance of understanding the psychological factors influencing voter support for, or opposition to, candidates of different races and genders. In this study, we investigated the relative strength of the implicit biases for and against hypothetical presidential candidates that varied by gender and race, using an evaluative priming paradigm on a broadly representative sample of U.S. citizens (n = 1076). Our main research question is: Do measures of implicit racial and gender biases predict political attitudes and voting better than measures of explicit prejudice? We find that measures of implicit bias are less strongly associated with political attitudes and voting than are explicit measures of sexist attitudes and modern racism. Moreover, once demographic characteristics and explicit prejudice are controlled statistically, measures of implicit bias provide little incremental predictive validity. Overall, explicit prejudice has a far stronger association with political preferences than does implicit bias. Full article

Implicit or unconscious bias is commonly proposed to be responsible for women’s underrepresentation in academia. The aim of this scoping review was to identify and discuss the evidence supporting this proposition. Publications about unconscious/implicit gender bias in academia indexed in Scopus or psycInfo up to February 2020 were identified. More than half were published in the period 2018–2020. Studies reporting empirical data were scrutinized for data, as well as analyses showing an association of a measure of implicit or unconscious bias and lesser employment or career opportunities in academia for women than for men. No studies reported empirical evidence as thus defined. Reviews of unconscious bias identified via informal searches referred exclusively to studies that did not self-identify as addressing unconscious bias. Reinterpretations and misrepresentations of studies were common in these reviews. More empirical evidence about unconscious gender bias in academia is needed. With the present state of knowledge, caution should be exercised when interpreting data about gender gaps in academia. Ascribing observed gender gaps to unconscious bias is unsupported by the scientific literature. Full article

Women in engineering continue to experience bias in the field. This constructivist case study uses feminist theory to examine the gendered experiences of graduating senior women engineering students in academic and workplace environments. In each setting we identified three subthemes; in academia: “I don’t think my education is any different,” “Being underestimated constantly,” and “You don’t want to be seen as getting advantages”; in the workplace: “Oh, you’re a girl,” “There’s a lot of sexism,” and Benefits of “girl power.” Overall, findings indicate that women experience bias in both settings, often via implicit bias in academia and with instances of implicit bias, sexism, and sexual harassment occurring even more often in the workplace through internship experiences. The article concludes with suggestions for practice, future research, and strategies to create supportive academic and workplace experiences and environments for women engineers. Full article

This paper offers an unorthodox appraisal of empirical research bearing on the question of the low representation of women in philosophy. It contends that fashionable views in the profession concerning implicit bias and stereotype threat are weakly supported, that philosophers often fail to report the empirical work responsibly, and that the standards for evidence are set very low—so long as you take a certain viewpoint. Full article