Gender Disparity within the Ivory Tower: Do Perceived Organizational Support and Procedural Justice Predict Self-Discrepant Time Allocation?

In the workplace, gender norms often affect women more negatively than men. Although women have demonstrated their abilities and competence in a variety of occupations and workplace settings, progress toward gender equity in academia is at a plateau. Using organizational support and organizational justice literature as a theoretical foundation, the purpose of the current study was to determine if two antecedents—perceived organizational support and procedural justice—influence how academics allocate their time spent on research, service, and teaching during the workweek and weekend. Ideal (i.e., preferred) time allocation and actual time allocation were examined. In addition, gender was proposed as a moderator of these relationships. Research on the potential antecedents of self-discrepant time allocation (i.e., the mismatch between ideal and actual time allocation) can enhance the understanding of how men and women faculty spend their time. To test hypotheses, time diary data was collected from faculty at a university in the southeastern U.S. Focal antecedent variables were collected in the first measurement wave. The second measurement wave, approximately one year later, assessed both ideal time allocation and actual time allocation. Although perceived organizational support and procedural justice did not predict research, service, and teaching self-discrepant time allocations, during the workweek and weekend, there were statistically significant findings when examining men and women’s research, service, and teaching during the workweek and weekend. The current study offers insight on academics’ time allocation and directions for future research, including improved measurement when categorizing daily activities. Overall, understanding discrepancies between ideal time allocation and actual time allocation in research, teaching, and service between men and women faculty can potentially improve organizational climate and retention in academia

Attitudes about Women, Sexuality, and Abortion

This document is a partial submission to Huskie Commons of the required SEF Final Report, submitted in partial fulfillment of the SEF Program of Northern Illinois University Spring 2017 Grant.Abortion currently and throughout history, has been a wide-spread, controversial topic, though one in three women will obtain abortion services by the time they are 45 (Guttmacher Institute, 2014). Even after the U.S. Supreme Court affirmed a woman’s right to undergo an abortion in the famous Roe vs. Wade (1973) case, state-wide laws and restrictions continue to be placed on abortion practices (Begun & Walls, 2014). Many Americans support and/or oppose the act of having an abortion based on their personal beliefs and attitudes on how women should conduct themselves in different situations, and Wolf (1991) points out that advocates on both sides of the issue respect human life, though in different ways. According to Livingston (2007), several factors relate to abortion attitudes, including religion, gender role attitudes, and political affiliation. However, less is known about what psychological constructs may be involved in how abortion attitudes are formed. Begun and Walls (2014) explored the relationship between abortion attitudes and sexism and found that individuals who reported a greater level of anti-abortion attitudes also reported greater levels of two kinds of sexism: benevolent sexism, which casts women as pure, but fragile creatures in need of men’s protection; and hostile sexism, which casts women as manipulative harridans who are out to denigrate men. While this work is a start at examining what attitudinal factors may influence individuals’ abortion attitudes, more research is needed. The current project seeks to further investigate how these attitudes are constructed, and what role gender plays in their formation.NIU's Student Engagement Fun

A New Comprehensive and Practical Taxonomy of Demands Healthcare Professionals Experience: The Development Process and Testing Using Machine Learning

Given the complex (Ratnapalan & Lang, 2020) and high stress environment of healthcare organizations (Freshwater & Cahill, 2010), a better understanding of the conditions in which healthcare professionals work is important. Although previous research has resulted in somewhat limited categories of the demands on healthcare professionals (Borteyrou et al., 2014; Shanafelt et al., 2020), a comprehensive taxonomy that covers the breadth and depth of demands is lacking. Using longitudinal data collected over 28 measurement waves spanning two years during the COVID-19 pandemic, the present studies outline the development of a taxonomy based on an in-depth literature review of related workplace models and taxonomies (Britannica, n.d.; Du Toit et al., 2003; Liu et al., 2018; Shirom & Melamed, 2006; Shoss, 2017; World Health Organization, 2021), 22,500+ qualitative comments from emergency medicine clinicians, and judgments and experiences of subject matter experts. An abductive approach was used to develop a tri-level taxonomy, the Taxonomy of Demands Healthcare Professionals Experience (TDHPE), that categorizes healthcare professionals’ concerns regarding medication and supply shortages, communication, economic stress, workload, organizational support, psychological distress, and society, to name a few. Although the TDHPE was robustly and rigorously developed, machine learning algorithms, specifically IBM Watson’s Natural Language Understanding (NLU) service, were used to replicate human coding. IBM Watson’s NLU service demonstrated a high level of accuracy replicating comments in the superordinate (i.e., large) and basic (i.e., medium) levels of the TDHPE but demonstrated a low level of accuracy replicating comments in the subordinate (i.e., small) level. As predicted, IBM Watson’s NLU service classified comments into the broader categories more accurately than the specific categories of the TDHPE. However, categories in the TDHPE that were generated by IBM Watson’s NLU service were not consistent across healthcare professionals’ roles. Ultimately, the purpose of these strategies is to help those who help other people; to help improve the workplace for our healthcare heroes