Browsing by Subject "Bias"
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Item A bias correction method in meta-analysis of randomized clinical trials with no adjustments for zero-inflated outcomes(John Wiley & Sons, Inc., 2021-09-03) Zhou, Zhengyang; Xie, Minge; Huh, David; Mun, Eun-YoungMany clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero-inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study-level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta-analysis. The current study proposes a novel statistical approach, the Zero-inflation Bias Correction (ZIBC) method, that can account for the bias introduced when using the Poisson regression model, despite a high rate of inflated zeros in the outcome distribution of a randomized clinical trial. This correction method only requires summary information from individual studies to correct intervention effect estimates as if they were appropriately estimated using the zero-inflated Poisson regression model, thus it is attractive for meta-analysis when individual participant-level data are not available in some studies. Simulation studies and real data analyses showed that the ZIBC method performed well in correcting zero-inflation bias in most situations.Item Implementation of an unconscious bias course for the National Research Mentoring Network(BioMed Central Ltd., 2022-05-21) Javier, Damaris; Solis, Linda Grace; Paul, Mirabelle Fernandes; Thompson, Erika L.; Maynard, Grace; Latif, Zainab; Stinson, Katie; Ahmed, Toufeeq; Vishwanatha, Jamboor K.Purpose: Increased awareness and mitigation of one's unconscious bias is a critical strategy in diversifying the Science, Technology, Engineering, Mathematics, and Medicine (STEMM) disciplines and workforce. Greater management of unconscious bias can enhance diverse recruitment, persistence, retention, and engagement of trainees. The purpose of this study was to describe the implementation of an asynchronous course on unconscious bias for people in STEMM. Specifically, we explored who engaged with the course and reflections from participation. Method: A five-part, asynchronous Unconscious Bias Course was developed and was hosted on a national mentoring platform starting in July 2020. To examine course engagement, we assessed the demographics of course participants and completion. Participant responses to reflection questions after each module were also synthesized using qualitative methods. Results: Overall, 977 people registered for the course and 42% completed all modules. In the reflection responses, participants reflected on their unconscious biases in their lived experiences and how it relates to actions, judgements, external factors, stereotypes, and un-intentionality. Participants also reflected on microaggressions, their impact on the recipients and others, and the relationship between microaggressions and unconscious bias. Participants reported four key strategies used by allies against unconscious bias: immediately acting (83%), reflection (46%), improving the organizational culture (30%), and individual-level ally-ship (44%). Strategies for self-awareness included: reflection, pausing/breathing, and self-observation. Conclusion: The assessment of the Unconscious Bias Course implementation revealed the course reached a wide cross-section of people in STEMM and demonstrated that participants were able to reflect on the underpinnings of the course. This course, and its suite of offerings, support a nationwide effort to mitigate bias and prepare individuals to be culturally competent in a diverse society in order to foster a STEMM environment that caters to individuals' success and diversification of these fields.