Modeling gender differences in the job promotion process: Replication and extension of Martell et al. (1996).

Differences in employee evaluations due to gender bias may be small in any given rating cycle, but they may accumulate to produce large disparities in the number of women and men promoted to the top of an organization. A highly cited simulation by Martell et al. (1996) demonstrates this cumulative advantage process in a multilevel organization. We replicated this simulation to uncover important details about its operating assumptions, and we extended the simulation to examine a range of variables that may impact the cumulative effects of gender bias. The replication revealed that the male cumulative advantage in the Martell et al. simulation requires (a) decades of typical promotion cycles to produce, (b) constant mean differences in the performance ratings of women and men but equal within-group variances, and (c) attrition that occurs randomly at a low and constant rate. Our extended simulation demonstrates that (a) cumulative effects of gender bias are higher when the attrition rate is lower, (b) gender biases are mitigated when attrition is more strongly associated with good or poor performance, and (c) the cumulative effects of mean gender differences in performance ratings can often be smaller than the cumulative effects of variance differences between gender subgroups. Results suggest that talent development and recognition of high performers might have a greater positive impact on female representation at top levels of a firm than programs aimed at reducing bias in employee evaluations. We encourage additional simulation work that further explores the dynamics of cumulative advantage in employment settings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

What can we learn from COVID-19?: examining the resilience of primary care teams.

Introduction: The COVID-19 pandemic continues to place an unprecedented strain on the US healthcare system, and primary care is no exception. Primary care services have shifted toward a team-based approach for delivering care in the last decade. COVID-19 placed extraordinary stress on primary care teams at the forefront of the pandemic response efforts. The current work applies the science of effective teams to examine the impact of COVID-19-a crisis or adverse event-on primary care team resilience. Methods: Little empirical research has been done testing the theory of team resilience during an extremely adverse crisis event in an applied team setting. Therefore, we conducted an archival study by using large-scale national data from the Veterans Health Administration to understand the characteristics and performance of 7,023 Patient Aligned Care Teams (PACTs) during COVID-19. Results: Our study found that primary care teams maintained performance in the presence of adversity, indicating possible team resilience. Further, team coordination positively predicted team performance (B = 0.53) regardless of the level of adversity a team was experiencing. Discussion: These findings in turn attest to the need to preserve team coordination in the presence of adversity. Results carry implications for creating opportunities for teams to learn and adjust to an adverse event to maintain performance and optimize team-member well-being. Teamwork can act as a protective factor against high levels of workload, burnout, and turnover, and should be studied further for its role in promoting team resilience.

An evidence-based, structured, expert approach to selecting essential indicators of primary care quality.

BACKGROUND: The purpose of this article is to illustrate the application of an evidence-based, structured performance measurement methodology to identify, prioritize, and (when appropriate) generate new measures of health care quality, using primary care as a case example. Primary health care is central to the health care system and health of the American public; thus, ensuring high quality is essential. Due to its complexity, ensuring high-quality primary care requires measurement frameworks that can assess the quality of the infrastructure, workforce configurations, and processes available. This paper describes the use of the Productivity Measurement and Enhancement System (ProMES) to compile a targeted set of such measures, prioritized according to their contribution and value to primary care. METHODS: We adapted ProMES to select and rank existing primary care measures according to value to the primary care clinic. Nine subject matter experts (SMEs) consisting of clinicians, hospital leaders and national policymakers participated in facilitated expert elicitation sessions to identify objectives of performance, corresponding measures, and priority rankings. RESULTS: The SMEs identified three fundamental objectives: access, patient-health care team partnerships, and technical quality. The SMEs also selected sixteen performance indicators from the 44 pre-vetted, currently existing measures from three different data sources for primary care. One indicator, Team 2-Day Post Discharge Contact Ratio, was selected as an indicator of both team partnerships and technical quality. Indicators were prioritized according to value using the contingency functions developed by the SMEs. CONCLUSION: Our article provides an actionable guide to applying ProMES, which can be adapted to the needs of various industries, including measure selection and modification from existing data sources, and proposing new measures. Future work should address both logistical considerations (e.g., data capture, common data/programming language) and lingering measurement challenges, such as operationalizating measures to be meaningful and interpretable across health care settings.

Impact of team configuration and team stability on primary care quality.

BACKGROUND: The science of effective teams is well documented; far less is known, however, about how specific team configurations may impact primary care team effectiveness. Further, teams experiencing frequent personnel changes (perhaps as a consequence of poor implementation) may have difficulty delivering effective, continuous, well-coordinated care. This study aims to examine the extent to which primary care clinics in the Veterans Health Administration have implemented team configurations consistent with recommendations based on the Patient-Centered Medical Home model and the extent to which adherence to said recommendations, team stability, and role stability impact healthcare quality. Specifically, we expect to find better clinical outcomes in teams that adhere to recommended team configurations, teams whose membership and configuration are more stable over time, and teams whose clinical manager role is more stable over time. METHODS/DESIGN: We will employ a combination of social network analysis and multilevel modeling to conduct a database review of variables extracted from the Veterans Health Administration's Team Assignments Report (TAR) (one of the largest, most diverse existing national samples of primary care teams (nteams > 7000)), as well as other employee and clinical data sources. To ensure the examination of appropriate clinical outcomes, we will enlist a team of subject matter experts to select a concise set of clear, prioritized primary care performance metrics. We will accomplish this using the Productivity Measurement and Enhancement System, an evidence-based methodology for developing and implementing performance measurement. DISCUSSION: We are unaware of other studies of healthcare teams that consider team size, composition, and configuration longitudinally or with sample sizes of this magnitude. Results from this study can inform primary care team implementation policy and practice in both private- and public-sector clinics, such that teams are configured optimally, with adequate staffing, and the right mix of roles within the team. TRIAL REGISTRATION: Not applicable-this study does not involve interventions on human participants.