Perceptions and Practices for Evaluating Faculty Workload by Pharmacy Education Administration/Leadership
Lisa Lebovitz, Sharon K. Park, Surajit Dey, Elizabeth A. Sheaffer, Cynthia K. Kirkwood, David J. Weldon, Melissa S. Medina, Ashley N. Castleberry, Kelly C. Lee, Omar Attarabeen, Margarita V. DiVall
Abstract
OBJECTIVE: To assess how department chairs/administrators define, measure, and evaluate faculty workload to better understand practices within the Academy. METHODS: An 18-item survey was distributed to department chairs/administrators via American Association of Colleges of Pharmacy Connect. Participants identified if they are a primary decision maker for faculty workload, whether their program has a workload policy, how workload is calculated, and how faculty satisfaction with workload equity is measured. RESULTS: Of 71 participants initiating the survey, data from 64 participants from 52 colleges/schools were eligible for analysis. Leaders of practice departments reported that their faculty spend an average of 38% of their time on teaching (compared to 46% for non-practice departments), 13% on research (vs 37%), 12% on service (vs 16%), and 36% on clinical practice (vs 0%). Most survey participants (n = 57, 89%) are at schools/colleges with a tenure system, and about 24 participants reported that faculty workload metrics differ across departments/divisions. Teaching assignments and service are reportedly negotiable between faculty and supervisors, and workload expectations are widely variable. The majority indicated they do not analyze faculty satisfaction with workload fairness (n = 35) and faculty do not provide evaluative feedback on how supervisors assign faculty workload (n = 34). Of 6 priorities considered when determining workload, 'support college/school strategies and priorities' ranked highest (1.92) and 'trust between the chair and faculty' ranked lowest (4.87). CONCLUSION: Overall, only half of the participants reported having a clear, written process of quantifying faculty workload. The use of workload metrics may be needed for evidence-based decision-making for personnel management and resource allocation.