Academic journal article American Journal of Pharmaceutical Education

A Novel Mathematical Model for Determining Faculty Workload

Academic journal article American Journal of Pharmaceutical Education

A Novel Mathematical Model for Determining Faculty Workload

Article excerpt


Workload is a key factor in faculty satisfaction and retention at US pharmacy schools. (1,2) Excessive workload was one of the top 2 reasons reported by faculty members for leaving an institution. (1) Faculty workload is an especially important consideration at schools of pharmacy that use a team-based learning (TBL) curriculum, because TBL has been shown to increase faculty workload. (3,4) There are only 2 published studies in which faculty workload was quantified using well-organized mathematical methods. (5,6) One of these publications was a workload-related survey of 12 pharmacy schools. (5) This survey did not include a comprehensive mathematical model for determining faculty workload, because only 40% of the respondents to the survey were unaware that a formula to measure workload existed at their institutions. (5) Desselle et al developed a more sophisticated model for faculty workload. (6) However, it was somewhat limited in format and scope in terms of representing an overall institutional mathematical workload model.

A faculty taskforce was formed in 2014 to more empirically quantify faculty workload at the California Northstate University College of Pharmacy. Using data gathered from 28 participating faculty members, a novel mathematical workload model was developed. (7) Specifically, this workload model is dynamic in nature (ie, uses minimum and maximum activity values) to ultimately create a better faculty evaluation system. (8)

This manuscript will describe the mathematical methods used to create the departmental and institutional workload models at California Northstate University College of Pharmacy. We believe that this type of model may offer a new template for other academic/pharmacy institutions that are interested in doing a faculty workload analysis.


The California Northstate University College of Pharmacy is located in Elk Grove, California. The College of Pharmacy consists of a Pharmaceutical and Biomedical Sciences Department and a Clinical Sciences Department. Our institution adopted a TBL-based curriculum in 2008. Nine faculty members from the Biomedical and Pharmaceutical Sciences Department and 21 faculty members from the Clinical Sciences Department were asked to participate in the analysis. This data-gathering phase of the study took place from March 2014 to July of 2014.

All participating faculty members were asked to list their activities in the areas of teaching, institutional service, scholarship and professional development. Faculty members were not typically prompted, or given a checklist. The Clinical Sciences Department also provided activities within the area of clinical service. Activities were directly listed as supplied by faculty members unless there was clear overlap in an activity as determined via conversations between the lead author (LRF) and faculty members. The individual activities designated by faculty members were assigned a timeframe, which was typically expressed as hours per week. In certain instances, clinical faculty members provided their information in hours per semester or hours per clinical experience. These totals were then converted to hours per week based on a 16-week semester or a 6-week clinical experience, respectively.

To create a mathematical workload model, faculty activities (hours per week) were first converted to activity scores using the interval scale shown in Table 1. This scoring system allowed a minimum score of 0 (for 0 hours for an activity) up to a maximum score of 5 (for 46 to 50 hours for an activity).

Using GraphPad Prism 6 (Graph Pad Software, Inc, La Jolla, CA), a mean value was calculated for each applicable faculty activity score. Subsequently, weighted means were determined by multiplying the mean activity score by the number of faculty members engaging in that activity. The 95% confidence limits [CL] for the weighted means were also determined as part of the workload analysis. …

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