Related Experiment Video
Updated: Jun 9, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Published on: August 8, 2019
A Survey Analysis of Motivations and Perceived Consequences of Work Hours Among Pharmacy Students
Sara Stallworth1, Madison Ricco2, Krisy-Ann Thornby3
1Department of Pharmacy Practice & Science, University of Kentucky College of Pharmacy, South Limestone, Lexington, KY, USA.
Objective:
To determine pharmacy students' weekly work hours during Doctor of Pharmacy (PharmD) semesters, the primary reasons for working or not working, and how work hours impact their pharmacy education experience.
Methods:
A multicenter cross-sectional survey was conducted among first-year (P1) to fourth-year (P4) pharmacy students enrolled in PharmD programs at 3 colleges of pharmacy between January and February 2024. The 22-item anonymous survey queried student characteristics, current and ideal paid work hours, primary reasons for working, and perceived consequences of work hours on pharmacy education.
Results:
A total of 622 pharmacy students participated in this survey (61% response rate). Community pharmacies (39%, n = 242) and inpatient hospital pharmacies (29%, n = 179) were the most common work settings for participants. Mean reported student work hours per week were statistically higher than ideal hours (12.3 vs 10.8). Most students were comfortable with their weekly work hours (63%, n = 307). Academic commitment was the main reason for students working fewer than ideal hours (76%, n = 65) while financial necessity was the primary reason for students working more than their ideal hours (86%, n = 82). Reduced study time (90%, n = 84), increased stress and fatigue (72%, n = 67), and limited extracurricular involvement (59%, n = 55) were consequences of working beyond ideal work hours.
Conclusion:
Most students are comfortable with their average 12-h/week work hours. Financial necessity is a primary reason for students working more than their ideal hours, and excess work hours may contribute to reduced student engagement in pharmacy education. Faculty can use this information in their discussions with students balancing work and academic commitments.
More Related Videos
05:40The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans
Published on: April 28, 2022
07:32Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity
Published on: February 10, 2016
Related Concept Videos
Chronopharmacokinetics: Circadian Rhythms and Influence on Drug Response
The time of drug administration is an important factor to consider, as it can influence the toxic dose of a drug. For example, a study conducted by Prins et al. in 1997 examined the effects of the timing of...
Analysis of Population Pharmacokinetic Data
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
Factors Affecting Drug Biotransformation: Biological
Species differences: Variations in enzyme systems across species can cause disparities in drug metabolism. For instance, humans may metabolize certain drugs faster than rodents, altering therapeutic effects.
Strain differences: Genetic variations within a species can result in differing enzyme activity, impacting drug response and toxicity. For example, some mouse strains may...
Pharmacodynamics: Overview and Principles
Most drugs' effects result from their interactions with drug receptors or targets within the body. These interactions trigger specific responses at the cellular or systemic level. Drug receptors can be found on the surfaces of cells or...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...