Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Student t Distribution01:31

Student t Distribution

14.2K
The population standard deviation is rarely known in many day-to-day examples of statistics. When the sample sizes are large, it is easy to estimate the population standard deviation using a confidence interval, which provides results close enough to the original value. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
The Student t distribution was developed by William S. Goset (1876–1937) of the...
14.2K
Microsoft Excel: Student's t-Test01:25

Microsoft Excel: Student's t-Test

1.7K
Student's t-test in Microsoft Excel is a statistical method used to compare the means of two groups to determine if they are significantly different from each other. It's commonly used to evaluate hypotheses, such as testing whether a treatment has an effect compared to a control group. Excel provides built-in functions to perform t-tests, making it accessible for users needing to conduct basic statistical analysis.
To conduct a t-test in Excel, use the T.TEST function or the "Data...
1.7K
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

6.1K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
6.1K
Self-Evaluation: Self-Enhancement and Self-Verification03:00

Self-Evaluation: Self-Enhancement and Self-Verification

5.8K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
5.8K
Bioavailability Enhancement: Drug Solubility Enhancement01:16

Bioavailability Enhancement: Drug Solubility Enhancement

273
Body:Bioavailability is a critical factor in determining a drug's effectiveness. It refers to the proportion of a drug that enters the circulation when introduced into the body and is, as a result, able to have an active effect. Enhancing bioavailability is essential for drugs with poor solubility, as it can significantly impact their therapeutic efficacy. Various methods are employed to increase the solubility of drugs, thereby enhancing their bioavailability.Micronization and nanonization are...
273
Bioavailability Enhancement: Drug Permeability Enhancement01:27

Bioavailability Enhancement: Drug Permeability Enhancement

213
Body:After oral administration, poor permeability often limits the rate at which drugs are absorbed through the intestinal epithelium. Enhancing drug permeability is crucial for effective therapy, and several strategies have been developed to overcome this challenge.One effective strategy involves the use of lipid-based formulations. These formulations enhance dissolution and solubility, targeting physiological mechanisms to increase drug absorption. This includes stimulating bile salt...
213

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaluating case-based learning approaches: Effects on student performance and perceived APPE readiness.

Currents in pharmacy teaching & learning·2026
Same author

Real-World Cardiovascular Outcomes of GLP-1 Receptor Agonists in Women With Type 2 Diabetes and Breast Cancer.

Pharmacotherapy·2026
Same author

Evolution of a capstone exam for third-year doctor of pharmacy students.

Currents in pharmacy teaching & learning·2023
Same author

Assessing pharmacy students' preferences with implementing electronic medical records into the pharmacy curriculum.

BMJ simulation & technology enhanced learning·2022
Same author

Using high-fidelity simulation to teach fundamental principles of cardiac catheterisation to pharmacy students.

BMJ simulation & technology enhanced learning·2022
Same author

A mobile health technology workshop to evaluate available technologies and their potential use in pharmacy practice.

BMJ simulation & technology enhanced learning·2022

Related Experiment Video

Updated: Feb 11, 2026

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
11:18

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Published on: January 22, 2011

16.5K

Enhancing Student Knowledge Through a Comprehensive Oncology Simulation.

Erini S Serag-Bolos1, Melissa Chudow1, Janelle Perkins1

  • 1College of Pharmacy, University of South Florida, Tampa, Florida.

American Journal of Pharmaceutical Education
|April 26, 2018
PubMed
Summary
This summary is machine-generated.

A comprehensive oncology simulation significantly improved pharmacy students' knowledge and perceptions of oncology pharmacy practice, enhancing their confidence in handling complex patient cases and understanding pharmacist roles.

Keywords:
counselinglaboratoryoncologysimulationsterile compounding

More Related Videos

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.7K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.6K

Related Experiment Videos

Last Updated: Feb 11, 2026

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
11:18

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Published on: January 22, 2011

16.5K
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.7K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.6K

Area of Science:

  • Pharmacy Education
  • Oncology Pharmacy Practice
  • Simulation-Based Learning

Background:

  • Oncology pharmacy requires specialized knowledge and skills.
  • Pharmacy students need practical experience in oncology settings.
  • Simulation offers a safe environment to develop these competencies.

Purpose of the Study:

  • To assess the effect of an oncology simulation on pharmacy students' knowledge.
  • To evaluate changes in students' perceptions of oncology pharmacists' roles.
  • To measure improvements in students' confidence in oncology practice.

Main Methods:

  • Third-year pharmacy students participated in an ovarian cancer case-based simulation.
  • Activities included order verification, patient counseling, and aseptic technique.
  • Pre- and post-simulation assessments measured knowledge and perceptions.

Main Results:

  • A statistically significant increase in oncology knowledge was observed.
  • Students' perceptions of pharmacist roles improved from 3.8 to 4.5.
  • Self-confidence in preparing regimens increased from 3.2 to 4.2.

Conclusions:

  • The oncology simulation enhanced students' therapeutic knowledge.
  • The simulation improved students' understanding of oncology pharmacists' contributions.
  • This educational approach boosts student readiness for oncology practice.