Social Media in Medical Education: Effective Communication Mediums

Kendrick A. Davis, MA, PhD Michael N. Nduati, MD, MBA, MPH

ABSTRACT A journal club selective was introduced to medical students to improve education regarding healthcare management and policy. Technology was employed to impact curriculum and assessment efforts. Blogging and tweeting were initially used as vehicles for students to actively shape curriculum throughout the learning process, and mediums for embedded formative assessment. Facebook became the first major student generated impact from this undertaking.

BACKGROUND • The selection of social mediums was initially generated by faculty.

• Not only was the social medium intended to be utilized for students to better shape the course, but it was also employed to evaluate the effective use of embedded formative assessment (EFA).

OBJECTIVES

• Increase student interest in:

• Gaining knowledge about the Affordable Care Act (ACA), national health policy, Medicare/Medicaid, etc.

• Becoming familiar with the business/operational side of medicine

• Pursuing additional health related education (e.g., MPH, MBA, MPP)

• Pursuing a leadership role in a healthcare delivery system, and/or impacting health care policy

• For students to:

• Shape the course curriculum via blogging and tweeting

• Identify, collect, and evaluate learning content via blogging and tweeting

• Assessment Objectives:

• Use blogging, and tweeting as embedded formative assessment components of the course, including during live instruction

• Integrate the “seven principles of good feedback practice2” as a framework for evaluating the effectiveness of each of the aforementioned assessment components, both individually and collectively

• Analyze data collected from each component of embedded formative assessment via descriptive and inferential statistical techniques

• Comparative analyses of outcome related assessments and embedded formative assessments

METHODS • Data was collected from students in-class using a 10-item Likert-type paper and

pencil survey

• To assess mean differences between incoming and outgoing interest and knowledge T-test were run on each item

• Effect sizes were calculated for each item to account for lack of statistical significance resulting from a small sample

• The following table provides a display of pre- and post-instruction mean scores, and effect size calculations indicating the magnitude of mean differences between student’s pre- and post-instruction interest and knowledge.

RESULTS

• The combined average weekly use of blogging and tweeting was (f = 2) per student. While the average weekly use of Facebook was (f = 4.5) per student. Posting articles and article responses doubled!

RESULTS CONTINUED

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Tweeting Blogging Facebooking

Axis

Title

Chart Title

CONCLUSIONS • Facebook was a more student-friendly medium for shaping curriculum and

employing assessment efforts!

• While pre- and post-instruction assessments indicate overall increases in interest and competence, only the increases on competence measures were statistically and practically significant!

• Present results from analyses are telling, however the bulk of the data was obtained via student self-report. Our next phase of research will consist in applying an EFA Measurement Battery!

• The assessment objectives set forth in the present research undertaking were approached only preliminarily. The bulk of the planned assessment activities led to greater planning, which resulted in the advent of the embedded formative assessment (EFA) measurement battery!

Survey Item Ȳ Time 1 Pre-Instruction

Ȳ Time 2 Post-Instruction

Effect Size Cohen’s d

M.A. Interest Ȳ1 = 3.89 Ȳ2 = 4.55 1

Leadership Interest Ȳ1 = 4.11 Ȳ2 = 4.64 1.46

Policy Interest Ȳ1 = 3.50 Ȳ2 = 3.73 .267

Business Med. Knowledge Ȳ1 = 2.06 Ȳ2 = 2.91 1.27

Operational Med. Knowledge

Ȳ1 = 2.17 Ȳ2 = 3.09 1.02

Affordable Care Act (ACA) Knowledge

Ȳ1 = 2.67 Ȳ2 = 3.64 1.51

Nation’s Health Policies Knowledge

Ȳ1 = 2.61 Ȳ2 = 3.64 1.6

Medi-Cal/Caid/Care Knowledge

Ȳ1 = 2.56 Ȳ2 = 3.27 .95

Delivery models Knowledge (HMO, PPO, etc.)

Ȳ1 = 2.78 Ȳ2 = 3.09 .46

Physician Shortage Knowledge

Ȳ1 = 3.56 Ȳ2 = 4.27 1.61

Source: https://www.mededportal.org/icollaborative/resource/706