Jour 405V: Data Journalism

Assignment #1: VA Fayetteville Data – Wait Times

_{Lemke Digital Media Lab Course, Fall 2017}

_{School of Journalism and Strategic Media}

For this assignment, you will analyze a spreadsheet of wait times for Veteran’s Administration hospitals in Fayetteville and Little Rock and write a short story based on your analysis.

The Veterans Administration suffered a major scandal when it was revealed that VA employees in many offices lied about how quickly they saw patients. This dataset will allow us to see trend in wait times at the local hospitals.

Examine the data and determine the overall trend for the Fayetteville and Little Rock hospitals. What percentage of the appointments were within 30 days? What percentage were longer? What other trends did you discover?

Source materials:

Data: va_wait_completed: VA station completed wait times information

Documentation VA Wait Time Goals

https://www.va.gov/HEALTH/docs/VA_Report_Section101-PL_113-146-Final.pdf

Three articles of background research on VA wait times you will gather on LexisNexis Academic, not Google. This assignment forces you to use LexisNexis Academic, a premier news database available through the university’s library.

Here’s a background report on the wait times controversy to get you started.

https://www.va.gov/health/docs/VAAccessAuditFindingsReport.pdf

The assignment is due Sunday, Sept. 17, 11:59 p.m. on Blackboard.

You are to post an article on Microsoft Word (no PDFs or other file formats) and upload your completed spreadsheet.

Rubric:

Data: 50%: Pivot table. Percent of whole

Writing: 30%: A clear lede supported by data and research

Research: 20%: Relevant articles cited correctly

Background:

LexisNexis Academic

http://libraries.uark.edu/eresources/help.asp?TitleCode=UNIV

VA Data

va_wait_completed

1) Interview Data

2) Sort and Filter

3) Data Dictionary Material:

va_wait_completed

station_id: VA station identification code
name: VA station name
visn_id: VA Veterans Integration Service Number
ref_date: Reference Date
prioritized: Stations that were designated to receive additional funding
○ 1: prioritized, 0 otherwise
tot_appts: Total appointments scheduled
perc_over_30: Percentage of appointments scheduled over 30 days
appts_under_7: Appointments scheduled between 0-7 days
appts_under_14: Appointments scheduled between 8-14 days
appts_under_30: Appointments scheduled between 15-30 days
appts_under_60: Appointments scheduled between 31-60 days
appts_under_90: Appointments scheduled between 61-90 days
appts_under_120: Appointments scheduled between 91-120 days
appts_over_120: Appointments scheduled over 120 days
pc_avg_wait: Primary Care Average Wait Time (in days)
sc_avg_wait: Speciality Care Average Wait Time (in days)
mh_avg_wait: Mental Health Average Wait Time (in days)
state: Facility state
address: Facility postal address
postal_code: Facility postal code
latitude: Facility latitude coordinate
longitude: Facility longitude coordinate
phone: Facility phone number
fax: Facility fax number
url: Facility website url