The changing landscape of research metricsStephen Curry
Presentation given by Prof Stephen Curry at the Gender Summit 7 (Europe - http://www.gender-summit.com/gs7-about). An overview of the use of performance metrics (in particular the finding of the 2014-15 HEFCE independent review of the role of metrics in research assessment - http://www.hefce.ac.uk/rsrch/metrics/)
The world of health care is full of policy interventions: a state expands eligibility rules for its Medicaid program, a medical society changes its recommendations for screening frequency, a hospital implements a new care coordination program. After a policy change, we often want to know, “Did it work?” This is a causal question; we want to know whether the policy CAUSED outcomes to change. One popular way of estimating causal effects of policy interventions is a difference-in-differences study. In this controlled pre-post design, we measure the change in outcomes of people who are exposed to the new policy, comparing average outcomes before and after the policy is implemented. We contrast that change to the change over the same time period in people who were not exposed to the new policy. The differential change in the treated group’s outcomes, compared to the change in the comparison group’s outcomes, may be interpreted as the causal effect of the policy. To do so, we must assume that the comparison group’s outcome change is a good proxy for the treated group’s (counterfactual) outcome change in the absence of the policy. This conceptual simplicity and wide applicability in policy settings makes difference-in-differences an appealing study design. However, the apparent simplicity belies a thicket of conceptual, causal, and statistical complexity. In this talk, I will introduce the fundamentals of difference-in-differences studies and discuss recent innovations including key assumptions and ways to assess their plausibility, estimation, inference, and robustness checks.
Cando se compara aos seres vivos con algunhas máquinas feitas polo ser humano, a miúdo recórrese a aquelas que teñen motores que utilizan combustible.
Igual que un vehículo de motor, un ser vivo toma substancias do medio, obtén enerxía a partir delas, utilízaas para funcionar e expulsa os refugallos que xera este proceso.
The changing landscape of research metricsStephen Curry
Presentation given by Prof Stephen Curry at the Gender Summit 7 (Europe - http://www.gender-summit.com/gs7-about). An overview of the use of performance metrics (in particular the finding of the 2014-15 HEFCE independent review of the role of metrics in research assessment - http://www.hefce.ac.uk/rsrch/metrics/)
The world of health care is full of policy interventions: a state expands eligibility rules for its Medicaid program, a medical society changes its recommendations for screening frequency, a hospital implements a new care coordination program. After a policy change, we often want to know, “Did it work?” This is a causal question; we want to know whether the policy CAUSED outcomes to change. One popular way of estimating causal effects of policy interventions is a difference-in-differences study. In this controlled pre-post design, we measure the change in outcomes of people who are exposed to the new policy, comparing average outcomes before and after the policy is implemented. We contrast that change to the change over the same time period in people who were not exposed to the new policy. The differential change in the treated group’s outcomes, compared to the change in the comparison group’s outcomes, may be interpreted as the causal effect of the policy. To do so, we must assume that the comparison group’s outcome change is a good proxy for the treated group’s (counterfactual) outcome change in the absence of the policy. This conceptual simplicity and wide applicability in policy settings makes difference-in-differences an appealing study design. However, the apparent simplicity belies a thicket of conceptual, causal, and statistical complexity. In this talk, I will introduce the fundamentals of difference-in-differences studies and discuss recent innovations including key assumptions and ways to assess their plausibility, estimation, inference, and robustness checks.
Cando se compara aos seres vivos con algunhas máquinas feitas polo ser humano, a miúdo recórrese a aquelas que teñen motores que utilizan combustible.
Igual que un vehículo de motor, un ser vivo toma substancias do medio, obtén enerxía a partir delas, utilízaas para funcionar e expulsa os refugallos que xera este proceso.
1. Amanda Ashdown
Human Factors Graduate Research Assistant at Old Dominion University
814 Liberal Arts ct.
Virginia Beach, VA
23462
757-831-6177
aashd001@odu.edu
ajashdown.com
Currently seeking a full-time HumanFactors position upon graduation from Old Dominion University
E D U C A T I O N D O C T O R AT E O F P H I L O SO P H Y : Human Factors Psychology
Old Dominion University (Norfolk, Virginia)
Expected Graduation: May 2018
Research focus in Human Factors
M A S T E R O F S C I EN C E : Experimental Psychology
Old Dominion University (Norfolk, Virginia)
Graduated: May 2015
Research focus in Human Factors
B A C H E L OR O F S C I E N C E : Psychology
Old Dominion University (Norfolk, Virginia)
Graduated: May 2012
Human Services minor
R E S E A RC H
E X P E RI EN C E
M A T E R N AL - FET A L H E A RT R A T E M O N I TO RI N G
- Evaluated clinicians’ ability to judge and interpret the signals and variability
imbedded in simulated fetal heart rate tracings
- Worked with medical doctors to develop different types of visual cues and
asses their benefits in facilitating the interpretation of fetal heart rate tracings
using a maternal fetal heart rate simulator
- Suggested using the MFHR simulator and visual cues to train clinicians in
assessing FHR tracings
I N T ERP E RSO N AL A N D I N T ERP R O FESS I O N AL
T R A I N I N G
- Evaluated voice recognition programming and feedback scoring
- Provided recommendations for future interface design
U L T R AS ON O G RA P H Y
- Worked with medical doctors to develop a rating scale to assess quality of
hepatorenal and PLAX images
- Future directions include comparing novices and experts using this scale and
developing even more objective rating scales
NASA RESEARCH INTERN (SUMMER 2014)
- I was involved in some new work to find ways to help pilots be more aware
during flight of what the automation is doing, or may do in the near future.
(For example, is there a good way to inform them early on that the
automation is approaching its limits of authority and he/she may need to re-
take control, or change the mode of automation?)
- A 2nd related research topic was Integrated Alerting to reduce complexity.
2. T E C H I N I C AL
R E P O RT S
Prytz, E., Anderson-Montoya, B., Kennedy, B., Montano, M., Ashdown, A.,
Warvel, L., & Scerbo, M. (2012). Virtual I.V. Self-Directed Learning
System. In Parodi, A. et al. (Eds.) LIVES Lab : Process Development for a
Conceptual Framework Driven, Process and Product Analysis Laboratory
for Medical & Healthcare Simulators and Simulations, Technical report,
Virginia Modeling, Simulation, Analysis, and Simulation Center.
Ashdown, A., Young, S. (2014). Airplane System Awareness. Unpublished
technical report, NASA Langley, Hampton, VA.
JOURNAL
PUBLICATIONS
Ashdown, A., Scerbo, M. W., Belfore II, L. A., Davis, S. S., & Abuhamad, A. Z.
(2016). Categorizing Fetal Heart Rate Variability with and without Visual
Aids. American Journal of Perinatology Reports, 6(04).
P R O C EE DI N GS
A R T I C L ES
Ashdown, A., Scerbo, M. W., Anderson, B. L., Belfore II, L. A., & Abuhamad, A. Z
(2014). User Placement of a Visual Aid for Detecting Critical Signals in
Fetal-Heart Rate Tracings: A Yoke-Control Study. Proceedings of the
Human Factors and Ergonomics Society Annual Meeting.
Ashdown, A., Scerbo, M. W., Belfore II, L. A., Abuhamad, A. Z, & Davis, S. S.
(2015). Categorizing Fetal Heart Rate Variability with and without Visual
Aids. Proceedings of the Human Factors and Ergonomics Society Annual
Meeting.
Zybak, S., Ashdown, A., & Scerbo, M. W. (2016). System reliability, trust, and
complacency in fetal heart rate monitoring. Proceedings of the Human
Factors and Ergonomics Society Annual Meeting.
P R O C EE DI N GS
A B S T R A C T S
Amanda Ashdown, MS, Samantha Zybak, BS, Mark W. Scerbo, PhD, Donald
V. Byars, MD, Felicia Toreno, PhD, Barry J. Knapp, MD. (2016). A
Comparison of Standardized Patient and Sonography Student Ratings
of Ultrasound Images. 17th International Meeting on Simulation in
Healthcare (IMSH 2017).
C E R T I FI C AT ES -
- Human Factors Modeling and Simulation Certificate (Old Dominion
University)
- Human Participants Protection Education for Research Teams, sponsored by
National Institutes of Health (NIH)
- Research Ethics Basic Course Certification, sponsored by Collaborative
Institutional Training Initiative (CITI)
- Graduate Teaching Assistant Instructor (GTAI)
P R O F FESSI ON AL
A F F I L I AT I ON S
-
- Human Factors and Ergonomics Society (HFES) Student Affiliate
- ODU HFES Student Chapter: 2016 Treasurer, 2015 President, 2014 Vice
President
- Society for Simulation in Healthcare
3. S K I L L S - SPSS
- Qualtrics
- Basic HTML, JavaScript, PHP, CSS Coding
- SuperLab