Data abstraction: designing the tools, recruiting and training the data abstractors.
Data abstraction is very exacting work requiring that data abstractors have both knowledge and experience of phenomena being studied. Quality of the data and efficiency of the data abstractors are dependent upon the content and design of the data abstraction tools. Taking the time to recruit and train qualified data abstractors and to develop effective data abstraction tools will result in data that not only answers the research question, but also withstands the most rigorous critique.
Simmons
Barbara
B
James A. Haley VA Hospital, Tampa, Florida, USA.
Bennett
Frank
F
Nelson
Audrey
A
Luther
Stephen L
SL
D000043:Abstracting and Indexing / Q000379:methods*; D003625:Data Collection / Q000379:methods*; D019317:Evidence-Based Medicine; D019983:Guideline Adherence / Q000706:statistics & numerical data*; D006787:Hospitals, Veterans; D006801:Humans; D015337:Multicenter Studies as Topic; D024802:Nurse's Role; D015588:Observer Variation; D010043:Outcome and Process Assessment, Health Care; D017410:Practice Guidelines as Topic; D012107:Research Design / Q000592:standards*; D012108:Research Personnel / Q000193:education; D013119:Spinal Cord Injuries / Q000628:therapy*; D014481:United States; D016270:United States Agency for Healthcare Research and Quality; D014493:United States Department of Veterans Affairs