Authors: Mary A. Lavin, Mary M. Krieger, Geralyn A. Meyer, Mark A. Spasser, Tome Cvitan, Cordie G. Reese, Judith H. Carlson, Anne G. Perry, and Patricia McNary.
Title: Development and evaluation of evidence-based nursing (EBN) filters and related databases
Journal: Journal of the Medical Library Association
Year: 2005 Volume: 93
Issue: 1
Pages: 104-115
Purpose and/or Problem Statement:
Following the notion that evidence-based nursing (EBN) differs materially from evidence-based medicine (EBM), the authors hypothesize that EBN should have its own literature research criteria and methods. Thus, the main objective of this study is clear and straightforward: to refine EBN search and to develop the appropriate search filters.
Research Method/Design:
The research was based on several consecutive steps, performed by experts in the fields of nursing, libraries and information management. Each step was based on the outcomes of the previous steps and adds a new layer towards achieving the goal of better search results for EBN in the relevant search engines. The researchers chosen the topic of sleep as an example for classical EBN search, as it relates to almost all areas of nursing and patients’ groups.
By analyzing the topic, the researchers were able to construct an EBN matrix, which may aid informatics specialists to highlight the aspects that are more relevant to nurses.
Finally, the matrix was implemented in the PubMed search engine, and tested for the sensitivity and specificity of the filter in terms of primary data and patients’ outcomes of the sources found in PubMed. In addition, the filters were tested to their compatibility with other research topics. This careful process produced not only insights about the requirements for EBN search, but also a useful technical tool, which is ready to be installed in PubMed.
Data Analysis/Results:
The article presents the results of each of the steps (e.g. the EBN matrixes and screen captions from PubMed) and the results of the sensitivity and specificity tests in a clear and highly visible manner.
The main test results were:
- Sensitivity and specificity for the combined nursing diagnosis and primary data filter were 64% and 99%, respectively
- Sensitivity and specificity for the patient outcomes filter were 75% and 71%, respectively.
- Comparing to regular (i.e. non EBN-specific) PubMed filters, the EBN filter finds more review articles than other types of sources. Moreover, nursing diagnoses were not registered as so, and thus when searching for nursing diagnoses, regular filters can be as useful as the EBN filter.
Clinical Implications:
Nursing research differs from other kinds of researchers in many ways. Hence, nurses should prefer using the EBN filter when conducting PubMed search, as the data presented is more accurate to the nursing interest. By performing search with the filter, EBN and nursing studies should be performed more accurately and efficiently.
Recommendations for future research:
The authors do not set clear recommendations for future research. However, they admit that there is a need for better compatibility of nursing search in PubMed, and argue in favor of a filter that can separate nursing diagnoses from other types of diagnoses in the search parameters.
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