Dataset Information

Overlaps of multiple database retrieval and citation tracking in dementia care research: a methodological study.

ABSTRACT:

Objective

We aimed to determine overlaps and optimal combination of multiple database retrieval and citation tracking for evidence synthesis, based on a previously conducted scoping review on facilitators and barriers to implementing nurse-led interventions in dementia care.

Methods

In our 2019 scoping review, we performed a comprehensive literature search in eight databases (CENTRAL, CINAHL, Embase, Emcare, MEDLINE, Ovid Nursing Database, PsycINFO, and Web of Science Core Collection) and used citation tracking. We retrospectively analyzed the coverage and overlap of 10,527 retrieved studies published between 2015 and 2019. To analyze database overlap, we used cross tables and multiple correspondence analysis (MCA).

Results

Of the retrieved studies, 6,944 were duplicates and 3,583 were unique references. Using our search strategies, considerable overlaps can be found in some databases, such as between MEDLINE and Web of Science Core Collection or between CINAHL, Emcare, and PsycINFO. Searching MEDLINE, CINAHL, and Web of Science Core Collection and using citation tracking were necessary to retrieve all included studies of our scoping review.

Conclusions

Our results can contribute to enhancing future search practice related to database selection in dementia care research. However, due to limited generalizability, researchers and librarians should carefully choose databases based on the research question. More research on optimal database retrieval in dementia care research is required for the development of methodological standards.

SUBMITTER: Hirt J

PROVIDER: S-EPMC8270360 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPublication records and citation indices often are used to evaluate academic performance. For this reason, obtaining or computing them accurately is important. This can be difficult, largely due to a lack of complete knowledge of an individual's publication list and/or lack of time available to manually obtain or construct the publication-citation record. While online publication search engines have somewhat addressed these problems, using raw search results can yield inaccurate estimates of publication-citation records and citation indices.MethodologyIn this paper, we present a new, automated method that produces estimates of an individual's publication-citation record from an individual's name and a set of domain-specific vocabulary that may occur in the individual's publication titles. Because this vocabulary can be harvested directly from a research web page or online (partial) publication list, our method delivers an easy way to obtain estimates of a publication-citation record and the relevant citation indices. Our method works by applying a series of stringent name and content filters to the raw publication search results returned by an online publication search engine. In this paper, our method is run using Google Scholar, but the underlying filters can be easily applied to any existing publication search engine. When compared against a manually constructed data set of individuals and their publication-citation records, our method provides significant improvements over raw search results. The estimated publication-citation records returned by our method have an average sensitivity of 98% and specificity of 72% (in contrast to raw search result specificity of less than 10%). When citation indices are computed using these records, the estimated indices are within of the true value 10%, compared to raw search results which have overestimates of, on average, 75%.ConclusionsThese results confirm that our method provides significantly improved estimates over raw search results, and these can either be used directly for large-scale (departmental or university) analysis or further refined manually to quickly give accurate publication-citation records.