PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 10276655

How to construct a subjective index.

F Alemi·D H Gustafson·M Johnson
View original source ↗Export metadata ↓

Abstract

We present a method of constructing quantitative indices, which is based on the subjective opinions of a panel of experts, and discuss how a Bayesian probability model and panel opinions can be used together to produce an index. Among the advantages of the method are its face validity and ease of construction. Research shows that when expert opinions are solicited according to certain guidelines, subjective methods may be as accurate as the more objective ones. Guidelines along with a brief report of a recent application are also discussed.

Explore related subjects

Abstracting and IndexingBayes TheoremHealth Status IndicatorsHealth SurveysHospitalsModels, TheoreticalProbabilityResearch DesignRiskJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

F Alemi, D H Gustafson, M Johnson. 1986. How to construct a subjective index.. https://doi.org/10.1177/016327878600900103

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

A comparative evaluation of full-text, concept-based, and context-sensitive search.

OBJECTIVES: Study comparatively (1) concept-based search, using documents pre-indexed by a conceptual hierarchy; (2) context-sensitive search, using structured, labeled documents; and (3) traditional full-text search. Hypotheses were: (1) more contexts lead to better retrieval accuracy; and (2) adding concept-based search to the other searches would improve upon their baseline performances. DESIGN: Use our Vaidurya architecture, for search and retrieval evaluation, of structured documents classified by a conceptual hierarchy, on a clinical guidelines test collection. MEASUREMENTS: Precision computed at different levels of recall to assess the contribution of the retrieval methods. Comparisons of precisions done with recall set at 0.5, using t-tests. RESULTS: Performance increased monotonically with the number of query context elements. Adding context-sensitive elements, mean improvement was 11.1% at recall 0.5. With three contexts, mean query precision was 42% +/- 17% (95% confidence interval [CI], 31% to 53%); with two contexts, 32% +/- 13% (95% CI, 27% to 38%); and one context, 20% +/- 9% (95% CI, 15% to 24%). Adding context-based queries to full-text queries monotonically improved precision beyond the 0.4 level of recall. Mean improvement was 4.5% at recall 0.5. Adding concept-based search to full-text search improved precision to 19.4% at recall 0.5. CONCLUSIONS: The study demonstrated usefulness of concept-based and context-sensitive queries for enhancing the precision of retrieval from a digital library of semi-structured clinical guideline documents. Concept-based searches outperformed free-text queries, especially when baseline precision was low. In general, the more ontological elements used in the query, the greater the resulting precision.

Abstracting and Indexing↗
PubMed2007

Improvements to the Red List Index.

The Red List Index uses information from the IUCN Red List to track trends in the projected overall extinction risk of sets of species. It has been widely recognised as an important component of the suite of indicators needed to measure progress towards the international target of significantly reducing the rate of biodiversity loss by 2010. However, further application of the RLI (to non-avian taxa in particular) has revealed some shortcomings in the original formula and approach: It performs inappropriately when a value of zero is reached; RLI values are affected by the frequency of assessments; and newly evaluated species may introduce bias. Here we propose a revision to the formula, and recommend how it should be applied in order to overcome these shortcomings. Two additional advantages of the revisions are that assessment errors are not propagated through time, and the overall level extinction risk can be determined as well as trends in this over time.

Abstracting and Indexing↗