OBJECTIVE: Healthcare debates and policy developments are increasingly concerned with a broad range of values-related areas. These include not only ethical, moral, religious, and other types of values 'proper', but also beliefs, preferences, experiences, choices, satisfaction, quality of life, etc. Research on such issues may be difficult to retrieve. This study used word frequency analysis to generate a broad pool of search terms and a brief filter to facilitate relevant searches in bibliographic databases. METHODS: Word frequency analysis for 'values terms' was performed on citations on diabetes, obesity, dementia, and schizophrenia (Medline; 2004-2006; 4440 citations; 1,110,291 words). Concordance® and SPSS 14.0 were used. Text words and MeSH terms of high frequency and precision were compiled into a search filter. It was validated on datasets of citations on dentistry and food hypersensitivity. RESULTS: 144 unique text words and 124 unique MeSH terms of moderate and high frequency (≥ 20) and very high precision (≥ 90%) were identified. Of these, 19 text words and seven MeSH terms were compiled into a 'brief values filter'. In the derivation dataset, it had a sensitivity of 76.8% and precision of 86.8%. In the validation datasets, its sensitivity and precision were, respectively, 70.1% and 63.6% (food hypersensitivity) and 47.1% and 82.6% (dentistry). CONCLUSIONS: This study provided a varied pool of search terms and a simple and highly effective tool for retrieving publications on health-related values. Further work is required to facilitate access to such research and enhance its chances of being translated into practice, policy, and service improvements.
OBJECTIVE: Healthcare debates and policy developments are increasingly concerned with a broad range of values-related areas. These include not only ethical, moral, religious, and other types of values 'proper', but also beliefs, preferences, experiences, choices, satisfaction, quality of life, etc. Research on such issues may be difficult to retrieve. This study used word frequency analysis to generate a broad pool of search terms and a brief filter to facilitate relevant searches in bibliographic databases. METHODS:Word frequency analysis for 'values terms' was performed on citations on diabetes, obesity, dementia, and schizophrenia (Medline; 2004-2006; 4440 citations; 1,110,291 words). Concordance® and SPSS 14.0 were used. Text words and MeSH terms of high frequency and precision were compiled into a search filter. It was validated on datasets of citations on dentistry and food hypersensitivity. RESULTS: 144 unique text words and 124 unique MeSH terms of moderate and high frequency (≥ 20) and very high precision (≥ 90%) were identified. Of these, 19 text words and seven MeSH terms were compiled into a 'brief values filter'. In the derivation dataset, it had a sensitivity of 76.8% and precision of 86.8%. In the validation datasets, its sensitivity and precision were, respectively, 70.1% and 63.6% (food hypersensitivity) and 47.1% and 82.6% (dentistry). CONCLUSIONS: This study provided a varied pool of search terms and a simple and highly effective tool for retrieving publications on health-related values. Further work is required to facilitate access to such research and enhance its chances of being translated into practice, policy, and service improvements.
Authors: Rachel L Shaw; Andrew Booth; Alex J Sutton; Tina Miller; Jonathan A Smith; Bridget Young; David R Jones; Mary Dixon-Woods Journal: BMC Med Res Methodol Date: 2004-03-16 Impact factor: 4.615
Authors: Chris Cooper; Andrew Booth; Jo Varley-Campbell; Nicky Britten; Ruth Garside Journal: BMC Med Res Methodol Date: 2018-08-14 Impact factor: 4.615