Mahmić-Kaknjo, M;
Tomić, V;
Ellen, ME;
Nussbaumer-Streit, B;
Sfetcu, R;
Baladia, E;
Riva, N;
... Marušić, A; + view all
(2023)
Delphi survey on the most promising areas and methods to improve systematic reviews' production and updating.
Systematic Reviews
, 12
, Article 56. 10.1186/s13643-023-02223-3.
Preview |
Text
s13643-023-02223-3.pdf - Published Version Download (1MB) | Preview |
Abstract
BACKGROUND: Systematic reviews (SRs) are invaluable evidence syntheses, widely used in biomedicine and other scientific areas. Tremendous resources are being spent on the production and updating of SRs. There is a continuous need to automatize the process and use the workforce and resources to make it faster and more efficient. METHODS: Information gathered by previous EVBRES research was used to construct a questionnaire for round 1 which was partly quantitative, partly qualitative. Fifty five experienced SR authors were invited to participate in a Delphi study (DS) designed to identify the most promising areas and methods to improve the efficient production and updating of SRs. Topic questions focused on which areas of SRs are most time/effort/resource intensive and should be prioritized in further research. Data were analysed using NVivo 12 plus, Microsoft Excel 2013 and SPSS. Thematic analysis findings were used on the topics on which agreement was not reached in round 1 in order to prepare the questionnaire for round 2. RESULTS: Sixty percent (33/55) of the invited participants completed round 1; 44% (24/55) completed round 2. Participants reported average of 13.3 years of experience in conducting SRs (SD 6.8). More than two thirds of the respondents agreed/strongly agreed the following topics should be prioritized: extracting data, literature searching, screening abstracts, obtaining and screening full texts, updating SRs, finding previous SRs, translating non-English studies, synthesizing data, project management, writing the protocol, constructing the search strategy and critically appraising. Participants have not considered following areas as priority: snowballing, GRADE-ing, writing SR, deduplication, formulating SR question, performing meta-analysis. CONCLUSIONS: Data extraction was prioritized by the majority of participants as an area that needs more research/methods development. Quality of available language translating tools has dramatically increased over the years (Google translate, DeepL). The promising new tool for snowballing emerged (Citation Chaser). Automation cannot substitute human judgement where complex decisions are needed (GRADE-ing). TRIAL REGISTRATION: Study protocol was registered at https://osf.io/bp2hu/ .
| Type: | Article |
|---|---|
| Title: | Delphi survey on the most promising areas and methods to improve systematic reviews' production and updating |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s13643-023-02223-3 |
| Publisher version: | https://doi.org/10.1186/s13643-023-02223-3 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| Keywords: | Automation tools, Evidence syntesis, Prioritization, Humans, Surveys and Questionnaires, Research Design, Records |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Applied Health Research |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10168559 |
Archive Staff Only
 |
View Item |
