ORCID Profile
0000-0002-1751-0512
Current Organisation
University of York
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Publisher: Elsevier BV
Date: 11-2017
DOI: 10.1016/J.JCLINEPI.2017.08.009
Abstract: While it is important for the evidence supporting practice guidelines to be current, that is often not the case. The advent of living systematic reviews has made the concept of "living guidelines" realistic, with the promise to provide timely, up-to-date and high-quality guidance to target users. We define living guidelines as an optimization of the guideline development process to allow updating in idual recommendations as soon as new relevant evidence becomes available. A major implication of that definition is that the unit of update is the in idual recommendation and not the whole guideline. We then discuss when living guidelines are appropriate, the workflows required to support them, the collaboration between living systematic reviews and living guideline teams, the thresholds for changing recommendations, and potential approaches to publication and dissemination. The success and sustainability of the concept of living guideline will depend on those of its major pillar, the living systematic review. We conclude that guideline developers should both experiment with and research the process of living guidelines.
Publisher: Cambridge University Press (CUP)
Date: 11-02-2019
Publisher: Springer Science and Business Media LLC
Date: 08-2017
Publisher: JMIR Publications Inc.
Date: 13-12-2018
DOI: 10.2196/RESPROT.9285
Publisher: Elsevier BV
Date: 06-2021
Publisher: BMJ
Date: 12-10-2022
DOI: 10.1136/EBMENTAL-2022-300530
Abstract: Behavioural and cognitive interventions remain credible approaches in addressing loneliness and depression. There was a need to rapidly generate and assimilate trial-based data during COVID-19. We undertook a parallel pilot RCT of behavioural activation (a brief behavioural intervention) for depression and loneliness (Behavioural Activation in Social Isolation, the BASIL-C19 trial ISRCTN94091479 ). We also assimilate these data in a living systematic review (PROSPERO CRD42021298788) of cognitive and/or behavioural interventions. Participants (≥65 years) with long-term conditions were computer randomised to behavioural activation (n=47) versus care as usual (n=49). Primary outcome was PHQ-9. Secondary outcomes included loneliness (De Jong Scale). Data from the BASIL-C19 trial were included in a metanalysis of depression and loneliness. The 12 months adjusted mean difference for PHQ-9 was −0.70 (95% CI −2.61 to 1.20) and for loneliness was −0.39 (95% CI −1.43 to 0.65). The BASIL-C19 living systematic review (12 trials) found short-term reductions in depression (standardised mean difference (SMD)=−0.31, 95% CI −0.51 to −0.11) and loneliness (SMD=−0.48, 95% CI −0.70 to −0.27). There were few long-term trials, but there was evidence of some benefit (loneliness SMD=−0.20, 95% CI −0.40 to −0.01 depression SMD=−0.20, 95% CI −0.47 to 0.07). We delivered a pilot trial of a behavioural intervention targeting loneliness and depression achieving long-term follow-up. Living meta-analysis provides strong evidence of short-term benefit for loneliness and depression for cognitive and/or behavioural approaches. A fully powered BASIL trial is underway. Scalable behavioural and cognitive approaches should be considered as population-level strategies for depression and loneliness on the basis of a living systematic review.
Publisher: International Global Health Society
Date: 12-2019
Publisher: BMJ
Date: 05-2019
Publisher: Elsevier BV
Date: 11-2017
DOI: 10.1016/J.JCLINEPI.2017.08.011
Abstract: New approaches to evidence synthesis, which use human effort and machine automation in mutually reinforcing ways, can enhance the feasibility and sustainability of living systematic reviews. Human effort is a scarce and valuable resource, required when automation is impossible or undesirable, and includes contributions from online communities ("crowds") as well as more conventional contributions from review authors and information specialists. Automation can assist with some systematic review tasks, including searching, eligibility assessment, identification and retrieval of full-text reports, extraction of data, and risk of bias assessment. Workflows can be developed in which human effort and machine automation can each enable the other to operate in more effective and efficient ways, offering substantial enhancement to the productivity of systematic reviews. This paper describes and discusses the potential-and limitations-of new ways of undertaking specific tasks in living systematic reviews, identifying areas where these human/machine "technologies" are already in use, and where further research and development is needed. While the context is living systematic reviews, many of these enabling technologies apply equally to standard approaches to systematic reviewing.
Publisher: John Wiley & Sons, Ltd
Date: 14-11-2012
Publisher: BMJ
Date: 12-10-2016
DOI: 10.1136/BMJ.I4919
Publisher: Wiley
Date: 30-11-2014
Publisher: BMJ
Date: 05-1998
DOI: 10.1136/EBMH.1.2.63
Publisher: Elsevier BV
Date: 05-2007
Publisher: Elsevier BV
Date: 11-2017
DOI: 10.1016/J.JCLINEPI.2017.08.010
Abstract: Systematic reviews are difficult to keep up to date, but failure to do so leads to a decay in review currency, accuracy, and utility. We are developing a novel approach to systematic review updating termed "Living systematic review" (LSR): systematic reviews that are continually updated, incorporating relevant new evidence as it becomes available. LSRs may be particularly important in fields where research evidence is emerging rapidly, current evidence is uncertain, and new research may change policy or practice decisions. We hypothesize that a continual approach to updating will achieve greater currency and validity, and increase the benefits to end users, with feasible resource requirements over time.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Rachel Churchill.