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      Are publicly available internet resources enabling women to make informed fertility preservation decisions before starting cancer treatment: an environmental scan?

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          Abstract

          Background

          To identify publicly available internet resources and assess their likelihood to support women making informed decisions about, and between, fertility preservation procedures before starting their cancer treatment.

          Methods

          A survey of publically available internet resources utilising an environmental scan method. Inclusion criteria were applied to hits from searches of three data sources (November 2015; repeated June 2017): Google (Chrome) for patient resources; repositories for clinical guidelines and projects; distribution email lists to contact patient decision aid experts. The Data Extraction Sheet applied to eligible resources elicited: resource characteristics; informed and shared decision making components; engagement health services.

          Results

          Four thousand eight hundred fifty one records were identified; 24 patient resources and 0 clinical guidelines met scan inclusion criteria. Most resources aimed to inform women with cancer about fertility preservation procedures and infertility treatment options, but not decision making between options. There was a lack of consistency about how health conditions, decision problems and treatment options were described, and resources were difficult to understand.

          Conclusions

          Unless developed as part of a patient decision aid project, resources did not include components to support proactively women’s fertility preservation decisions. Current guidelines help people deliver information relevant to treatment options within a single disease pathway; we identified five additional components for patient decision aid checklists to support more effectively people’s treatment decision making across health pathways, linking current with future health problems.

          Electronic supplementary material

          The online version of this article (10.1186/s12911-018-0698-3) contains supplementary material, which is available to authorized users.

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          Most cited references56

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          A systematic development process for patient decision aids

          Background The original version of the International Patient Decision Aid Standards (IPDAS) recommended that patient decision aids (PtDAs) should be carefully developed, user-tested and open to scrutiny, with a well-documented and systematically applied development process. We carried out a review to check the relevance and scope of this quality dimension and, if necessary, to update it. Methods Our review drew on three sources: a) published papers describing PtDAs evaluated in randomised controlled trials and included in the most recent Cochrane Collaboration review; b) linked papers cited in the trial reports that described how the PtDAs had been developed; and c) papers and web reports outlining the development process used by organisations experienced in developing multiple PtDAs. We then developed an extended model of the development process indicating the various steps on which documentation is required, as well as a checklist to assess the frequency with which each of the elements was publicly reported. Results Key features common to all patient decision aid (PtDA) development processes include: scoping and design; development of a prototype; ‘alpha’ testing with patients and clinicians in an iterative process; ‘beta’ testing in ‘real life’ conditions (field tests); and production of a final version for use and/or further evaluation. Only about half of the published reports on the development of PtDAs that we reviewed appear to have been field tested with patients, and even fewer had been reviewed or tested by clinicians not involved in the development process. Very few described a distribution strategy, and surprisingly few (17%) described a method for reviewing and synthesizing the clinical evidence. We describe a model development process that includes all the original elements of the original IPDAS criterion, expanded to include consideration of format and distribution plans as well as prototype development. Conclusions The case for including each of the elements outlined in our model development process is pragmatic rather than evidence-based. Optimal methods for ensuring that each stage of the process is carried out effectively require further development and testing.
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            Cancer, pregnancy and fertility: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.

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              Searching for grey literature for systematic reviews: challenges and benefits.

              There is ongoing interest in including grey literature in systematic reviews. Including grey literature can broaden the scope to more relevant studies, thereby providing a more complete view of available evidence. Searching for grey literature can be challenging despite greater access through the Internet, search engines and online bibliographic databases. There are a number of publications that list sources for finding grey literature in systematic reviews. However, there is scant information about how searches for grey literature are executed and how it is included in the review process. This level of detail is important to ensure that reviews follow explicit methodology to be systematic, transparent and reproducible. The purpose of this paper is to provide a detailed account of one systematic review team's experience in searching for grey literature and including it throughout the review. We provide a brief overview of grey literature before describing our search and review approach. We also discuss the benefits and challenges of including grey literature in our systematic review, as well as the strengths and limitations to our approach. Detailed information about incorporating grey literature in reviews is important in advancing methodology as review teams adapt and build upon the approaches described.
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                Author and article information

                Contributors
                +44 (0)113 812 2934 , N.Mahmoodi@leedsbeckett.ac.uk
                H.L.Bekker@leeds.ac.uk
                N.V.King@leeds.ac.uk
                Jane.Hughes2@sheffield.ac.uk
                G.L.Jones@leedsbeckett.ac.uk
                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central (London )
                1472-6947
                19 November 2018
                19 November 2018
                2018
                : 18
                : 104
                Affiliations
                [1 ]ISNI 0000 0001 0745 8880, GRID grid.10346.30, Department of Psychology, School of Social Sciences, , Leeds Beckett University, ; Leeds, LS1 3HE UK
                [2 ]ISNI 0000 0004 1936 8403, GRID grid.9909.9, Institute of Health Sciences - School of Medicine, University of Leeds, ; Leeds, UK
                [3 ]ISNI 0000 0004 1936 9262, GRID grid.11835.3e, School of Health & Related Research, , University of Sheffield, ; Sheffield, UK
                Author information
                http://orcid.org/0000-0002-8598-3733
                Article
                698
                10.1186/s12911-018-0698-3
                6245564
                30453942
                8f5f8dd9-d441-4cf9-9e32-b095477b2d31
                © The Author(s). 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

                History
                : 1 June 2018
                : 26 October 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100011703, Yorkshire Cancer Research;
                Award ID: S391
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2018

                Bioinformatics & Computational biology
                patient decision aid,shared decision making,fertility preservation,cancer treatment,ehealth,environmental scan

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