The Knight of Malta

BACKGROUND: For most women, participation in decision-making during maternity care has a positive impact on their childbirth experiences. Shared decision-making (SDM) is widely advocated as a way to support people in their healthcare choices. The aim of this study was to identify quality criteria and professional competencies for applying shared decision-making in maternity care. We focused on decision-making in everyday maternity care practice for healthy women. METHODS: An international three-round web-based Delphi study was conducted. The Delphi panel included international experts in SDM and in maternity care: mostly midwives, and additionally obstetricians, educators, researchers, policy makers and representatives of care users. Round 1 contained open-ended questions to explore relevant ingredients for SDM in maternity care and to identify the competencies needed for this. In rounds 2 and 3, experts rated statements on quality criteria and competencies on a 1 to 7 Likert-scale. A priori, positive consensus was defined as 70% or more of the experts scoring >/=6 (70% panel agreement). RESULTS: Consensus was reached on 45 quality criteria statements and 4 competency statements. SDM in maternity care is a dynamic process that starts in antenatal care and ends after birth. Experts agreed that the regular visits during pregnancy offer opportunities to build a relationship, anticipate situations and revisit complex decisions. Professionals need to prepare women antenatally for unexpected, urgent decisions in birth and revisit these decisions postnatally. Open and respectful communication between women and care professionals is essential; information needs to be accurate, evidence-based and understandable to women. Experts were divided about the contribution of professional advice in shared decision-making and about the partner's role. CONCLUSIONS: SDM in maternity care is a dynamic process that takes into consideration women's individual needs and the context of the pregnancy or birth. The identified ingredients for good quality SDM will help practitioners to apply SDM in practice and educators to prepare (future) professionals for SDM, contributing to women's positive birth experience and satisfaction with care

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research