Cross-national variations in reported discrimination among people treated for major depression worldwide: The ASPEN/INDIGO international study

Background: No study has so far explored differences in discrimination reported by people with major depressive disorder (MDD) across countries and cultures. Aims: To (a) compare reported discrimination across different countries, and (b) explore the relative weight of individual and contextual factors in explaining levels of reported discrimination in people with MDD. Method: Cross-sectional multisite international survey (34 countries worldwide) of 1082 people with MDD. Experienced and anticipated discrimination were assessed by the Discrimination and Stigma Scale (DISC). Countries were classified according to their rating on the Human Development Index (HDI). Multilevel negative binomial and Poisson models were used. Results: People living in 'very high HDI' countries reported higher discrimination than those in 'medium/low HDI' countries. Variation in reported discrimination across countries was only partially explained by individual-level variables. The contribution of country-level variables was significant for anticipated discrimination only. Conclusions: Contextual factors play an important role in anticipated discrimination. Country-specific interventions should be implemented to prevent discrimination towards people with MDD

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.Peer reviewe