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

Using an Online Platform for Conducting Face-To-Face Interviews

Semi-structured interviews are useful for exploring participants experiences, understandings, and opinions on a particular issue. Traditionally, interviews have taken place in-person however, because of in-person restrictions with Covid-19, and with the changing landscape of online connection, opportunities have arisen for how to conduct interviews using an online platform. The purpose of this article is to highlight the first author’s experiences with using an online platform to conduct face-to-face interviews and the valuable contribution that online interviewing could offer as a valid research tool that differs to that of in-person face-to-face interviews. Online semi-structured interviews were conducted with fourteen midwives and five pregnant people from New Zealand using Microsoft Teams. Interviews were videorecorded and conducted as part of a larger mixed methods multiphase study to explore participants experiences with how they use communication technology to connect with one another. The interviews took place between September 2022 – May 2023. Two key areas which highlight the benefits and challenges with online interviews were identified. These were around the potential to ‘capture the essence of the person’ and through the flexibility of the technology in enabling FTF connections. Challenges were also noted around connectivity issues. Videorecording online interviews offered an ability to capture the ‘essence of the person’ through visual and auditory cues. These same cues were shown to assist with lipreading when transcribing inaudible words which can assist in the analysis of data. There were disruptions to some interviews due to interviewing taking place in the person’s home and connectivity issues, however, these were felt to be minimal. Online interviewing should not be considered a ‘poor relation’ to in-person face-to-face interviews, but instead, a valuable option that contributes towards the growing body of knowledge around online interviewing as a valid research tool that is different from face-to-face

Comparing satisfaction and burnout between caseload and standard care midwives: findings from two cross-sectional surveys conducted in Victoria, Australia

BACKGROUND: Caseload midwifery reduces childbirth interventions and increases women’s satisfaction with care. It is therefore important to understand the impact of caseload midwifery on midwives working in and alongside the model. While some studies have reported higher satisfaction for caseload compared with standard care midwives, others have suggested a need to explore midwives’ work-life balance as well as potential for stress and burnout. This study explored midwives’ attitudes to their professional role, and also measured burnout in caseload midwives compared to standard care midwives at two sites in Victoria, Australia with newly introduced caseload midwifery models. METHODS: All midwives providing maternity care at the study sites were sent questionnaires at the commencement of the caseload midwifery model and two years later. Data items included the Midwifery Process Questionnaire (MPQ) to examine midwives’ attitude to their professional role, the Copenhagen Burnout Inventory (CBI) to measure burnout, and questions about midwives’ views of caseload work. Data were pooled for the two sites and comparisons made between caseload and standard care midwives. The MPQ and CBI data were summarised as individual and group means. RESULTS: Twenty caseload midwives (88%) and 130 standard care midwives (41%) responded at baseline and 22 caseload midwives (95%) and 133 standard care midwives (45%) at two years. Caseload and standard care midwives were initially similar across all measures except client-related burnout, which was lower for caseload midwives (12.3 vs 22.4, p = 0.02). After two years, compared to midwives in standard care, caseload midwives had higher mean scores in professional satisfaction (1.08 vs 0.76, p = 0.01), professional support (1.06 vs 0.11, p <0.01) and client interaction (1.4 vs 0.09, p <0.01) and lower scores for personal burnout (35.7 vs 47.7, p < 0.01), work-related burnout (27.3 vs 42.7, p <0.01), and client-related burnout (11.3 vs 21.4, p < 0.01). CONCLUSION: Caseload midwifery was associated with lower burnout scores and higher professional satisfaction. Further research should focus on understanding the key features of the caseload model that are related to these outcomes to help build a picture of what is required to ensure the long-term sustainability of the model

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 science. © The Author(s) 2019. Published by Oxford University Press