A peer review programme for mental health service users
Objective
Service user involvement should be an essential component of medical research, and many funders are requiring evidence in funding applications that expertise by experience is included. As editors, while we highly value peer review by researchers with lived experience of mental illness, we are concerned that individuals without formal training in research methods are not given the opportunity to express their opinion on the work we publish. Our objective was to conduct a pilot programme to explore how people with lived experience but without research qualifications or an employment contract at a university can contribute to peer review.
Methods
The Lancet Psychiatry launched in 2018 a peer review programme for mental health service users in collaboration with the McPin Foundation, a charity based in London, UK that champions expertise from experience in mental health research. Participants who have used mental health services in the UK were recruited via on-line advertisements, The Lancet Psychiatry and service user news bulletins. Applications were reviewed by a panel from the McPin Foundation and Lancet Psychiatry. Selected individuals received training in peer review in London, UK, and online from The Lancet Psychiatry editorial team supported by McPin staff. Travel costs for an annual meeting are funded by the McPin Foundation, and The Lancet Psychiatry pays for each review. 
Results
48 people applied. McPin staff produced a short list of 20 candidates. Lancet Psychiatry editors and McPin staff then selected ten participants: eight women and two men. 9 participants attended a training day at the Lancet office in December 2018. All participants were encouraged to complete the online training in peer review provided by Publons with a Lancet Psychiatry editor as their mentor. 7 participants (6 women, 1 man) had completed this by mid-August 2019 and have started reviewing manuscripts for The Lancet Psychiatry. They are being invited to review manuscripts in areas such as clinical trials, service delivery, observational studies, qualitative research, health policy, systematic reviews, and opinion pieces. 


A peer review programme for mental health service users

Between 21 May 2016 and 9 September 2019, 1,983 bioRxiv preprints received a single public comment on the bioRxiv website. More than two thirds of those comments were posted by non-authors (n=1,366, 69%) while the remainder were posted by the preprint’s authors (n=617, 31%). Overall, the non-author comments were longer than comments posted by the authors (median number of words was 38, IQR 17 to 83, for non-authors vs 18, IQR 11 to 32, for authors, Mann-Whitney test, P<0.001), and they were posted a median of 23 days (IQR 3 to 117) after the preprints. In comparison, authors’ comments were posted after a median of 91 days (IQR 3 to 23, Mann-Whitney test, P<0.001). Twelve percent of non-author’s comments (n=168) were full review reports resembling those traditionally submitted during the journal peer review process. Comments not resembling full peer review reports most commonly praised the preprint (n=577, 42%), made suggestions on how to improve it (n=399, 29%), or criticized some aspect of the preprint (n=226, 17%). Authors’ comments most commonly contained updates about the preprint’s publication status (n=354, 57%), additional information on the study (n=158, 26%), or solicited feedback for the preprint (n=65, 11%).

From amazing work to I beg to differ: analysis of bioRxiv preprints that received one public comment till September 2019

The time required for a vehicle to travel different routes in the daily traffic of large cities varies and changes constantly, impacting the daily lives of everyone dwelling in those cities. Trying to predict such time is essential to evolve in understanding the behavior of vehicle traffic. On the other hand, due to the vast amount of data generated in this context, it is necessary to use new ways of dealing with this problem. This paper presents an exploratory analysis of the behavior of batch and stream learning algorithms for predicting the trip duration time for vehicles going through different routes. We understand that batch learning algorithms are not necessarily adequate for being used in stream mining situations. However, we would like to have a testbed to analyze the behavior of stream learning algorithms. For our experimental analysis, we used real data from three specific routes. The results show that the use of data stream learning for this domain yields promising results

Lauren A. Maggio

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