Background Novel immunisation methods against respiratory syncytial virus (RSV) are emerging, but knowledge of risk factors for severe RSV disease is insufficient for their optimal targeting. We aimed to identify predictors for RSV hospitalisation, and to develop and validate a clinical prediction model to guide RSV immunoprophylaxis for under 1-year-old infants. Methods In this retrospective cohort study using nationwide registries, we studied all infants born in 1997-2020 in Finland (n = 1 254 913) and in 2006-2020 in Sweden (n = 1 459 472), and their parents and siblings. We screened 1 510 candidate predictors and we created a logistic regression model with 16 predictors and compared its performance to a machine learning model (XGboost) using all 1 510 candidate predictors. Findings In addition to known predictors such as severe congenital heart defects (CHD, adjusted odds ratio (aOR) 2.89, 95% confidence interval 2.28-3.65), we identified novel predictors for RSVH, most notably esophageal malformations (aOR 3.11, 1.86-5.19) and lower complexity CHDs (aOR 1.43, 1.25-1.63). In validation data from 2018-2020, the C-statistic was 0.766 (0.742-0.789) in Finland and 0.737 (0.710- 0.762) in Sweden. The clinical prediction model's performance was similar to the machine learning model (C-statistic in Finland 0.771, 0.754-0.788). Calibration varied according to epidemic intensity. Model performance was similar across different strata of parental income. The infants in the 90th percentile of predicted RSVH probability hospitalisation had 3.3 times higher observed risk than the population's average. Assuming 60% effectiveness, immunisation in this top 10% of infants at highest risk would have a number needed to treat of 23 in Finland and 40 in Sweden in preventing hospitalisations. Interpretation The identified predictors and the prediction model can be used in guiding RSV immunoprophylaxis in infants.

Industry and higher education are increasingly utilizing online environments due to digitalization. As a result, the learning experiences in these new digital learning ecosystems as communities must be re-examined critically. This study incorporates the second cycle of the design-based research (DBR) study developing the design principles and theoretical framework for a digital learning ecosystem in Information and Communication Technology (ICT) engineering education in Lapland University of Applied Sciences (UAS), Finland. This cycle examines students’ learning experiences in a project and Industry 4.0-based approach in a digital learning ecosystem with authentic industry assignments and involvement. The study examines the learning experiences of ICT engineering students in a project and Industry 4.0-based approach in a digital environment with authentic industry assignments and involvement. The study was carried out using the Community of Inquiry (CoI) approach. Rasch Rating Scale Model was used to analyse first-, second-, and third-year students’ responses to a translated and adapted CoI questionnaire. Open-ended questions were added to the questionnaire, which was then analysed using content analysis. The results indicate that students perceived project-based learning in an online setting positively. However, the findings point to issues with social interactions and the actual application of learnt knowledge and skills. Challenges in task management and scheduling, as well as receiving feedback, had a somewhat negative impact on the learning experience, particularly during the first year of study. Finally, this paper concludes by presenting a visual model summarizing the design framework developed through a broader DBR study informed by the previous DBR cycles. The findings may benefit practitioners in developing similar communities and ecosystems.