Multidimensional assessment of infant, parent and staff outcomes during a family centered care enhancement project in a tertiary neonatal intensive care unit:study protocol of a longitudinal cohort study

Background: The therapeutic advances and progress in the care for preterm infants have enabled the regular survival of very immature infants. However, the high burden of lifelong sequelae following premature delivery constitutes an ongoing challenge. Regardless of premature delivery, parental mental health and a healthy parent–child relationship were identified as essential prerogatives for normal infant development. Family centered care (FCC) supports preterm infants and their families by respecting the particular developmental, social and emotional needs in the Neonatal Intensive Care Unit. Due to the large variations in concepts and goals of different FCC initiatives, scientific data on the benefits of FCC for the infant and family outcome are sparse and its effects on the clinical team need to be elaborated. Methods: This prospective single centre longitudinal cohort study enrols preterm infants ≤ 32 + 0 weeks of gestation and/or birthweight ≤ 1500 g and their parents at the neonatal department of the Giessen University Hospital, Giessen, Germany. Following a baseline period, the rollout of additional FCC elements is executed following a stepwise 6-months approach that covers the NICU environment, staff training, parental education and psychosocial support for parents. Recruitment is scheduled over a 5.5. year period from October 2020 to March 2026. The primary outcome is corrected gestational age at discharge. Secondary infant outcomes include neonatal morbidities, growth, and psychomotor development up to 24 months. Parental outcome measures are directed towards parental skills and satisfaction, parent-infant-interaction and mental health. Staff issues are elaborated with particular focus on the item workplace satisfaction. Quality improvement steps are monitored using the Plan- Do- Study- Act cycle method and outcome measures cover the infant, the parents and the medical team. The parallel data collection enables to study the interrelation between these three important areas of research. Sample size calculation was based on the primary outcome. Discussion: It is scientifically impossible to allocate improvements in outcome measures to individual enhancement steps of FCC that constitutes a continuous change in NICU culture and attitudes covering diverse areas of change. Therefore, our trial is designed to allocate childhood, parental and staff outcome measures during the stepwise changes introduced by a FCC intervention program. Trial registration: Clinicaltrials.gov, trial registration number NCT05286983, date of registration 03/18/2022, retrospectively registered, http://clinicaltrials.gov .</p

Tutorials at PPSN 2016

PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended without prior requirements. All PPSN attendees are cordially invited to take this opportunity to learn about ongoing research activities in our field

Parallelized bayesian optimization for expensive robot controller evolution

An important class of black-box optimization problems relies on using simulations to assess the quality of a given candidate solution. Solving such problems can be computationally expensive because each simulation is very time-consuming. We present an approach to mitigate this problem by distinguishing two factors of computational cost: the number of trials and the time needed to execute the trials. Our approach tries to keep down the number of trials by using Bayesian optimization (BO) –known to be sample efficient– and reducing wall-clock times by parallel execution of trials. We compare the performance of four parallelization methods and two model-free alternatives. Each method is evaluated on all 24 objective functions of the Black-Box-Optimization-Benchmarking (BBOB) test suite in their five, ten, and 20-dimensional versions. Additionally, their performance is investigated on six test cases in robot learning. The results show that parallelized BO outperforms the state-of-the-art CMA-ES on the BBOB test functions, especially for higher dimensions. On the robot learning tasks, the differences are less clear, but the data do support parallelized BO as the ‘best guess’, winning on some cases and never losing