Increasing the Use of Newborn Pain Treatment Following the Implementation of a Parent-Targeted Video: An Outcome Evaluation.

Background/objectives: Despite strong evidence that breastfeeding, skin-to-skin care, and sucrose reduce pain in newborns during minor painful procedures, these interventions remain underutilized in practice. To address this knowledge-to-practice gap, we produced a five-minute parent-targeted video demonstrating the analgesic effects of these strategies and examined whether the use of newborn pain treatment increased in maternal-newborn care settings following the introduction of the video by nurses.

Methods: The design was a pre-post outcome evaluation.

Effectiveness of a treat-to-target strategy in patients with moderate to severely active rheumatoid arthritis treated with abatacept.

Background: To compare a treat-to-target (T2T) approach and routine care (RC) in adults with active to severely active rheumatoid arthritis (RA) initiating subcutaneous abatacept.

Methods: A 12-month cluster-randomized trial in active RA patients treated with abatacept was conducted. Physicians were randomized to RC or T2T with a primary endpoint of achieving sustained Clinical Disease Activity Index (CDAI) low disease activity (LDA) at two consecutive assessments approximately 3 months apart.

Exploring implementation processes of a parent-targeted educational video for improving newborn pain treatment: A sequential exploratory mixed-methods study.

Despite known analgesic effects of breastfeeding (BF), skin-to-skin care (SSC), and sweet solutions (sucrose) for newborns, these interventions remain underutilized. Our team produced a five-minute parent-targeted video (BSweet2Babies) demonstrating BF, SSC, and sucrose during newborn blood sampling. We conducted a sequential exploratory mixed-methods study with eight maternal-newborn units across Ontario, Canada to identify barriers and facilitators to implementing the video and the three pain management strategies.

Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models.

This study aims to address the challenge of developing accurate and efficient parking occupancy forecasting models at the city level for autonomous vehicles. Although deep learning techniques have been successfully employed to develop such models for individual parking lots, it is a resource-intensive process that requires significant amounts of time and data for each parking lot. To overcome this challenge, we propose a novel two-step clustering technique that groups parking lots based on their spatiotemporal patterns.