Roadmaps for Reflection: Implementation Drivers to Bridge the Research-to-Practice Gap in Early Childhood Intervention

Training alone will not enable early childhood practitioners to operationalize evidence-based practices. When newly learned practices do not immediately work as intended, the temptation to return to old ways can be great. Moving an innovation from research to practice may require the use of implementation drivers. The field of implementation science informs us of the need for ongoing support during the implementation process. Implementation guides using a flow chart design may assist practitioners to implement evidence-based practices with fidelity immediately after training. This article provides a framework for developing implementation guides to move quickly from knowledge to utilization after a training occurs as well as a means to consider the usefulness of flowcharts for driving implementation efforts.

Using a human-centred design approach to develop a comprehensive newborn monitoring chart for inpatient care in Kenya

Introduction Job aids such as observation charts are commonly used to record inpatient nursing observations. For sick newborns, it is important to provide critical information, intervene, and tailor treatment to improve health outcomes, as countries work towards reducing neonatal mortality. However, inpatient vital sign readings are often poorly documented and little attention has been paid to the process of chart design as a method of improving care quality. Poorly designed charts do not meet user needs leading to increased mental effort, duplication, suboptimal documentation and fragmentation. We provide a detailed account of a process of designing a monitoring chart. Methods We used a Human-Centred Design (HCD) approach to co-design a newborn monitoring chart between March and May 2019 in three workshops attended by 16–21 participants each (nurses and doctors) drawn from 14 hospitals in Kenya. We used personas, user story mapping during the workshops and observed chart completion to identify challenges with current charts and design requirements. Two new charts were piloted in four hospitals between June 2019 and February 2020 and revised in a cyclical manner. Results Challenges were identified regarding the chart design and supply, and how staff used existing charts. Challenges to use included limited staffing, a knowledge deficit among junior staff, poor interprofessional communication, and lack of appropriate and working equipment. We identified a strong preference from participants for one chart to capture vital signs, assessment of the baby, and feed and fluid prescription and monitoring; data that were previously captured on several charts. Discussion Adopting a Human-Centred Design approach, we designed a new comprehensive newborn monitoring chart that is unlike observation charts in the literature that only focus on vital signs. While the new chart does not address all needs, we believe that once implemented, it can help build a clearer picture of the care given to newborns. Conclusion The chart was co-designed and piloted with the user and context in mind resulting in a unique monitoring chart that can be adopted in similar settings.

Monitoring Mortality Caused by COVID-19 Using Gamma-Distributed Variables Based on Generalized Multiple Dependent State Sampling

More recently in statistical quality control studies, researchers are paying more attention to quality characteristics having nonnormal distributions. In the present article, a generalized multiple dependent state (GMDS) sampling control chart is proposed based on the transformation of gamma quality characteristics into a normal distribution. The parameters for the proposed control charts are obtained using in-control average run length (ARL) at specified shape parametric values for different specified average run lengths. The out-of-control ARL of the proposed gamma control chart using GMDS sampling is explored using simulation for various shift size changes in scale parameters to study the performance of the control chart. The proposed gamma control chart performs better than the existing multiple dependent state sampling (MDS) based on gamma distribution and traditional Shewhart control charts in terms of average run lengths. A case study with real-life data from ICU intake to death caused by COVID-19 has been incorporated for the realistic handling of the proposed control chart design.

Monitoring the Zero-Inflated Time Series Model of Counts with Random Coefficient

In this research, we consider monitoring mean and correlation changes from zero-inflated autocorrelated count data based on the integer-valued time series model with random survival rate. A cumulative sum control chart is constructed due to its efficiency, the corresponding calculation methods of average run length and the standard deviation of the run length are given. Practical guidelines concerning the chart design are investigated. Extensive computations based on designs of experiments are conducted to illustrate the validity of the proposed method. Comparisons with the conventional control charting procedure are also provided. The analysis of the monthly number of drug crimes in the city of Pittsburgh is displayed to illustrate our current method of process monitoring.