Implementation of the Frailty Care Bundle (FCB) to promote mobilisation, nutrition and cognitive engagement in older people in acute care settings: protocol for an implementation science study

Background: Older people are among the most vulnerable patients in acute care hospitals. The hospitalisation process can result in newly acquired functional or cognitive deficits termed hospital associated decline (HAD). Prioritising fundamental care including mobilisation, nutrition, and cognitive engagement can reduce HAD risk. Aim: The Frailty Care Bundle (FCB) intervention aims to implement and evaluate evidence-based principles on early mobilisation, enhanced nutrition and increased cognitive engagement to prevent functional decline and HAD in older patients. Methods: A hybrid implementation science study will use a pragmatic prospective cohort design with a pre-post mixed methods evaluation to test the effect of the FCB on patient, staff, and health service outcomes. The evaluation will include a description of the implementation process, intervention adaptations, and economic costs analysis. The protocol follows the Standards for Reporting Implementation Studies (StaRI). The intervention design and implementation strategy will utilise the behaviour change theory COM-B (capability, motivation, opportunity) and the Promoting Action on Research Implementation in Health Services (i-PARIHS). A clinical facilitator will use a co-production approach with staff. All patients will receive care as normal, the intervention is delivered at ward level and focuses on nurses and health care assistants (HCA) normative clinical practices. The intervention will be delivered in three hospitals on six wards including rehabilitation, acute trauma, medical and older adult wards. Evaluation: The evaluation will recruit a volunteer sample of 180 patients aged 65 years or older (pre 90; post 90 patients). The primary outcomes are measures of functional status (modified Barthel Index (MBI)) and mobilisation measured as average daily step count using accelerometers. Process data will include ward activity mapping, staff surveys and interviews and an economic cost-impact analysis. Conclusions: This is a complex intervention that involves ward and system level changes and has the potential to improve outcomes for older patients.

A Prediction Study about the Pandemic Era based on Machine Learning Methods

Coronavirus pandemic has been going on since late 2019, millions of people died worldwide, vaccination has recently started in many countries and new strategies are sought by countries since they are still struggling to defeat the virus. So, this research is made to predict the possible ending time of the coronavirus pandemic in Turkey using data mining and statistical studies. Data mining is a computer science study that processes large amounts of data and produces new useful information. It is especially used to support decision making in companies today. So, this project could support the decision making of authorities in developing an effective strategy against the on-going pandemic. During the research we have practiced on Turkey’s coronavirus and vaccination data between 13 January 2021 and 28 May 2021. We used Rapidminer and the Random Forest method for data mining. After all the simulations we have applied and observed during our research, it was clearly seen that vaccination parameters were decreasing the new cases. Also, the stringency index did not affect the new cases. As a conclusion of our research and observations, we think that the government should vaccinate as many people as it can in order to relax restrictions for the last time.

A Value-Based Steering Model for Healthcare

This article aims to answer how a commissioning body can steer health services based on value in an environment where the commissioner is responsible for the health services of a population with varying health service needs. In this design science study, we constructed a value-based steering model consisting of three parts: (1) the principles of steering; (2) the steering process; and (3) Value Steering Canvas, a concrete tool for steering. The study is based on Finland, a tax-funded healthcare system, where healthcare is a public service. The results can be applied in any system where there is a commissioner and a service provider, whether they are two separate organizations or not. We conclude that steering can be done based on value. The commissioning body can start using value-based steering without changes in legislation or in the present service system. Further research is needed to test the model in practice.

Gender Representation Cues Labels of Hard and Soft Sciences

While women's representation in STEM fields has increased over the past several decades, some fields have seen a greater increase women's participation than others. In the present research, we explore how women's participation in STEM disciplines influences labeling of those disciplines as hard vs. soft sciences. Study 1 found that increasing perceived participation of women in a STEM discipline increased the likelihood that participants would label it a soft science. Study 2 found that among people who did not work in science, this tendency to associate women's participation with soft science was correlated with endorsement of stereotypes about women's STEM competency. And Studies 3A and 3B showed that labeling disciplines as soft sciences led to the fields being devalued, deemed less rigorous, and less worthy of federal funding. These studies show that stereotypes about women's STEM competency can impact perceptions of fields in which women participate, with consequences for how scientific disciplines are perceived.