Development of Android-Based Mobile Learning (M-Learning) on Voltaic Cell Sub Materials to Increase Learning Effectiveness in Pandemic Covid-19 Era

This study was conducted to know the worthiness of learning media mobile learning (M-Learning) is increasing students' learning effectiveness. Research and Development (R&D) has adapted the method used. The research was conducted involving 26 students and three validators. The validator consists of 1 lecturer as a media expert lecturer, one material expert, and one chemistry teacher. Based on the study results, the results obtained media validity of 93%, and the effectiveness of the media in terms of increasing student scores as much as 41.9%. So that the android-based M-Learning product developed can be said to be feasible and can be implemented in chemistry learning on voltaic cell sub materials.

How Online Counselling Is Utilised, Evaluated, and Received

This chapter covers the broad range of online counselling work, using the COVID-19 era as a point of reference. It provides an overview of online applications of counselling and psychotherapy at pre-COVID-19 time and informs the reader of how online counselling provision has been accelerated during the pandemic. A theoretical overview of the key counselling and therapeutic processes as conceptualised in the cyberspace which considers six distinct modes of online communication are provided. An evaluation and the review of the latest efficacy and effectiveness research evidence of online counselling is also provided. The key benefits and challenges of digitalised therapeutic interventions from the clients' and therapists' perspectives covering pre and during COVID-19 are identified. Attention is drawn to existing studies on counselling engagement, adherence, outreach, non-stigmatising counselling practices, power imbalances in the counselling process, and therapy outcomes.

Assessing The Performance of Physician’s Prescribing Preference As An Instrumental Variable in Comparative Effectiveness Research With Moderate and Small Sample Sizes: A Simulation Study

BackgroundInstrumental variable (IV) analyses are used to account for unmeasured confounding in Comparative Effectiveness Research (CER) in pharmacoepidemiology. To date, simulation studies assessing the performance of IV analyses have been based on large samples. However, in many settings, sample sizes are not large.Objective In this simulation study, we assess the utility of Physician’s Prescribing Preference (PPP) as an IV for moderate and smaller sample sizes.MethodsWe designed a simulation study in a CER setting with moderate (around 2500) and small (around 600) sample sizes. The outcome and treatment variables were binary and three variables were used to represent confounding (a binary and a continuous variable representing measured confounding, and a further continuous variable representing unmeasured confounding). We compare the performance of IV and non-IV approaches using two-stage least squares (2SLS) and ordinary least squares (OLS) methods, respectively. Further, we test the performance of different forms of proxies for PPP as an IV.ResultsThe PPP IV approach results in a percent bias of approximately 20%, while the percent bias of OLS is close to 60%. The sample size is not associated with the level of bias for the PPP IV approach. However, smaller sample sizes led to lower statistical power for the PPP IV. Using proxies for PPP based on longer prescription histories result in stronger IVs, partly offsetting the effect on power of smaller sample sizes.Conclusion Irrespective of sample size, the PPP IV approach leads to less biased estimates of treatment effectiveness than conventional multivariable regression adjusting for known confounding only. Particularly for smaller sample sizes, we recommend constructing PPP from long prescribing histories to improve statistical power.

Risk of bias in observational studies using routinely collected data of comparative effectiveness research: a meta-research study

Background To assess the completeness of reporting, research transparency practices, and risk of selection and immortal bias in observational studies using routinely collected data for comparative effectiveness research. Method We performed a meta-research study by searching PubMed for comparative effectiveness observational studies evaluating therapeutic interventions using routinely collected data published in high impact factor journals from 01/06/2018 to 30/06/2020. We assessed the reporting of the study design (i.e., eligibility, treatment assignment, and the start of follow-up). The risk of selection bias and immortal time bias was determined by assessing if the time of eligibility, the treatment assignment, and the start of follow-up were synchronized to mimic the randomization following the target trial emulation framework. Result Seventy-seven articles were identified. Most studies evaluated pharmacological treatments (69%) with a median sample size of 24,000 individuals. In total, 20% of articles inadequately reported essential information of the study design. One-third of the articles (n = 25, 33%) raised some concerns because of unclear reporting (n = 6, 8%) or were at high risk of selection bias and/or immortal time bias (n = 19, 25%). Only five articles (25%) described a solution to mitigate these biases. Six articles (31%) discussed these biases in the limitations section. Conclusion Reporting of essential information of study design in observational studies remained suboptimal. Selection bias and immortal time bias were common methodological issues that researchers and physicians should be aware of when interpreting the results of observational studies using routinely collected data.

Understanding Source-Related Influences on Physicians’ Translating Comparative Effectiveness Research into Patient Care: Results from a Study of Cardiologists

Comparative effectiveness research (CER), which refers to an evaluation of the clinical effectiveness of two or more medical interventions that are used to treat the same condition, has the potential to inform decision-making in both policy circles and physicians’ exam rooms. The ability of stakeholders to translate that research into practice has important implications for health outcomes, but the impact of information sources on physicians in translating CER remains understudied. This project examines the source-related influences on and motivations of cardiologists with respect to willingness to make changes in their practice based on emerging CER results. The results from this survey of cardiologists (N = 42) indicate that the source of information (including perceived credibility of those sources) matters greatly to cardiologists when deciding whether to make a change in practice. These findings suggest data-based implications for researchers and practitioners that are engaged in closing the CER translation gap.

Analysing and reporting of observational data: a systematic review informing the EULAR points to consider when analysing and reporting comparative effectiveness research with observational data in rheumatology

ObjectivesTo evaluate the analysis and reporting of comparative effectiveness research with observational data in rheumatology, informing European Alliance of Associations for Rheumatology points to consider.MethodsWe performed a systematic literature review searching Ovid MEDLINE for original articles comparing drug effectiveness in longitudinal observational studies, published in key rheumatology journals between 2008 and 2019. The extracted information focused on reporting and types of analyses. We evaluated if year of publication impacted results.ResultsFrom 9969 abstracts reviewed, 211 articles fulfilled the inclusion criteria. Ten per cent of studies did not adjust for confounding factors. Some studies did not explain how they chose covariates for adjustment (9%), used bivariate screening (21%) and/or stepwise selection procedures (18%). Only 33% studies reported the number of patients lost to follow-up and 25% acknowledged attrition (drop-out or treatment cessation). To account for attrition, studies used non-responder imputation, followed by last observation carried forward (LOCF) and complete case (CC) analyses. Most studies did not report the number of missing data on covariates (83%), and when addressed, 49% used CC and 11% LOCF. Date of publication did not influence the results.ConclusionMost studies did not acknowledge missing data and attrition, and a tenth did not adjust for any confounding factors. When attempting to account for them, several studies used methods which potentially increase bias (LOCF, CC analysis, bivariate screening…). This study shows that there is no improvement over the last decade, highlighting the need for recommendations for the assessment and reporting of comparative drug effectiveness in observational data in rheumatology.