More from Louis Baxter on doctors taking MAT

The question of whether or not doctors and other health care professionals on medication‐assisted treatment (MAT) are safe to practice medicine has been debated for the last few years since the advent of Food and Drug Administration (FDA)–approved MAT for opioid use disorder (OUD). The newly approved medications have been primarily buprenorphine formulations for OUD, naltrexone formulations for OUD and alcohol use disorder (AUD), and, most recently, an alpha 2‐adrenergic medication that specifically targets amelioration of opioid‐withdrawal symptoms from OUD (lofexidine). Quite frankly, the question of safety about medications to treat substance use disorder (SUD) has been asked since the development of methadone for OUD treatment more than 30 years ago.     

A more powerful unconditional exact test of homogeneity for 2 × c contingency table analysis

The classical unconditional exact p-value test can be used to compare two multinomial distributions with small samples. This general hypothesis requires parameter estimation under the null which makes the test severely conservative. Similar property has been observed for Fisher's exact test with Barnard and Boschloo providing distinct adjustments that produce more powerful testing approaches. In this study, we develop a novel adjustment for the conservativeness of the unconditional multinomial exact p-value test that produces nominal type I error rate and increased power in comparison to all alternative approaches. We used a large simulation study to empirically estimate the 5th percentiles of the distributions of the p-values of the exact test over a range of scenarios and implemented a regression model to predict the values for two-sample multinomial settings. Our results show that the new test is uniformly more powerful than Fisher's, Barnard's, and Boschloo's tests with gains in power as large as several hundred percent in certain scenarios. Lastly, we provide a real-life data example where the unadjusted unconditional exact test wrongly fails to reject the null hypothesis and the corrected unconditional exact test rejects the null appropriately.     

Rights for All: A Response to Blau

Most of the world's nations have revised their constitutions to protect the human rights of their citizens. Yet there has been no national discussion in this country to write human rights into our own constitution. Building on Blau's (2015) call to action, this work explores ways in which sociologists can align the principles of our profession to the advancement of human society and the protection of human rights.     

Speed Mentoring in Teaching and Learning: Young people with experience of the care system mentor social work students

The value of listening and talking to young people with experience of the care system has been recognised as a vital and positive contribution to social work students' learning. A model of co-production was used to develop a series of speed mentoring events whereby social work students placed in local authority children and family settings were mentored by young people with experience of the care system. This tentative review of this small-scale project shares the authors' critical reflections on the value and reliability of this model, for evaluating the outcomes of co-productive mentoring relationships between young people and social work students. The reversal of the power dynamic between student and service user contributed to the unsettling of assumptions about each other and empathy for each other's role. The student authors reflect on learning about the child's experience that has influenced their practice. The mentor authors welcomed the opportunity to participate in the development of ‘young people friendly’ social workers. Suggestions are made as to how this model could be developed to contribute to a more systematic approach to the role of service users in mentoring, advising and contributing to the supervision process of students and social workers.     

Some implications of common consequences in lotteries

This work studies the implications of some aspects of preferences toward risk in the choice between two binary lotteries exhibiting a common consequence. The results obtained are then applied to two different problems: the choice between two risky challenges characterized by different rewards in the case of success and different probabilities of success and the choice between self-protection and self-insurance in the presence of the risk of incurring financial loss.

     

Utilizing Bayesian predictive power in clinical trial design

The Bayesian paradigm provides an ideal platform to update uncertainties and carry them over into the future in the presence of data. Bayesian predictive power (BPP) reflects our belief in the eventual success of a clinical trial to meet its goals. In this paper we derive mathematical expressions for the most common types of outcomes, to make the BPP accessible to practitioners, facilitate fast computations in adaptive trial design simulations that use interim futility monitoring, and propose an organized BPP-based phase II-to-phase III design framework.     

Improving Risk Management: From Lame Excuses to Principled Practice

The three classic pillars of risk analysis are risk assessment (how big is the risk and how sure can we be?), risk management (what shall we do about it?), and risk communication (what shall we say about it, to whom, when, and how?). We propose two complements as important parts of these three bases: risk attribution (who or what addressable conditions actually caused an accident or loss?) and learning from experience about risk reduction (what works, and how well?). Failures in complex systems usually evoke blame, often with insufficient attention to root causes of failure, including some aspects of the situation, design decisions, or social norms and culture. Focusing on blame, however, can inhibit effective learning, instead eliciting excuses to deflect attention and perceived culpability. Productive understanding of what went wrong, and how to do better, thus requires moving past recrimination and excuses. This article identifies common blame‐shifting “lame excuses” for poor risk management. These generally contribute little to effective improvements and may leave real risks and preventable causes unaddressed. We propose principles from risk and decision sciences and organizational design to improve results. These start with organizational leadership. More specifically, they include: deliberate testing and learning—especially from near‐misses and accident precursors; careful causal analysis of accidents; risk quantification; candid expression of uncertainties about costs and benefits of risk‐reduction options; optimization of tradeoffs between gathering additional information and immediate action; promotion of safety culture; and mindful allocation of people, responsibilities, and resources to reduce risks. We propose that these principles provide sound foundations for improving successful risk management.