Israel Approves Compassionate Use of MDMA to Treat PTSD

Ido Efrati
www.haaretz.com
Originally posted February 10, 2019

MDMA, popularly known as ecstasy, is a drug more commonly associated with raves and nightclubs than a therapist’s office.

Emerging research has shown promising results in using this “party drug” to treat patients suffering from post-traumatic stress disorder, and Israel’s Health Ministry has just approved the use of MDMA to treat dozens of patients.

MDMA is classified in Israel as a “dangerous drug”, recreational use is illegal, and therapeutic use of MDMA has yet to be formally approved and is still in clinical trials.

However, this treatment is deemed as “compassionate use,” which allows drugs that are still in development to be made available to patients outside of a clinical trial due to the lack of effective alternatives.

The info is here.

Actions speak louder than outcomes in judgments of prosocial behavior.

Yudkin, D. A., Prosser, A. M. B., & Crockett, M. J. (2018).
Emotion. Advance online publication.
http://dx.doi.org/10.1037/emo0000514

Abstract

Recently proposed models of moral cognition suggest that people’s judgments of harmful acts are influenced by their consideration both of those acts’ consequences (“outcome value”), and of the feeling associated with their enactment (“action value”). Here we apply this framework to judgments of prosocial behavior, suggesting that people’s judgments of the praiseworthiness of good deeds are determined both by the benefit those deeds confer to others and by how good they feel to perform. Three experiments confirm this prediction. After developing a new measure to assess the extent to which praiseworthiness is influenced by action and outcome values, we show how these factors make significant and independent contributions to praiseworthiness. We also find that people are consistently more sensitive to action than to outcome value in judging the praiseworthiness of good deeds, but not harmful deeds. This observation echoes the finding that people are often insensitive to outcomes in their giving behavior. Overall, this research tests and validates a novel framework for understanding moral judgment, with implications for the motivations that underlie human altruism.

Biased algorithms: here’s a more radical approach to creating fairness

Tom Douglas
theconversation.com
Originally posted January 21, 2019

Here is an excerpt:

What’s fair?

AI researchers concerned about fairness have, for the most part, been focused on developing algorithms that are procedurally fair – fair by virtue of the features of the algorithms themselves, not the effects of their deployment. But what if it’s substantive fairness that really matters?

There is usually a tension between procedural fairness and accuracy – attempts to achieve the most commonly advocated forms of procedural fairness increase the algorithm’s overall error rate. Take the COMPAS algorithm for example. If we equalised the false positive rates between black and white people by ignoring the predictors of recidivism that tended to be disproportionately possessed by black people, the likely result would be a loss in overall accuracy, with more people wrongly predicted to re-offend, or not re-offend.

We could avoid these difficulties if we focused on substantive rather than procedural fairness and simply designed algorithms to maximise accuracy, while simultaneously blocking or compensating for any substantively unfair effects that these algorithms might have. For example, instead of trying to ensure that crime prediction errors affect different racial groups equally – a goal that may in any case be unattainable – we could instead ensure that these algorithms are not used in ways that disadvantage those at high risk. We could offer people deemed “high risk” rehabilitative treatments rather than, say, subjecting them to further incarceration.

The info is here.

Artificial Intelligence and ethics will drive a patient matching revolution in 2019

Mark Larow
MedCity News
Originally posted January 1, 2019

Here is an excerpt:

Yet nowhere can AI have a more immediate and accessible impact than in patient matching. Currently, health systems have teams of data stewards and health information management (HIM) professionals dedicated to finding, reviewing, researching, and resolving records that their EHR or EMPI has flagged as “potential duplicates.” Essentially, these employees are spending hours each day looking at, for example, a record for Jane Jones and another for Jane Smith, trying to decide if both Janes are actually the same person and if her records should be merged.

Referential matching technology can automate 50-to-75 percent of this manual effort by being an intelligent and data-driven technology. It can automatically find and resolve duplicate records that EHRs and EMPIs have missed, enabling data stewards and HIM staff to focus on higher-value projects—while simultaneously lowering the operational costs and inefficiencies plaguing health systems by automating manual work.

Ultimately, automating the discovery and resolution of duplicate records with referential matching technology can reduce claims denials to save up to $1.5 million, reduce operational costs by at least $200,000, improve the ROI of EHR deployments, and enable value-based care and patient engagement initiatives by enabling more complete and accurate patient health histories.

Ethics

Health systems are increasingly making technology investments not just to reduce costs or improve efficiencies, but also because not using new technologies is becoming unethical. We have reached a tipping point where innovative new technologies are prominent, successful, and inexpensive enough for ethics to begin driving technology purchasing decisions.

The info is here.

The Ethics of Paternalism

Ingrid M. Paulin, Jenna Clark, & Julie O'Brien
Scientific American
Originally published on December 21, 2018

Here is an excerpt:

Choosing what to do and which approach to take requires making a decision about paternalism, or influencing someone’s behavior for their own good. Every time someone designs policies, products or services, they make a decision about paternalism, whether they are aware of it or not. They will inevitably influence how people behave; there's no such thing as a neutral choice.

Arguments about paternalism have traditionally focused on the extreme ends of the spectrum; you either let people have complete autonomy, or you completely restrict undesirable behaviors. In reality, however, there are many options in between, and there are few guidelines about how one should navigate the complex moral landscape of influence to decide which approach is justified in a given situation.

Traditional economists may argue for more autonomy on the grounds that people will always behave in line with their own best interest. In their view, people have stable preferences and are always weighing the costs and benefits of every option before making decisions. Because they know their preferences better than do others, they should be able to act autonomously to maximize their own positive outcomes.

The info is here.

What is ‘moral distress’? A narrative synthesis of the literature

Georgina Morley, Jonathan Ives, Caroline Bradbury-Jones, & Fiona Irvine

Nursing Ethics

First Published October 8, 2017 Review Article  

https://doi.org/10.1177/0969733017724354

Introduction

The concept of moral distress (MD) was introduced to nursing by Jameton who defined MD as arising, ‘when one knows the right thing to do, but institutional constraints make it nearly impossible to pursue the right course of action’. MD has subsequently gained increasing attention in nursing research, the majority of which conducted in North America but now emerging in South America, Europe, the Middle East and Asia. Studies have highlighted the deleterious effects of MD, with correlations between higher levels of MD, negative perceptions of ethical climate and increased levels of compassion fatigue among nurses. Consensus is that MD can negatively impact patient care, causing nurses to avoid certain clinical situations and ultimately leave the profession. MD is therefore a significant problem within nursing, requiring investigation, understanding, clarification and responses. The growing body of MD research, however, is arguably failing to bring the required clarification but rather has complicated attempts to study it. The increasing number of cited causes and effects of MD means the term has expanded to the point that according to Hanna and McCarthy and Deady, it is becoming an ‘umbrella term’ that lacks conceptual clarity referring unhelpfully to a wide range of phenomena and causes. Without, however, a coherent and consistent conceptual understanding, empirical studies of MD’s prevalence, effects, and possible responses are likely to be confused and contradictory.

A useful starting point is a systematic exploration of existing literature to critically examine definitions and understandings currently available, interrogating their similarities, differences, conceptual strengths and weaknesses. This article presents a narrative synthesis that explored proposed necessary and sufficient conditions for MD, and in doing so, this article also identifies areas of conceptual tension and agreement.

The research is here.

Is It Ethical to Use Prognostic Estimates from Machine Learning to Treat Psychosis?

Nicole Martinez-Martin, Laura B. Dunn, and Laura Weiss Roberts
AMA J Ethics. 2018;20(9):E804-811.
doi: 10.1001/amajethics.2018.804.

Abstract

Machine learning is a method for predicting clinically relevant variables, such as opportunities for early intervention, potential treatment response, prognosis, and health outcomes. This commentary examines the following ethical questions about machine learning in a case of a patient with new onset psychosis: (1) When is clinical innovation ethically acceptable? (2) How should clinicians communicate with patients about the ethical issues raised by a machine learning predictive model?

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Conclusion

In order to implement the predictive tool in an ethical manner, Dr K will need to carefully consider how to give appropriate information—in an understandable manner—to patients and families regarding use of the predictive model. In order to maximize benefits from the predictive model and minimize risks, Dr K and the institution as a whole will need to formulate ethically appropriate procedures and protocols surrounding the instrument. For example, implementation of the predictive tool should consider the ability of a physician to override the predictive model in support of ethically or clinically important variables or values, such as beneficence. Such measures could help realize the clinical application potential of machine learning tools, such as this psychosis prediction model, to improve the lives of patients.

Therapist empathy and client outcome: An updated meta-analysis

Elliott, R., Bohart, A. C., Watson, J. C., & Murphy, D. (2018).
Psychotherapy, 55(4), 399-410.

Abstract

Put simply, empathy refers to understanding what another person is experiencing or trying to express. Therapist empathy has a long history as a hypothesized key change process in psychotherapy. We begin by discussing definitional issues and presenting an integrative definition. We then review measures of therapist empathy, including the conceptual problem of separating empathy from other relationship variables. We follow this with clinical examples illustrating different forms of therapist empathy and empathic response modes. The core of our review is a meta-analysis of research on the relation between therapist empathy and client outcome. Results indicated that empathy is a moderately strong predictor of therapy outcome: mean weighted r = .28 (p < .001; 95% confidence interval [.23, .33]; equivalent of d = .58) for 82 independent samples and 6,138 clients. In general, the empathy–outcome relation held for different theoretical orientations and client presenting problems; however, there was considerable heterogeneity in the effects. Client, observer, and therapist perception measures predicted client outcome better than empathic accuracy measures. We then consider the limitations of the current data. We conclude with diversity considerations and practice recommendations, including endorsing the different forms that empathy may take in therapy.

The research is here.

Clinical Impact Statement—

Question: Does therapist empathy predict success in psychotherapy? 

Findings: In general, clients have moderately better outcomes in psychotherapy when clients, therapists, and observers perceive therapists as understanding them. 

Meaning: Empathy is an important element of any therapeutic relationship, and worth the investment of time and effort required to do it well and consistently. 

Next Steps: Careful research using diverse methods is needed to firmly establish and explain the causal role of therapist empathy in bringing about client outcome; clinicians can contribute by identifying situations in which empathy may be particularly valuable or conversely contraindicated.

James Gunn's Firing Is What Happens When We Outsource Morality to Capitalism

Anhar Karim
Forbes.com
Originally posted September 16, 2018

Here is an excerpt:

A study last year from Cone Communications found that 87% of consumers said they’d purchase a company’s product if said company showed that they cared about issues consumers cared about. On the flip side of that, 75% of consumers said they would not buy from a company which showed they did not care. If business executives and CEOs are following along, as they surely are, the lesson is this: If a company wants to stay on top in the modern age, and if they want to maximize their profits, then they need to beat their competitors not only with superior products but also with demonstrated, superior moral behavior.

This, on its face, does not appear horrible. Indeed, this new development has led to a lot of undeniable good. It’s this idea that gave the #MeToo movement its bite and toppled industry giants such as Harvey Weinstein, Kevin Spacey and Les Moonves. It’s this strategy that’s led Warner Brothers to mandate an inclusion rider, Sony to diversify their comic titles, and Marvel to get their heroes to visit children in hospitals.

So how could any of this be negative?

Well, consider the other side of these attempts at corporate responsibility, the efforts that look good but help no one. What am I talking about? Consider that we recently had a major movie with a song celebrating difference and being true to yourself. That sounds good. However, the plot of the film is actually about exploiting minorities for profit. So it falls flat. Or consider that we had a woman cast in a Marvel franchise playing a role normally reserved for a man. Sounds progressive, right? Until we realize that that is also an example of a white actor trying her best to look Asian and thus limiting diversity. Also, consider that Sony decided to try and help fight back against bullying. Noble intent, but the way they went about it? They helped put up posters oddly suggesting that bullying could be stopped with sending positive emojis. Again, all of these sound sort of good on paper, but in practice, they help no one.

The info is here.

Association Between Physician Burnout and Patient Safety, Professionalism, and Patient Satisfaction: A Systematic Review and Meta-analysis.

Maria Panagioti, PhD; Keith Geraghty, PhD; Judith Johnson, PhD; et al
JAMA Intern Med. Published online September 4, 2018.
doi:10.1001/jamainternmed.2018.3713

Abstract

Importance  Physician burnout has taken the form of an epidemic that may affect core domains of health care delivery, including patient safety, quality of care, and patient satisfaction. However, this evidence has not been systematically quantified.

Objective  To examine whether physician burnout is associated with an increased risk of patient safety incidents, suboptimal care outcomes due to low professionalism, and lower patient satisfaction.

Data Sources  MEDLINE, EMBASE, PsycInfo, and CINAHL databases were searched until October 22, 2017, using combinations of the key terms physicians, burnout, and patient care. Detailed standardized searches with no language restriction were undertaken. The reference lists of eligible studies and other relevant systematic reviews were hand-searched.

Data Extraction and Synthesis  Two independent reviewers were involved. The main meta-analysis was followed by subgroup and sensitivity analyses. All analyses were performed using random-effects models. Formal tests for heterogeneity (I2) and publication bias were performed.

Main Outcomes and Measures  The core outcomes were the quantitative associations between burnout and patient safety, professionalism, and patient satisfaction reported as odds ratios (ORs) with their 95% CIs.

Results  Of the 5234 records identified, 47 studies on 42 473 physicians (25 059 [59.0%] men; median age, 38 years [range, 27-53 years]) were included in the meta-analysis. Physician burnout was associated with an increased risk of patient safety incidents (OR, 1.96; 95% CI, 1.59-2.40), poorer quality of care due to low professionalism (OR, 2.31; 95% CI, 1.87-2.85), and reduced patient satisfaction (OR, 2.28; 95% CI, 1.42-3.68). The heterogeneity was high and the study quality was low to moderate. The links between burnout and low professionalism were larger in residents and early-career (≤5 years post residency) physicians compared with middle- and late-career physicians (Cohen Q = 7.27; P = .003). The reporting method of patient safety incidents and professionalism (physician-reported vs system-recorded) significantly influenced the main results (Cohen Q = 8.14; P = .007).

Conclusions and Relevance  This meta-analysis provides evidence that physician burnout may jeopardize patient care; reversal of this risk has to be viewed as a fundamental health care policy goal across the globe. Health care organizations are encouraged to invest in efforts to improve physician wellness, particularly for early-career physicians. The methods of recording patient care quality and safety outcomes require improvements to concisely capture the outcome of burnout on the performance of health care organizations.

The research is here.

How to find the right therapist

Nicole Spector
www.nbcnews.com
Originally posted August 5, 2018

Here are two excerpts:

What does therapy mean to you? What areas of your life do you want to explore and how? Do you want to talk about your family, or would you rather focus on a very specific past trauma or would you just like someone to talk with about whatever might be troubling you that week? The answers to these questions may change over time, but when you first go into therapy, ideally you should have some picture of what you want.

“You should know what you want to work on [when beginning therapy],” says Dr. Cira. “Do you feel really strong that you don't want to focus on your past and only the present? Do you want to focus more on things that have happened to you in the past? Do you want someone to help you ‘solve’ your problems or someone who will really sit with you in your pain or both? These are all things you should ask yourself that will help guide your search.”

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“Listen to your intuition,” says Humphreys. “If you feel instinctively unsafe with a therapist, that will probably inhibit the progress you will make. In contrast, if you feel you ‘click’ with a therapist, that's a good sign that you will be able to build a working alliance with them.”

Jor-El Caraballo, a licensed therapist, wellness coach and co-creator of Viva Wellness, notes that while there are measures that are clinical in nature there is also “a visceral feeling of just being comfortable enough to sit in a room with someone for the therapy hour. That can't be replaced and if you don't feel comfortable enough in a few sessions then it's probably best to tell your therapist this and work toward moving on.”

The info is here.

The Business Case for Curiosity

Francesca Gino
Harvard Business Review
Originally posted September-October Issue

Here are two excerpts:

The Benefits of Curiosity

New research reveals a wide range of benefits for organizations, leaders, and employees.

Fewer decision-making errors.

In my research I found that when our curiosity is triggered, we are less likely to fall prey to confirmation bias (looking for information that supports our beliefs rather than for evidence suggesting we are wrong) and to stereotyping people (making broad judgments, such as that women or minorities don’t make good leaders). Curiosity has these positive effects because it leads us to generate alternatives.

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It’s natural to concentrate on results, especially in the face of tough challenges. But focusing on learning is generally more beneficial to us and our organizations, as some landmark studies show. For example, when U.S. Air Force personnel were given a demanding goal for the number of planes to be landed in a set time frame, their performance decreased. Similarly, in a study led by Southern Methodist University’s Don VandeWalle, sales professionals who were naturally focused on performance goals, such as meeting their targets and being seen by colleagues as good at their jobs, did worse during a promotion of a product (a piece of medical equipment priced at about $5,400) than reps who were naturally focused on learning goals, such as exploring how to be a better salesperson. That cost them, because the company awarded a bonus of $300 for each unit sold.

A body of research demonstrates that framing work around learning goals (developing competence, acquiring skills, mastering new situations, and so on) rather than performance goals (hitting targets, proving our competence, impressing others) boosts motivation. And when motivated by learning goals, we acquire more-diverse skills, do better at work, get higher grades in college, do better on problem-solving tasks, and receive higher ratings after training. Unfortunately, organizations often prioritize performance goals.

The information is here.

Negative effects in psychotherapy

Alexander Rozental, Louis Castonguay, Sona Dimidjian, and others
BJPsych Open (2018) 4, 307–312.

Background

Psychotherapy can alleviate mental distress and improve quality of life, but little is known about its potential negative effects and how to determine their frequency.

Aims

To present a commentary on the current understanding and future research directions of negative effects in psychotherapy.

Method

An anonymous survey was distributed to a select group of researchers, using an analytical framework known as strengths, weaknesses, opportunities and threats.

Results

The researchers perceive an increased awareness of negative effects in psychotherapy in recent years, but also discuss some of the unresolved issues in relation to their definition, assessment and reporting. Qualitative methods and naturalistic designs are regarded as important to pursue, although a number of obstacles to using such methods are identified.

Conclusion

Negative effects of psychotherapy are multifaceted, warranting careful considerations in order for them to be monitored and reported in research settings and routine care.

The info is here.

Moral fatigue: The effects of cognitive fatigue on moral reasoning

Shane Timmons and Ruth MJ Byrne
Quarterly Journal of Experimental Psychology
pp. 1–12

Abstract

We report two experiments that show a moral fatigue effect: participants who are fatigued after they have carried out a tiring cognitive task make different moral judgements compared to participants who are not fatigued. Fatigued participants tend to judge that a moral violation is less permissible even though it would have a beneficial effect, such as killing one person to save the lives of five others. The moral fatigue effect occurs when people make a judgement that focuses on the harmful action, killing one person, but not when they make a judgement that focuses on the beneficial
outcome, saving the lives of others, as shown in Experiment 1 (n=196). It also occurs for judgements about morally good actions, such as jumping onto railway tracks to save a person who has fallen there, as shown in Experiment 2 (n=187).  The results have implications for alternative explanations of moral reasoning.

The article is here.

The Ethics of Ceding More Power To Machines

Brhmie Balaram
www.theRSA.org
Originally posted May 31, 2018

Here is an excerpt:

This gets to the crux of people’s fears about AI – there is a perception that we may be ceding too much power to AI, regardless of the reality. The public’s concerns seem to echo that of the academic Virginia Eubanks, who argues that the fundamental problem with these systems is that they enable the ethical distance needed “to make inhuman choices about who gets food and who starves, who has housing and who remains homeless, whose family stays together and whose is broken up by the state.”

Yet, these systems also have the potential to increase the fairness of outcomes if they are able to improve accuracy and minimise biases. They may also increase efficiency and savings for both the organisation that deploys the systems, as well as the people subject to the decision.

These are the sorts of trade-offs that a public dialogue, and in particular, a long-form deliberative process like a citizens’ jury, can address.

The info is here.

Can Morality Be Engineered In Artificial General Intelligence Systems?

Abhijeet Katte
Analytics India Magazine
Originally published May 10, 2018

Here is an excerpt:

This report Engineering Moral Agents – from Human Morality to Artificial Morality discusses challenges in engineering computational ethics and how mathematically oriented approaches to ethics are gaining traction among researchers from a wide background, including philosophy. AGI-focused research is evolving into the formalization of moral theories to act as a base for implementing moral reasoning in machines. For example, Kevin Baum from the University of Saarland talked about a project about teaching formal ethics to computer-science students wherein the group was involved in building a database of moral-dilemma examples from the literature to be used as benchmarks for implementing moral reasoning.

Another study, titled Towards Moral Autonomous Systems from a group of European researchers states that today there is a real need for a functional system of ethical reasoning as AI systems that function as part of our society are ready to be deployed.One of the suggestions include having every assisted living AI system to have  a “Why did you do that?” button which, when pressed, causes the robot to explain why it carried out the previous action.

The information is here.

Determined to be humble? Exploring the relationship between belief in free will and humility

Earp, B. D., Everett, J. A., Nadelhoffer, T., Caruso, G. D., Shariff, A., & Sinnott-Armstrong, W. (2018, April 24).
 
Abstract

In recent years, diminished belief in free will or increased belief in determinism have been associated with a range of antisocial or otherwise negative outcomes: unjustified aggression, cheating, prejudice, less helping behavior, and so on. Only a few studies have entertained the possibility of prosocial or otherwise positive outcomes, such as greater willingness to forgive and less motivation to punish retributively. Here, five studies explore the relationship between belief in determinism and another positive outcome or attribute, namely, humility. The reported findings suggest that relative disbelief in free will is reliably associated with at least one type of humility—what we call ‘Einsteinian’ humility—but is not associated with, or even negatively associated with, other types of humility described in the literature.

The preprint is here.

Moral Fatigue: The Effects of Cognitive Fatigue on Moral Reasoning

S. Timmons and R. Byrne
Quarterly Journal of Experimental Psychology (March 2018)

Abstract

We report two experiments that show a moral fatigue effect: participants who are fatigued after they have carried out a tiring cognitive task make different moral judgments compared to participants who are not fatigued. Fatigued participants tend to judge that a moral violation is less permissible even though it would have a beneficial effect, such as killing one person to save the lives of five others. The moral fatigue effect occurs when people make a judgment that focuses on the harmful action, killing one person, but not when they make a judgment that focuses on the beneficial outcome, saving the lives of others, as shown in Experiment 1 (n = 196). It also occurs for judgments about morally good actions, such as jumping onto railway tracks to save a person who has fallen there, as shown in Experiment 2 (n = 187). The results have implications for alternative explanations of moral reasoning.

The research is here.

No Luck for Moral Luck

Markus Kneer, University of Zurich Edouard Machery, University of Pittsburgh
Draft, March 2018

Abstract

Moral philosophers and psychologists often assume that people judge morally lucky and morally unlucky agents differently, an assumption that stands at the heart of the puzzle of moral luck. We examine whether the asymmetry is found for reflective intuitions regarding wrongness, blame, permissibility and punishment judgments, whether people's concrete, case-based judgments align with their explicit, abstract principles regarding moral luck, and what psychological mechanisms might drive the effect. Our experiments  produce three findings: First, in within-subjects experiments favorable to reflective deliberation, wrongness, blame, and permissibility judgments across different moral luck conditions are the same for the vast majority of people. The philosophical puzzle of moral luck, and the challenge to the very possibility of systematic ethics it is frequently taken to engender, thus simply does not arise. Second, punishment judgments are significantly more outcome-dependent than wrongness, blame, and permissibility  judgments. While this is evidence in favor of current dual-process theories of moral  judgment, the latter need to be qualified since punishment does not pattern with blame. Third, in between-subjects experiments, outcome has an effect on all four types of moral  judgments. This effect is mediated by negligence ascriptions and can ultimately be explained as due to differing probability ascriptions across cases.

The manuscript is here.

Getting Ethics Training Right for Leaders and Employees

Deloitte
The Wall Street Journal
Originally posted April 9, 2018

Here is an excerpt:

Ethics training has needed a serious redesign for some time, and we are seeing three changes to make training more effective. First, many organizations recognize that compliance training is not enough. Simply knowing the rules and how to call the ethics helpline does not necessarily mean employees will raise their voice when they see ethical issues in the workplace. Even if employees want to say something they often hesitate, worried that they may not be heard, or even worse, that voicing may lead to formal or informal retaliation. Overcoming this hesitation requires training to help employees learn how to voice their values with in-person, experiential practice in everyday workplace situations. More and more organizations are investing in this training, as a way to simultaneously support employees, reduce risk and proactively reshape their culture.

Another significant change in ethics training is a focus on helping senior leaders consider how their own ethical leadership shapes the culture. This requires leaders to examine the signals they send in their everyday behaviors, and how these signals make employees feel safe to voice ideas and concerns. In my training sessions with senior leaders, we use exercises that help them identify the leadership behaviors that create such trust, and those that may be counter-productive. We then redesign the everyday processes, such as the weekly meeting or decision-making models, that encourage voice and explicitly elevate ethical concerns.

Third, more organizations are seeing the connection between ethics and greater sense of purpose in the workplace. Employee engagement, performance and retention often increases when employees feel they are contributing something beyond profit creation. Ethics training can help employees see this connection and practice the so-called giver strategies that help others, their organizations, and their own careers at the same time.

The article is here.