Moral resilience and moral injury of nurse leaders during crisis situations: A qualitative descriptive analysis

Bergman, A., Nelson, K., et al. (2024).

Nursing Management, 55(12), 16–26.

https://doi.org/10.1097/nmg.0000000000000196

Nurse leaders are a heterogeneous group encompassing a variety of roles, settings, and specialties. What ties these diverse professionals together is a common code of ethics. Nurse leaders apply the provisions of their code of ethics not only to patient scenarios, but also to their interactions with nursing colleagues who rely on their leaders as advocates for ethical nursing practice. Successful nurse leaders embody principles of professionalism, utilize effective communication and interpersonal skills, have a broad familiarity with the healthcare system and its nuances and complexity, and demonstrate skillful business acumen.

Despite their extensive training, nurse leaders have long been an underappreciated and largely unseen force maintaining the health of the healthcare systems and functioning as a safety net for both patients and nursing staff. However, nurse leaders are under more scrutiny and subject to extraordinary stressors related to the COVID-19 pandemic. Some of these stressors occurred due to the ethical challenges placed on leaders navigating an unprecedented pandemic. Others reflect long-standing patterns within healthcare and nursing that were exacerbated during the pandemic.

Unresolved ethical issues combined with unrelenting stress can lead to escalating degrees of moral suffering that undermines integrity and well-being. Moral injury (MI) occurs when an individual compromises personal or professional values, violating the individual's sense of right and wrong and causing this person to question their ability to navigate ethical concerns with integrity. Conversely, moral resilience (MR) is the capacity to restore or sustain integrity in response to ethical or moral adversity. MR includes six pillars: personal integrity, relational integrity, buoyancy, self-regulation/self-awareness, moral efficacy, and self-stewardship.

This research substudy aimed to explore the experiences and scenarios that exposed nurse leaders to MI during the COVID-19 pandemic and the strategies and solutions that nurse leaders employ to bolster their MR and integrity. This research strives to amplify their stories in the hopes of developing practical solutions to organizational, professional, and individual concerns rooted in their ethical values as nurse leaders.

The article is linked above.

This qualitative study examines the moral injury (MI) and moral resilience (MR) of nurse leaders during the COVID-19 pandemic. Researchers surveyed US nurse leaders, analyzing both quantitative MI and MR scores and qualitative responses exploring their experiences. Five key themes emerged: absent nursing voice, unsustainable workload, lack of leadership support, need for leadership capacity building, and prioritization of finances over patient care. The Reina Trust & Betrayal Model framed the analysis, revealing widespread broken trust impacting all three dimensions of trust: communication, character, and capability. The study concludes with recommendations to rebuild trust and address nurse leader well-being.

The Unpaid Toll: Quantifying the Public Health Impact of AI

Han, Y. et al.

arXiv:2412.06288 [cs.CY]

https://doi.org/10.48550/arXiv.2412.06288

Abstract

The surging demand for AI has led to a rapid expansion of energy-intensive data centers, impacting the environment through escalating carbon emissions and water consumption. While significant attention has been paid to AI's growing environmental footprint, the public health burden, a hidden toll of AI, has been largely overlooked. Specifically, AI's lifecycle, from chip manufacturing to data center operation, significantly degrades air quality through emissions of criteria air pollutants such as fine particulate matter, substantially impacting public health. This paper introduces a methodology to model pollutant emissions across AI's lifecycle, quantifying the public health impacts. Our findings reveal that training an AI model of the Llama3.1 scale can produce air pollutants equivalent to more than 10,000 round trips by car between Los Angeles and New York City. The total public health burden of U.S. data centers in 2030 is valued at up to more than $20 billion per year, double that of U.S. coal-based steelmaking and comparable to that of on-road emissions of California. Further, the public health costs unevenly impact economically disadvantaged communities, where the per-household health burden could be 200x more than that in less-impacted communities. We recommend adopting a standard reporting protocol for criteria air pollutants and the public health costs of AI, paying attention to all impacted communities, and implementing health-informed AI to mitigate adverse effects while promoting public health equity.

The research is linked above.

This research paper quantifies the previously overlooked public health consequences of artificial intelligence (AI), focusing on the air pollution generated throughout its lifecycle—from chip manufacturing to data center operation. The authors present a methodology for modeling pollutant emissions and their resulting health impacts, finding that AI's environmental footprint translates to substantial health costs, potentially exceeding $20 billion annually in the US by 2030 and disproportionately affecting low-income communities. This "hidden toll" of AI, the paper argues, necessitates standardized reporting protocols for air pollutants and health impacts, the development of "health-informed AI" to mitigate adverse effects, and a focus on achieving public health equity.

Psychologists could find the information in the sources valuable as it highlights the potential mental health consequences of socioeconomic disparities exacerbated by AI's environmental impact. The sources reveal that the health burden of AI, particularly from data centers, is unevenly distributed and disproportionately affects low-income communities. This raises concerns about increased stress, anxiety, and depression in these communities due to factors like higher exposure to air pollution, reduced access to healthcare, and financial strain from increased health costs. Understanding these psychological impacts could inform interventions and policies aimed at mitigating the negative mental health consequences of AI's growth, particularly for vulnerable populations.

Are Large Language Models More Empathetic than Humans?

Welivita, A., and Pu, P. (2024, June 7).

arXiv.org.

https://arxiv.org/html/2406.05063v1

Abstract

With the emergence of large language models (LLMs), investigating if they can surpass humans in areas such as emotion recognition and empathetic responding has become a focal point of research. This paper presents a comprehensive study exploring the empathetic responding capabilities of four state-of-the-art LLMs: GPT-4, LLaMA-2-70B-Chat, Gemini-1.0-Pro, and Mixtral-8x7B-Instruct in comparison to a human baseline. We engaged 1,000 participants in a between-subjects user study, assessing the empathetic quality of responses generated by humans and the four LLMs to 2,000 emotional dialogue prompts meticulously selected to cover a broad spectrum of 32 distinct positive and negative emotions. Our findings reveal a statistically significant superiority of the empathetic responding capability of LLMs over humans. GPT-4 emerged as the most empathetic, marking ≈31% increase in responses rated as Good compared to the human benchmark. It was followed by LLaMA-2, Mixtral-8x7B, and Gemini-Pro, which showed increases of approximately 24%, 21%, and 10% in Good ratings, respectively. We further analyzed the response ratings at a finer granularity and discovered that some LLMs are significantly better at responding to specific emotions compared to others. The suggested evaluation framework offers a scalable and adaptable approach for assessing the empathy of new LLMs, avoiding the need to replicate this study’s findings in future research.

The research is here.

Here are some thoughts:

The research presents a groundbreaking study exploring the empathetic responding capabilities of large language models (LLMs), specifically comparing GPT-4, LLaMA-2-70B-Chat, Gemini-1.0-Pro, and Mixtral-8x7B-Instruct against human responses. The researchers designed a comprehensive between-subjects user study involving 1,000 participants who evaluated responses to 2,000 emotional dialogue prompts covering 32 distinct emotions.

By utilizing the EmpatheticDialogues dataset, the study meticulously selected dialogue prompts to ensure equal distribution across positive and negative emotional spectrums. The researchers developed a nuanced approach to evaluating empathy, defining it through cognitive, affective, and compassionate components. They provided LLMs with specific instructions emphasizing the multifaceted nature of empathetic communication, which went beyond traditional linguistic proficiency to capture deeper emotional understanding.

The findings revealed statistically significant superiority in LLMs' empathetic responding capabilities. GPT-4 emerged as the most empathetic, demonstrating approximately a 31% increase in responses rated as "Good" compared to the human baseline. Other models like LLaMA-2, Mixtral-8x7B, and Gemini-Pro showed increases of 24%, 21%, and 10% respectively. Notably, the study also discovered that different LLMs exhibited varying capabilities in responding to specific emotions, highlighting the complexity of artificial empathy.

This research represents a significant advancement in understanding AI's potential for nuanced emotional communication, offering a scalable and adaptable framework for assessing empathy in emerging language models.

Moral agency under oppression

Hirji, S. (2024).

Philosophy and Phenomenological Research.

https://doi.org/10.1111/phpr.13105

Abstract

In Huckleberry Finn, a thirteen-year old white boy in antebellum Missouri escapes from his abusive father and befriends a runaway slave named Jim. On a familiar reading of the novel, both Huck and Jim are, in their own ways, morally impressive, transcending the unjust circumstances in which they find themselves in to treat each other as equals. Huck saves Jim's life from two men looking for runaway slaves, and later Jim risks his chance at freedom to save Huck's friend Tom. I want to complicate the idea that Huck and Jim are morally commendable for what they do. More generally, I want to explore how oppression undermines the moral agency of the oppressed, and to some degree, the oppressor. In §1 I take a careful look at Jim's choice, arguing that his enslavement compromises his moral agency. In §2 I show how Jim's oppression also shapes the extent to which Huck can be praiseworthy for his action. In §3, I consider the consequences for thinking about the moral agency of the oppressed, and in §4 I explore the limitations of the concept of moral worth for theorizing in cases of oppression.

The article is here.

Here are some thoughts: 

This article examines moral agency within the context of oppression, using Mark Twain's Huckleberry Finn as a case study. The author challenges the conventional interpretation of Huck and Jim's actions as morally commendable, arguing that Jim's enslavement fundamentally restricts his agency, regardless of his choices. This limitation, the author contends, also impacts the assessment of Huck's actions, suggesting his seemingly virtuous choices are inadvertently shaped by the system of oppression. The article further explores how established moral philosophical concepts inadequately address the complexities of moral agency under oppression, proposing a nuanced understanding that considers both capacity and the ability to fully express that capacity in action. Finally, the article broadens its scope to consider contemporary instances of oppression, demonstrating the persistent challenges to moral agency in various social contexts.

Incompetence & Losing Capacity: Answers to 8 FAQs

Leslie Kernisan

(2024, November 30). Better Health While Aging.

Link is here.

Perhaps your elderly father insists he has no difficulties driving, even though he’s gotten into some fender benders and you find yourself a bit uncomfortable when you ride in the car with him.

Or you’ve worried about your aging aunt giving an alarming amount of money to people who call her on the phone.

Or maybe it’s your older spouse, who has started refusing to take his medication, claiming that it’s poisoned because the neighbor is out to get him.

These situations are certainly concerning, and they often prompt families to ask me if they should be worried about an older adult becoming “incompetent.”

In response, I usually answer that we need to do at least two things:

• We should assess whether the person has “capacity” to make the decision in question.

• If there are signs concerning for memory or thinking problems, we should evaluate to determine what might be causing them.

If you’ve been concerned about an older person’s mental wellbeing or ability to make decisions, understanding what clinicians — and lawyers —mean by capacity is hugely important.

The info is here.

The website addresses concerns related to the decision-making capacity of older adults, particularly in light of cognitive impairments such as dementia. It emphasizes the importance of understanding "capacity," which refers to an individual's ability to make informed decisions about specific matters. Dr. Leslie Kernisan outlines that capacity is not a binary state; instead, it is decision-specific and can fluctuate based on health conditions. For example, an older adult may retain the capacity to make simple decisions but struggle with more complex ones, especially if they are experiencing health issues or cognitive decline.

Dr. Kernisan distinguishes between incapacity and incompetence, noting that capacity is typically assessed in clinical settings by healthcare professionals, while competence is a legal determination made by courts. The document explains that various types of decisions—such as medical consent, financial matters, and driving—require different capacities, and the legal standards for these capacities can vary by state.

The article also highlights the impact of Alzheimer's disease and other dementias on decision-making abilities. In early stages, individuals may still have the capacity for many decisions, but as the disease progresses, their ability to make even simple choices may diminish. Therefore, it is crucial for families to seek clinical assessments of capacity when there are concerns about an older adult's decision-making abilities.

Moreover, the document advises that legal determinations of incapacity may be necessary before overriding an older person's decisions, especially in matters concerning safety or financial well-being. Families are encouraged to consult with legal professionals when navigating these issues to ensure they are acting within legal and ethical boundaries.

Overall, the article serves as a practical guide for caregivers and family members dealing with the complexities of aging and cognitive decline, stressing the need for respectful communication and proactive measures to protect the autonomy and safety of older adults.

Artificial Intelligence (AI) Psychotherapy: Coming Soon to a Consultation Room Near You?

Swartz, H. A. (2023).

American Journal of Psychotherapy,

76(2), 55–56.

https://doi.org/10.1176/appi.psychotherapy.20230018

News stories about generative artificial intelligence (AI) abound, alternately predicting that AI will cause humanity’s demise and touting it as the solution to everything from climate change to health care scarcity. In 2022, OpenAI’s release of ChatGPT, an easy-to-use AI interface that produces fluent text in response to prompts, intensified highly ambivalent discussions about AI. Pundits and knowledgeable experts alike marveled at ChatGPT’s ability to produce text that resembles human writing while acknowledging that the speed of its evolution was outstripping our ability to understand its risks. This technology performed better on law school and medical school entrance exams than many human applicants, leading to predictions that AI will eventually render some professions obsolete. Psychotherapists, too, seem at risk for being replaced by AI. Are fears of AI supplanting psychotherapists reasonable?

To explore how AI might respond to a patient in distress, I typed this prompt into ChatGPT: “I feel sad. Can you help me?” It quickly generated the following text:

I am sorry to hear that you are feeling sad. I’ll do my best to help you. It’s important to remember that I’m an AI language model and may not fully understand or empathize with your emotions, but I can offer some general suggestions and support. . . . Remember, everyone experiences sadness from time to time, and it’s okay to seek help when you need it. You don’t have to face it alone.

Despite being impressed by the speed and conversational tone of ChatGPT’s text generation and the convenience of receiving an immediate response during hours when a human therapist would likely be unavailable, the formulaic exchange and canned feedback provided by ChatGPT left me with doubts about its ability to provide genuine soothing to humans experiencing depression. 

The information is here.

Here are some thoughts:

This editorial examines the potential of artificial intelligence (AI) in psychotherapy. While AI chatbots offer increased accessibility and convenience, providing self-help tools and improving symptom management, studies reveal limitations, including a lack of genuine human connection and potential risks like increased self-harm. The author concludes that AI is a useful supplementary tool, particularly in low-resource settings, but cannot replace human therapists for complex emotional and interpersonal issues. Ultimately, a blended approach incorporating both AI and human interaction is suggested for optimal therapeutic outcomes.

Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions

Luo, M., et al. (2024, May 26).

arXiv.org.

https://arxiv.org/abs/2405.16402

Abstract

The integration of Large Language Models (LLMs) into the healthcare domain has the potential to significantly enhance patient care and support through the development of empathetic, patient-facing chatbots. This study investigates an intriguing question Can ChatGPT respond with a greater degree of empathy than those typically offered by physicians? To answer this question, we collect a de-identified dataset of patient messages and physician responses from Mayo Clinic and generate alternative replies using ChatGPT. Our analyses incorporate novel empathy ranking evaluation (EMRank) involving both automated metrics and human assessments to gauge the empathy level of responses. Our findings indicate that LLM-powered chatbots have the potential to surpass human physicians in delivering empathetic communication, suggesting a promising avenue for enhancing patient care and reducing professional burnout. The study not only highlights the importance of empathy in patient interactions but also proposes a set of effective automatic empathy ranking metrics, paving the way for the broader adoption of LLMs in healthcare.

A preprint is here.

Here are some thoughts:

The research explores an innovative approach to assessing empathy in healthcare communication by comparing responses from physicians and ChatGPT, a large language model (LLM). The study focuses on prostate cancer patient interactions, utilizing a real-world dataset from Mayo Clinic to investigate whether AI-powered chatbots can potentially deliver more empathetic responses than human physicians.

The researchers developed a novel methodology called EMRank, which employs multiple evaluation techniques to measure empathy. This approach includes both automated metrics using LLaMA (another language model) and human assessments. By using zero-shot, one-shot, and few-shot learning strategies, they created a flexible framework for ranking empathetic communication that could be generalized across different healthcare domains.

Key findings suggest that LLM-powered chatbots like ChatGPT have significant potential to surpass human physicians in delivering empathetic communication. The study's unique contributions include using real patient data, developing innovative automatic empathy ranking metrics, and incorporating patient evaluations to validate the assessment methods. By demonstrating the capability of AI to generate compassionate responses, the research opens new avenues for enhancing patient care and potentially reducing professional burnout among healthcare providers.

The methodology carefully addressed privacy concerns by de-identifying patient and physician information, and controlled for response length to ensure a fair comparison. Ultimately, the study represents a promising step towards integrating artificial intelligence into healthcare communication, highlighting the potential of LLMs to provide supportive, empathetic interactions in medical contexts.

Negative economic shocks and the compliance to social norms

Bogliacino, F.,  et al. (2024).

Judgment and Decision Making, 19.

https://doi.org/10.1017/jdm.2024.1

Abstract

We study why suffering a negative economic shock, i.e., a significant loss, may trigger a change in other-regarding behavior. We conjecture that people trade off concern for money with a conditional preference to follow social norms and that suffering a shock makes extrinsic motivation more salient, leading to more norm violation. This hypothesis is grounded on the premise that preferences are norm-dependent. We study this question experimentally: after administering losses on the earnings from a real-effort task, we analyze choices in prosocial and antisocial settings. To derive our predictions, we elicit social norms for each context analyzed in the experiments. We find evidence that shock increases deviations from norms.

The research is linked above.

Here are some thoughts.

The research indicates another way in which moral norms shift based on context. The study investigates how experiencing significant financial losses, termed negative economic shocks (NES), influences individuals' adherence to social norms. The authors hypothesize that when individuals face NES, they become more focused on monetary concerns, leading to a higher likelihood of violating social norms. This hypothesis is grounded in the concept of norm-dependent utility, where individuals weigh the psychological costs of deviating from norms against their financial needs. The researchers conducted three experiments where participants experienced an 80% loss in earnings from a real-effort task and subsequently engaged in various tasks measuring norm compliance, including stealing, cheating, and cooperation.

The findings reveal that participants who experienced NES exhibited increased norm violations across several contexts. Specifically, there was a notable rise in stealing behaviors and a significant increase in cheating during the "die-under-the-cup" task. Additionally, the study found that retaliation behaviors decreased markedly in "joy of destruction" scenarios. Importantly, the results suggest that the effects of NES on social behavior are distinct from mere wealth effects, indicating that experiencing a shock alters individuals' motivations and decision-making processes. Overall, this research contributes valuable insights into the complex interplay between economic stressors and social behavior, highlighting how financial adversity can lead to deviations from established social norms.

Personalized progression modelling and prediction in Parkinson’s disease with a novel multi-modal graph approach

Lian, J., Luo, X., et al. (2024).

Npj Parkinson S Disease, 10(1).

doi.org/10.1038/s41531-024-00832-w

Abstract

Parkinson’s disease (PD) is a complex neurological disorder characterized by dopaminergic neuron degeneration, leading to diverse motor and non-motor impairments. This variability complicates accurate progression modelling and early-stage prediction. Traditional classification methods based on clinical symptoms are often limited by disease heterogeneity. This study introduces an graph-based interpretable personalized progression method, utilizing data from the Parkinson’s Progression Markers Initiative (PPMI) and Stroke Parkinson’s Disease Biomarker Program (PDBP). Our approach integrates multimodal inter-individual and intra-individual data, including clinical assessments, MRI, and genetic information to make multi-dimension predictions. Validated using the PDBP dataset from 12 to 36 months, our AdaMedGraph method demonstrated strong performance, achieving AUC values of 0.748 and 0.714 for the 12-month Hoehn and Yahr Scale and Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) III on the PPMI test set. Ablation analysis reveals the importance of baseline clinical assessment predictors. This novel framework improves personalized care and offers insights into unique disease trajectories in PD patients.

The research is here.

Here are some thoughts:

The research introduces AdaMedGraph, an innovative tool designed to model and predict the progression of Parkinson’s Disease (PD) using personalized, multimodal graph-based methods. This approach integrates clinical assessments, MRI imaging, and genetic data to create individualized disease trajectories. AdaMedGraph demonstrates superior predictive performance compared to traditional machine learning methods, achieving high accuracy for progression markers like the Hoehn and Yahr Scale (e.g., AUC 0.748) and effectively addressing the challenges of disease heterogeneity. Its ability to incorporate diverse data sources allows for detailed predictions across motor (e.g., rigidity, tremors) and non-motor symptoms (e.g., cognition, sleep patterns), which are critical for comprehensive PD management.

For psychologists, this research has significant implications. The integration of cognitive and behavioral assessments into the predictive framework underscores the importance of psychological evaluations in understanding PD progression. The model’s ability to identify personalized trajectories enables psychologists to tailor interventions for both motor and non-motor symptoms, enhancing patient-centered care. Furthermore, the study’s exploration of medication effects on symptoms provides valuable insights into the interaction between treatments and behavioral outcomes, informing therapeutic adjustments. By identifying critical predictive features and offering interpretable patient similarity analyses, AdaMedGraph aligns well with the emphasis on individualized care in psychological practice. This tool has the potential to advance multidisciplinary collaboration, enabling psychologists to combine behavioral insights with advanced predictive analytics to improve outcomes for individuals with Parkinson’s Disease.