Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, education, employment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature. Given the globally aging population and proliferation of AI, there is a need to critically examine the presence of age-related bias in AI systems. This forum article discusses ageism in AI systems and introduces a conceptual model that outlines intersecting pathways of technology development that can produce and reinforce digital ageism in AI systems. We also describe the broader ethical and legal implications and considerations for future directions in digital ageism research to advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader cycles of injustice.

The purpose of this theoretical paper is to propose an integrated model of nurse/patient empowerment that could be used as a guide for creating high-quality nursing practice work environments that ensure positive outcomes for both nurses and their patients. There are few integrated theoretical approaches to nurse and patient empowerment in the literature, although nurse empowerment is assumed to positively affect patient outcomes. The constructs described in Kanter's (1993) work empowerment theory are conceptually consistent with the nursing care process and can be logically extended to nurses' interactions with their patients and the outcomes of nursing care. We propose a model of nurse/patient empowerment derived from Kanter's theory that suggests that empowering working conditions increase feelings of psychological empowerment in nurses, resulting in greater use of patient empowerment strategies by nurses, and, ultimately, greater patient empowerment and better health outcomes. Empirical testing of the model is recommended prior to use of the model in clinical practice. We argue that empowered nurses are more likely to empower their patients, which results in better patient and system outcomes. Strategies for managers to empower nurses and for nurses to empower patients are suggested.

There are widespread concerns about bias and discriminatory output related to artificial intelligence (AI), which may propagate social biases and disparities. Digital ageism refers to ageism reflected design, development, and implementation of AI systems and technologies and its resultant data. Currently, the prevalence of digital ageism and the sources of AI bias are unknown. A scoping review informed by the Arksey and O’Malley methodology was undertaken to explore age-related bias in AI systems, identify how AI systems encode, produce, or reinforce age-related bias, what is known about digital ageism, and the social, ethical and legal implications of age-related bias. A comprehensive search strategy that included five electronic bases and grey literature sources including legal sources was conducted. A framework of machine learning biases spanning from data to user by Mehrabi et al. is used to present the findings (Mehrabi et al. 2021). The academic search resulted in 7595 articles that were screened according to the inclusion criteria, of which 307 were included for full-text screening, and 49 were included in this review. The grey literature search resulted in 2639 documents screened, of which 235 were included for full text screening, and 25 were found to be relevant to the research questions pertaining to age and AI. As a result, a total of 74 documents were included in this review. The results show that the most common AI applications that intersected with age were age recognition and facial recognition systems. The most frequent machine learning algorithms used were convolutional neural networks and support vector machines. Bias was most frequently introduced in the early ‘data to algorithm’ phase in machine learning and the ‘algorithm to user’ phase specifically with representation bias (n = 33) and evaluation bias (n = 29), respectively (Mehrabi et al. 2021). The review concludes with a discussion of the ethical implications for the field of AI and recommendations for future research.

Climate change is a threat to the mental and emotional wellbeing of all humans, but young people are particularly vulnerable. Emerging evidence has found that young people's awareness of climate change and the danger it poses to the planet can lead to negative emotions. To increase our understanding about this, survey instruments are needed that measure the negative emotions young people experience about climate change. (1) What survey instruments are used to measure negative emotional responses to climate change in young people? (2) Do survey instruments measuring young people's negative emotional responses to climate change have evidence of reliability and validity? (3) What factors are associated with young people's negative emotional responses to climate change? A systematic review was conducted by searching seven academic databases on November 30, 2021, with an update on March 31, 2022. The search strategy was structured to capture three elements through various keywords and search terms: (1) negative emotions, (2) climate change, and (3) surveys. A total of 43 manuscripts met the study inclusion criteria. Among the 43 manuscripts, 28% focused specifically on young people, while the other studies included young people in the sample but did not focus exclusively on this population. The number of studies using surveys to examine negative emotional responses to climate change among young people has increased substantially since 2020. Survey instruments that examined worry or concern about climate change were the most common. Despite growing interest in climate change emotions among young people, there is a lack of research on the validity of measures of such emotions. Further efforts to develop survey instruments geared to operationalize the emotions that young people are experiencing in relation to climate change are needed.

Background: Health practitioner regulation (HPR) systems are increasingly recognized as playing an important role in supporting health workforce availability, accessibility, quality, and sustainability, while promoting patient safety. This review aimed to identify evidence on the design, delivery and effectiveness of HPR to inform policy decisions.Methods: We conducted an integrative analysis of literature published between 2010 and 2021. Fourteen databases were systematically searched, with data extracted and synthesized based on a modified Donabedian framework.Findings: This large-scale review synthesized evidence from a range of academic (n = 410) and grey literature (n = 426) relevant to HPR. We identified key themes and findings for a series of HPR topics organized according to our structures–processes–outcomes conceptual framework. Governance reforms in HPR are shifting towards multi-profession regulators, enhanced accountability, and risk-based approaches; however, comparisons between HPR models were complicated by a lack of a standardized HPR typology. HPR can support government workforce strategies, despite persisting challenges in cross-border recognition of qualifications and portability of registration. Scope of practice reform adapted to modern health systems can improve access and quality. Alternatives to statutory registration for lower-risk health occupations can improve services and protect the public, while standardized evaluation frameworks can aid regulatory strengthening. Knowledge gaps remain around the outcomes and effectiveness of HPR processes, including continuing professional development models, national licensing examinations, accreditation of health practitioner education programs, mandatory reporting obligations, remediation programs, and statutory registration of traditional and complementary medicine practitioners.Conclusion: We identified key themes, issues, and evidence gaps valuable for governments, regulators, and health system leaders. We also identified evidence base limitations that warrant caution when interpreting and generalizing the results across jurisdictions and professions. Themes and findings reflect interests and concerns in high-income Anglophone countries where most literature originated. Most studies were descriptive, resulting in a low certainty of evidence. To inform regulatory design and reform, research funders and governments should prioritize evidence on regulatory outcomes, including innovative approaches we identified in our review. Additionally, a systematic approach is needed to track and evaluate the impact of regulatory interventions and innovations on achieving health workforce and health systems goals.

Background: Artificial intelligence (AI) has emerged as a major driver of technological development in the 21st century, yet little attention has been paid to algorithmic biases toward older adults. Objective: This paper documents the search strategy and process for a scoping review exploring how age-related bias is encoded or amplified in AI systems as well as the corresponding legal and ethical implications. Methods: The scoping review follows a 6-stage methodology framework developed by Arksey and O’Malley. The search strategy has been established in 6 databases. We will investigate the legal implications of ageism in AI by searching grey literature databases, targeted websites, and popular search engines and using an iterative search strategy. Studies meet the inclusion criteria if they are in English, peer-reviewed, available electronically in full text, and meet one of the following two additional criteria: (1) include “bias” related to AI in any application (eg, facial recognition) and (2) discuss bias related to the concept of old age or ageism. At least two reviewers will independently conduct the title, abstract, and full-text screening. Search results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guideline. We will chart data on a structured form and conduct a thematic analysis to highlight the societal, legal, and ethical implications reported in the literature. Results: The database searches resulted in 7595 records when the searches were piloted in November 2021. The scoping review will be completed by December 2022. Conclusions: The findings will provide interdisciplinary insights into the extent of age-related bias in AI systems. The results will contribute foundational knowledge that can encourage multisectoral cooperation to ensure that AI is developed and deployed in a manner consistent with ethical values and human rights legislation as it relates to an older and aging population. We will publish the review findings in peer-reviewed journals and disseminate the key results with stakeholders via workshops and webinars. Trial Registration: OSF Registries AMG5P; https://osf.io/amg5p International Registered Report Identifier (IRRID): DERR1-10.2196/33211