LGBTQIA+ Cultural Competence in Physical Therapy: An Exploratory Qualitative Study From the Clinician's Perspective.

OBJECTIVE: The purpose of this study was to understand the lesbian, gay, bisexual, transgender, queer, intersex, agender, and other gender and sexually diverse identities (LGBTQIA+) health care experience and associated cultural competence from the physical therapist perspective (physical therapist and physical therapist assistant). METHODS: An exploratory qualitative approach implementing semi-structured focus groups and private interviews was utilized. To further anonymity, researchers allowed subjects to keep their camera off on Zoom. An interview protocol included questions guided by Campinha-Bacote domains of cultural competence (cultural awareness, skill, knowledge, encounter, and desire) to collect individual experiences, stories, discussions, thoughts, and opinions. Physical therapist clinicians were recruited from the clinical education affiliation lists of Regis University and Thomas Jefferson University. Seventy-one practicing physical therapists from the USA agreed to be part of the study. RESULTS: Themes were organized using the Social Ecological Model Framework. Themes are in parentheses following each level of the Social Ecological Model and include intrapersonal level (psychological stress and implicit and explicit biases), interpersonal (acceptance and competency), organizational (experience), community (advocacy), and society and policy (explicit biases and policy). CONCLUSION: Cultural competence in physical therapy is influenced by intrapersonal, interpersonal, organizational, community, and social and policy factors. Themes of psychological stress, limited awareness, decreased acceptance, and competency as well as limited exposure and experience, and a lack of advocacy and broader societal and policy issues prevent adequate LGBTQIA+ cultural competency of physical therapist providers. Further research in the physical therapist profession is needed to elaborate on the student, educator, and patient perspectives and how this information informs the LGBTQIA+ cultural competence of clinicians. IMPACT: This project may have a significant impact on suggestions for the delivery of content for health profession education to best impact health equity goals and save lives. Implementation of this content may have a direct impact on health disparities in LGBTQIA+ populations by reducing stigma and discrimination from health care providers, thus improving quality of health care and decreasing rates of patient mortality for LGBTQIA+ individuals.

Publishing fast and slow: A path toward generalizability in psychology and AI.

Artificial intelligence (AI) shares many generalizability challenges with psychology. But the fields publish differently. AI publishes fast, through rapid preprint sharing and conference publications. Psychology publishes more slowly, but creates integrative reviews and meta-analyses. We discuss the complementary advantages of each strategy, and suggest that incorporating both types of strategies could lead to more generalizable research in both fields.

Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models.

Language is crucial for human intelligence, but what exactly is its role? We take language to be a part of a system for understanding and communicating about situations. In humans, these abilities emerge gradually from experience and depend on domain-general principles of biological neural networks: connection-based learning, distributed representation, and context-sensitive, mutual constraint satisfaction-based processing. Current artificial language processing systems rely on the same domain general principles, embodied in artificial neural networks. Indeed, recent progress in this field depends on query-based attention, which extends the ability of these systems to exploit context and has contributed to remarkable breakthroughs. Nevertheless, most current models focus exclusively on language-internal tasks, limiting their ability to perform tasks that depend on understanding situations. These systems also lack memory for the contents of prior situations outside of a fixed contextual span. We describe the organization of the brain's distributed understanding system, which includes a fast learning system that addresses the memory problem. We sketch a framework for future models of understanding drawing equally on cognitive neuroscience and artificial intelligence and exploiting query-based attention. We highlight relevant current directions and consider further developments needed to fully capture human-level language understanding in a computational system.

Is coding a relevant metaphor for building AI?

Brette contends that the neural coding metaphor is an invalid basis for theories of what the brain does. Here, we argue that it is an insufficient guide for building an artificial intelligence that learns to accomplish short- and long-term goals in a complex, changing environment.

Adaptive Communication: Languages with More Non-Native Speakers Tend to Have Fewer Word Forms.

Explaining the diversity of languages across the world is one of the central aims of typological, historical, and evolutionary linguistics. We consider the effect of language contact-the number of non-native speakers a language has-on the way languages change and evolve. By analysing hundreds of languages within and across language families, regions, and text types, we show that languages with greater levels of contact typically employ fewer word forms to encode the same information content (a property we refer to as lexical diversity). Based on three types of statistical analyses, we demonstrate that this variance can in part be explained by the impact of non-native speakers on information encoding strategies. Finally, we argue that languages are information encoding systems shaped by the varying needs of their speakers. Language evolution and change should be modeled as the co-evolution of multiple intertwined adaptive systems: On one hand, the structure of human societies and human learning capabilities, and on the other, the structure of language.

A quantitative empirical analysis of the abstract/concrete distinction.

This study presents original evidence that abstract and concrete concepts are organized and represented differently in the mind, based on analyses of thousands of concepts in publicly available data sets and computational resources. First, we show that abstract and concrete concepts have differing patterns of association with other concepts. Second, we test recent hypotheses that abstract concepts are organized according to association, whereas concrete concepts are organized according to (semantic) similarity. Third, we present evidence suggesting that concrete representations are more strongly feature-based than abstract concepts. We argue that degree of feature-based structure may fundamentally determine concreteness, and we discuss implications for cognitive and computational models of meaning.