Assessing the prevalence of unmet need for genetic counseling in Canada and exploring associations with sociodemographic factors
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Large language models (LLMs) have emerged as transformative tools with the potential to revolutionize philosophical counseling. By harnessing their advanced natural language processing and reasoning capabilities, LLMs offer innovative solutions to overcome limitations inherent in traditional counseling approaches—such as counselor scarcity, difficulties in identifying mental health issues, subjective outcome assessment, and cultural adaptation challenges. In this study, we explore cutting‐edge technical strategies—including prompt engineering, fine‐tuning, and retrieval‐augmented generation—to integrate LLMs into the counseling process. Our analysis demonstrates that LLM-assisted systems can provide counselor recommendations, streamline session evaluations, broaden service accessibility, and improve cultural adaptation. We also critically examine challenges related to user trust, data privacy, and the inherent inability of current AI systems to genuinely understand or empathize. Overall, this work presents both theoretical insights and practical guidelines for the responsible development and deployment of AI-assisted philosophical counseling practices.
Commentary: Why is genetic testing underutilized worldwide? The case for hereditary breast cancer
It is thirty years since the BRCA1 and BRCA2 genes were discovered and genetic testing for BRCA1 and BRCA2 was introduced. Despite increasing awareness of the genetic basis of cancer and our evolving knowledge of effective means of prevention, screening, and treatment for hereditary breast and ovarian cancers, genetic testing is underutilized, and most mutation carriers remain unidentified. In this commentary, we explore possible reasons for why this might be so. Our focus is on factors that may influence or deter a patient from pursuing testing, rather than discussing the implications of receiving a positive test result. Issues of concern include an inadequate number of genetic counselors, restrictive (and conflicting) eligibility criteria for testing, the cost of the test, health insurance coverage, fear of future insurance discrimination, privacy issues, lack of familiarity with the testing process in primary care and gaps in both patient and provider knowledge about the impact and the value of testing. We discuss how these factors may lead to the underutilization of genetic testing in North America and throughout the world and discuss alternative models of genetic healthcare delivery. We have invited leaders in cancer genetic from around the world to tell us what they think are the barriers to testing in their host countries.
Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence
Much research in the behavioral sciences aims to characterize the “typical” person. A statistically significant group-averaged effect size is often interpreted as evidence that the typical person shows an effect, but that is only true under certain distributional assumptions for which explicit evidence is rarely presented. Mean effect size varies with both within-participant effect size and population prevalence (proportion of population showing effect). Few studies consider how prevalence affects mean effect size estimates and existing estimators of prevalence are, conversely, confounded by uncertainty about effect size. We introduce a widely applicable Bayesian method, the p-curve mixture model, that jointly estimates prevalence and effect size by probabilistically clustering participant-level data based on their likelihood under a null distribution. Our approach, for which we provide a software tool, outperforms existing prevalence estimation methods when effect size is uncertain and is sensitive to differences in prevalence or effect size across groups or conditions.
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Global burden of young-onset dementia, from 1990 to 2021: an age-period-cohort analysis from the global burden of disease study 2021
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