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Objective To identify factors that predict admission to long‐term care (LTC) and mortality among community‐based, dependent older people in Ireland, who were in receipt of formal home support. Methods An audit was conducted of all community‐dwelling older adults receiving government funded home support during 2017 in the Dublin North Central, Health Service Executive administrative area. Data were extracted from the Common Summary Assessment Report (CSAR), a mandatory form used in the provision of home support. Multiple logistic regression analysis was used to examine the factors associated with admission to LTC and mortality, with the results presented as odds ratios (OR) and 95% confidence intervals. Results The audit comprised 1597 community‐dwelling older adults with a mean age of 83.3 (SD: 7.2) years. The prevalence of transition to LTC and mortality was 8% and 9%, respectively, during the 12‐month period. Factors significantly associated with admission to LTC were “cognitive dysfunction” [OR 2.10 (1.41‐3.14), P < .001] and the intensity of home support [OR 1.05 (1.01‐1.06), P < .003], as measured by weekly formal care hours. Physical dependency and advanced age (aged 95 years +) were significantly associated with mortality in this population (P < .001). Conclusion “Cognitive dysfunction” and intensity of formal home support were associated with transition to LTC, while physical dependency and advanced age were associated with mortality. Investment in personalised, cognitive‐specific, services and supports are necessary to keep people with dementia and related cognitive impairments living at home for longer.

Performance improved and the variability due to the initial conditions for training decreased with the number of hidden units. The effect of training set design on test set performance was also examined. The performance of a three-layered network was better than trained human listeners and the network generalized better than a nearest neighbor classifier.

The European Union Artificial Intelligence (AI) Act proposes to ban AI systems that ”manipulate persons through subliminal techniques or exploit the fragility of vulnerable individuals, and could potentially harm the manipulated individual or third person”. This article takes the perspective of cognitive psychology to analyze and understand what algorithmic manipulation consists of, who vulnerable individuals may be, and what is considered as harm. Subliminal techniques are expanded with concepts from behavioral science and the study of preference change. Individual psychometric differences which can be exploited are used to expand the concept of vulnerable individuals. The concept of harm is explored beyond physical and psychological harm to consider harm to one's time and right to an un-manipulated opinion. The paper offers policy recommendations that extend from the paper's analyses.

Objective To identify the factors associated with perceived COVID-19 risk among people living in the US. Methods A cross-sectional representative sample of 485 US residents was collected in mid-April 2020. Participants were asked about (a) perceptions of COVID-19 risk, (b) demographic factors known to be associated with increased COVID-19 risk, and (c) the impact of COVID-19 on different life domains. We used a three-step hierarchical linear regression model to assess the differential contribution of the factors listed above on perceived COVID-19 risk. Results The final model accounted for 16% of variability in perceived risk, F (18,458) = 4.8, p < .001. Participants who were White reported twice as much perceived risk as participants of color ( B = −2.1, 95% CI[−3.4,-0.8]. Higher perceived risk was observed among those who reported a negative impact of the pandemic on their sleep ( B = 1.5, 95% CI[0.8,2.1]) or work ( B = 0.7, 95%CI[0.1,1.3]). The number of cases per capita in their state of residence, age, or proximity to someone with a COVID-19 diagnosis were not found to meaningfully predict perceived risk. Conclusions Perceived risk was not found to be associated with known demographic risk factors, except that the effect of race/ethnicity was in the opposite direction of existing evidence. Perception of COVID-19 risk was associated with the perceived personal impact of the pandemic.