Factors Associated With the Quality of Life of Nursing Home Residents During the COVID-19 Pandemic: A Cross-Sectional Study

Objectives Quality of life (QoL) of nursing home (NH) residents is critical, yet understudied, particularly during the COVID-19 pandemic. Our objective was to examine whether COVID-19 outbreaks, lack of access to geriatric professionals, and care aide burnout were associated with NH residents' QoL. Design Cross-sectional study (July to December 2021). Setting and Participants We purposefully selected 9 NHs in Alberta, Canada, based on their COVID-19 exposure (no or minor/short outbreaks vs repeated or extensive outbreaks). We included data for 689 residents from 18 care units. Methods We used the DEMQOL-CH to assess resident QoL through video-based care aide interviews. Independent variables included a COVID-19 outbreak in the NH in the past 2 weeks (health authority records), care unit-levels of care aide burnout (9-item short-form Maslach Burnout Inventory), and resident access to geriatric professionals (validated facility survey). We ran mixed-effects regression models, adjusted for facility and care unit (validated surveys), and resident covariates (Resident Assessment Instrument–Minimum Data Set 2.0). Results Recent COVID-19 outbreaks (β = 0.189; 95% CI: 0.058–0.320), higher proportions of emotionally exhausted care aides on a care unit (β = 0.681; 95% CI: 0.246–1.115), and lack of access to geriatric professionals (β = 0.216; 95% CI: 0.003–0.428) were significantly associated with poorer resident QoL. Conclusions and Implications Policies aimed at reducing infection outbreaks, better supporting staff, and increasing access to specialist providers may help to mitigate how COVID-19 has negatively affected NH resident QoL

Neural processing of natural sounds

Natural sounds include animal vocalizations, environmental sounds such as wind, water and fire noises and non-vocal sounds made by animals and humans for communication. These natural sounds have characteristic statistical properties that make them perceptually salient and that drive auditory neurons in optimal regimes for information transmission.Recent advances in statistics and computer sciences have allowed neuro-physiologists to extract the stimulus-response function of complex auditory neurons from responses to natural sounds. These studies have shown a hierarchical processing that leads to the neural detection of progressively more complex natural sound features and have demonstrated the importance of the acoustical and behavioral contexts for the neural responses.High-level auditory neurons have shown to be exquisitely selective for conspecific calls. This fine selectivity could play an important role for species recognition, for vocal learning in songbirds and, in the case of the bats, for the processing of the sounds used in echolocation. Research that investigates how communication sounds are categorized into behaviorally meaningful groups (e.g. call types in animals, words in human speech) remains in its infancy.Animals and humans also excel at separating communication sounds from each other and from background noise. Neurons that detect communication calls in noise have been found but the neural computations involved in sound source separation and natural auditory scene analysis remain overall poorly understood. Thus, future auditory research will have to focus not only on how natural sounds are processed by the auditory system but also on the computations that allow for this processing to occur in natural listening situations.The complexity of the computations needed in the natural hearing task might require a high-dimensional representation provided by ensemble of neurons and the use of natural sounds might be the best solution for understanding the ensemble neural code