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Assisted Living, Residential Care, and Memory Care Staffing Level Dynamics: Oregon, 2017-2023
1Institute on Aging, College of Urban and Public Affairs, Portland State University, Portland, OR, USA.
Objectives:
To examine changes in staffing levels over time in Oregon assisted living and residential care (AL/RC) communities between 2017 and 2023.
Design:
Longitudinal study of licensed AL/RC communities.
Setting And Participants:
A total of 1720 setting-year observations from 535 individual AL/RC communities in Oregon between 2017 and 2023.
Methods:
Primary data were collected through the Oregon Community-Based Care (CBC) study. Staffing levels were estimated as care staff hours per resident per day and reported overall and by staff type, including registered nurses, licensed practical nurses, certified nursing assistants, certified medication aides, unlicensed care staff, life enrichment staff, and social workers. Fixed-effects linear regression models were estimated to account for the time-invariant characteristics in the panel data.
Results:
The mean care staff hours per resident per day was 3 hours and 30 minutes (range, 3 hours and 11 minutes to 4 hours and 1 minute), depending on the survey setting year. The mean occupancy rate was 77.5% and declined significantly from 80.3% to 72.2% during the pandemic (P < .001). Staffing levels (overall and by staff type) varied significantly across AL/RC settings. There was an increase in staffing levels immediately after the pandemic started. Most (64%) of the variation observed in staffing levels could be attributed to between-AL/RC differences. On average, total care staff levels were 19 minutes higher in 2021 and 28 minutes higher in 2023 compared with 2017.
Conclusions And Implications:
The wide range of staffing levels points to significant differences in operational structures, resource allocations, and resident characteristics across different communities, with implications for the quality of care provided to residents. Understanding the significant changes to staffing patterns (levels and mix) after the COVID-19 pandemic is crucial for developing policies to improve adaptability and resilience of AL/RC settings.
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