Provider adherence to training components from the Trial to Reduce Antimicrobial use In Nursing home residents with Alzheimer's disease and other Dementias (TRAIN-AD) intervention

养老院阿尔茨海默病及其他痴呆症患者抗菌药物使用减少试验(TRAIN-AD)干预措施的提供者对培训内容的依从性

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Abstract

BACKGROUND: The Trial to Reduce Antimicrobial use In Nursing home residents with Alzheimer's disease and other Dementias (TRAIN-AD) was a cluster randomized clinical trial evaluating a multicomponent program to improve infection management among residents with advanced dementia. This report examines facility and provider characteristics associated with greater adherence to training components of the TRAIN-AD intervention. METHODS: Logistic regression was used to identify facility and provider characteristics associated with: 1. Training seminar attendance, 2. Online course completion, and 3. Overall adherence, defined as participation in neither seminar nor course, either seminar or course, or both seminar and course. RESULTS: Among 380 participating providers (nurses, N = 298; prescribing providers, N = 82) almost all (93%) participated in at least one training activity. Being a nurse was associated with higher likelihood of any seminar attendance (adjusted odds ratio (AOR) 5.37; 95% confidence interval (CI), 2.80-10.90). Providers who were in facilities when implementation begun (AOR, 3.01; 95% CI, 1.34-6.78) and in facilities with better quality ratings (AOR, 2.70; 95% CI, 1.59-4.57) were more likely to complete the online course. Prevalent participation (AOR, 2.01; 95% CI, 1.02-3.96) and higher facility quality (AOR, 2.44; 95% CI, 1.27-4.66) were also significantly associated with greater adherence to either seminar or online course. CONCLUSION: TRAIN-AD demonstrates feasibility in achieving high participation among nursing home providers in intervention training activities. Findings also suggest opportunities to maximize adherence, such as enhancing training efforts in lower quality facilities and targeting of providers who join the facility after implementation start-up.

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