RESUMO
Objective Starting from the actual numbers of health personnel of tertiary public hospitals of Traditional Chinese Medicine(TCM),to quantitatively analyze the influencing factors on the allocation of human resources and obtain a prediction model.Methods The balanced panel data from 517 Tertiary Public TCM Hospitals in the period of 2011-2020 were collected,and the two-way fixed effects model was used to empirically analyze the impact of scale,demand and other factors on the actual number of health personnel in these hospitals.Result The number of beds is a key factor affecting the human resource allocation of Public TCM Hospitals,and various factors such as de-mand,policy,price,efficiency,and administrative management also have significant impacts on the allocation.The demand for outpatient services,government financial support,and efficiency of resource utilization are all promoting factors,while the increase in human resource prices,income generation efficiency,and administrative manage-ment levels have negative effects.A prediction model is proposed.Conclusion The planning principle of matching bed numbers with human resources allocation is in line with the actual environment.When predicting the total personnel allocation or authorized strength,various factors should also be fully considered,which can provide reference for the formulation of human resource policies in Public TCM Hospitals.
RESUMO
AO_SCPLOWBSTRACTC_SCPLOWThe importance of pandemic forecast cannot be overemphasized. We propose an interpretable machine learning approach for forecasting pandemic transmission rates by utilizing local mobility statistics and government policies. A calibration step is introduced to deal with time-varying relationships between transmission rates and predictors. Experimental results demonstrate that our approach is able to make accurate two-week ahead predictions of the state-level COVID-19 infection trends in the US. Moreover, the models trained by our approach offer insights into the spread of COVID-19, such as the association between the baseline transmission rate and the state-level demographics, the effectiveness of local policies in reducing COVID-19 infections, and so on. This work provides a good understanding of COVID-19 evolution with respect to state-level characteristics and can potentially inform local policymakers in devising customized response strategies.
RESUMO
BACKGROUND AND OBJECTIVES: We tested long-acting injectable depot naltrexone for its tolerability, pharmacokinetics, and safety in Phase I. METHODS: The Phase I trial enrolled 36 healthy participants in two panels (A, B). In Panel A, 24 subjects were randomly assigned to the high-dosage group (400 mg naltrexone, n=6; placebo, n=6) or low-dosage group (200 mg naltrexone, n=6; placebo, n=6). In Panel B, 12 subjects were randomized to take six doses of monthly injectable naltrexone (400 mg) or placebo. RESULTS: After a single injection of naltrexone 200 and 400 mg, means (SD) of naltrexone plasma concentrations were .57 (.28) ng/ml and 1.5 (.8) ng/ml 30 days post-injection. There was no effect of accumulation after multiple dosing. Eleven of 30 subjects (36.67%) who were administered injectable depot naltrexone reported a total of 12 adverse events (AEs). Seven of these 11 AEs were coded as possibly related with study medication. All treatment-related AEs were mild in severity. No serious treatment-related AEs occurred. DISCUSSION AND CONCLUSIONS: This long-acting formulation of injectable depot naltrexone is well tolerated, results in constant plasma concentration of naltrexone for at least 1 month. SCIENTIFIC SIGNIFICANCE: The tolerability and safety of long-acting injectable depot naltrexone are good.
