Physical therapy recruitment and retention strategies of hospitals in the US

Hospitals are seeing a reduction of physical therapy (PT) staff due to increased opportunities and competition. Planning effective recruitment and retention strategies for PTs in hospital settings may play an important role in reducing the problem. The primary purpose of this descriptive research was to compile information on recruitment and retention strategies used for physical therapists working in hospital settings. Four hundred surveys were mailed nationwide to hospital-based physical therapy managers. Strategies most commonly used were: attractive benefit package, interdisciplinary teams, competitive salaries, and student employment. The least used strategies used were: sign-on bonus, incentive pay programs, recruitment and retention committee and temporary staffing. It was concluded that hospital administrators need to analyze current strategies used and future recruitment and retention staffing trends, in order to institute successful strategies appropriate to their departments to effectively recruit and retain their staff.

Spatial-temporal mining of archived automatic vehicle location (AVL) data for transit service planning applications

An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^