Geographically mobile healthcare workers and the conditions of their travel: The perspectives of managers

Many healthcare workers are “on the road” traveling to and from fixed sites (eg, patients’/clients’ homes). Qualitative interviews with nine Nova Scotian managers of mobile healthcare workers explored the conditions of workers’ travel. Findings highlight challenges such as changing schedules, as well as positive features including flexibility over the travel schedule. Some managers noted worker mobility-related responsibilities including having to decide if travel is too dangerous due to poor weather. A few managers suggested that workers may not receive adequate economic reimbursement for travel costs (eg, wear and tear on vehicle), and in some instances, workers need to use a benefit (eg, vacation day) or are not paid if they cannot drive due to poor weather. Reported organizational supports for workers’ travel were variable. This research indicates a need for supportive mobility-related policies and practices across all organizations, including policies that cover economic costs related to travel for all workers.

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Road Pothole Detection using Deep Learning Classifiers

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f7349.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 563-570 ◽

Cited By ~ 1

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Data Collection ◽

Convolutional Neural Networks ◽

Road Maintenance ◽

The Road ◽

Wear And Tear ◽

On The Road ◽

Maintenance Process ◽

Pothole Detection

Pothole is one of the major types of defects frequently found on the road whose assessment is necessary to process. It is one of the important reason of accidents on the road along with the wear and tear of vehicles. Road defects assessment is to be done through defects data collection and processing of this collected data. Currently, using various types of imaging systems data collection is near about becomes automated but an assessment of defects from collected data is still manual. Manual classification and evaluation of potholes are expensive, labour-intensive, time-consuming and thus slows down the overall road maintenance process. This paper describe a method for classification and detection of the potholes on road images using convolutional neural networks which are deep learning algorithms. In the proposed system we used convolutional neural networks based approach with pre-trained models to classify given input images into a pothole and non-pothole categories. The method was implemented in python using OpenCV library under windows and colab environment, trained on 722 and tested on 116 raw images. The results are evaluated and compared for convolutional neural networks and various seven pre-trained models through accuracy, precision and recall metrics. The results show that pre-trained models InseptionResNetV2 and DenseNet201 can detect potholes on road images with reasonably good accuracy of 89.66%.

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