Training of Infection Prevention and Control to Healthcare Workers of Mining Health Facilities: A Shared Responsibility for Improving Safety of Mining Communities

Introduction: Without appropriately trained healthcare workers (HCWs), infection prevention and control (IPC) cannot be implemented according to set standards. Although training is crucial, authorities rarely consider those working in health facilities owned by the mining. We describe the training which was conducted in North Mara in Mara region mining health facilities. Methods: This was descriptive study on the training of IPC to HCWs of mining health facilities. The training was conducted to North Mara Gold Mine Limited on April 2024. We targeted the HCWs and supporting staff working in the health facilities of the mining communities. The duration of the training was five days. The sessions started with pre-training test to evaluate what participants understood before training and followed with training itself. The training was carried out using mixed adult learning methods like: illustrated lectures, demonstrations, brainstorming, small group activities, group discussions, role plays, case studies and simulations. The training was finalized with posttest. Results: A total of ten HCWs were trained out of 13 of the facility. In that training six were males and four were females. Also, out of the ten trained three were clinicians, four nurses, one lab technician, one pharmaceutical technician, one support staff. The average score of the results of the pretest was 70.7% with a range of 16% (minimum 64% and maximum 80%) and that of the posttest was 79.8% with a range of 12% (min 74% and max 88%). Conclusions: If HCWs are well trained to comply with IPC standards and transmission-based precautions, they have the ability to deliver safe health services and protect themselves, patients, environment and the community. Training of HCWs working at the mining, therefore, can be adapted in all mines to improve the quality of mining healthcare and respond to the need to improve the safety of mining communities.

Fast Community Detection Based on Distance Dynamics

The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale networks. To identify the community structure of a large-scale network with high speed and high quality, in this paper, we propose a fast community detection algorithm, the F-Attractor, which is based on the distance dynamics model. The main contributions of the F-Attractor are as follows. First, we propose the use of two prejudgment rules from two different perspectives： node and edge. Based on these two rules, we develop a strategy of internal edge prejudgment for predicting the internal edges of the network. Internal edge prejudgment can reduce the number of edges and their neighbors that participate in the distance dynamics model. Second, we introduce a triangle distance to further enhance the speed of the interaction process in the distance dynamics model. This triangle distance uses two known distances to measure a third distance without any extra computation. We combine the above techniques to improve the distance dynamics model and then describe the community detection process of the F-Attractor. The results of an extensive series of experiments demonstrate that the F-Attractor offers high-speed community detection and high partition quality.