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Perceived Stress Among Students of Private and Public Sector Medical Colleges of Pakistan: A Cross Sectional Study
- M. Fatima, Z. Mehdi, S. Saeed, A. Nisar, M. Zain, J. Binte Shakir, I. Aamer, F. Arain, M. Jawad, N. Aziz
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- Journal:
- European Psychiatry / Volume 65 / Issue S1 / June 2022
- Published online by Cambridge University Press:
- 01 September 2022, pp. S303-S304
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- Article
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- You have access Access
- Open access
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Introduction
Medical-education is associated with high overall stress and it is important to identify relevant factors.
ObjectivesThe study was aimed to discern the differences in perceived stress among the students of public and private medical colleges of Pakistan and to identify factors subservient to any hypothesized difference.
MethodsThis cross-sectional study was conducted at different private and public medical colleges of Pakistan using validated tools: PSS-14 (Perceived Stress Scale) to find out the levels of stress faced by each sector and MSSQ (Medical Student Stressor Questionnaire) to determine the factors associated with increased stress.
ResultsTotal of 424 medical students from various public and private medical colleges of Pakistan (212 each) filled the questionnaires. The mean score +/- SD of PSS-14 was 36.17 ± 6.096 for the public sector and 36.29 ±5.732 for the private sector. Hence, there was no difference between the two comparative means of PSS score, t(422)=-0.213,p=0.831.The results for both sectors were classified as high perceived stress (27-40 score is high perceived stress). Out of 40 individual stress-causing factors in MSSQ, the students from private-sector scored higher as compared to public-sector: Quota System in examination t(422)=-3.951,p=0.000, stress caused by lack of time for friends and family t(422)=-3.225,p=0.001, stress caused by Tests/Examination t(422)=-2.131,p=0.034, stress caused by the parental wish for them to study medicine t(422)=-2.346,p=0.019 and stress caused by fear of getting poor marks t(422)=-2.183,p=0.030.
ConclusionsThere exists no overall difference in the perceived-stress among the medical students of public and private medical colleges despite private-sectors having significantly more operational financial resources.
DisclosureNo significant relationships.
18 - Energy efficient heterogeneous networks
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- By Y. Qi, University of Surrey, M. A. Imran, University of Surrey, M. Z. Shakir, Texas A&M University at Qatar, K. A. Qaraqe, Texas A&M University at Qatar
- Edited by Alagan Anpalagan, Ryerson Polytechnic University, Toronto, Mehdi Bennis, University of Oulu, Finland, Rath Vannithamby
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- Book:
- Design and Deployment of Small Cell Networks
- Published online:
- 05 December 2015
- Print publication:
- 17 December 2015, pp 462-483
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- Chapter
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Summary
Introduction
This chapter introduces novel approaches in heterogeneous networks (HetNets) where both large and small cells are deployed in a mixed manner to satisfy the increasing traffic demand and, at the same time, to improve the energy efficiency (EE) of future cellular networks.
In recent years, there has been a tremendous increase in the number of mobile handsets, in particular smart phones, supporting a wide range of applications, such as image and video transfer, cloud services, and cloud storage. The average smart phone usage rate has nearly been tripled and the overall amount of mobile data traffic demand grew 2.3 times in 2011 [1]. Furthermore, the amount of mobile data traffic is expected to increase dramatically in the coming years; recent forecasts are expecting the data traffic to increase more than 500 times in the next ten years [2, 3]. The current cellular systems would not be able to cope with the expected traffic demand increase. This huge amount of traffic demand leads to the need for further densification of the networks, for example in hotspot areas where traffic demand is concentrated as seen in Figure 18.1.
However, traffic load varies from time to time because of the typical night–day behavior due to the users’ daily activities in offices and being back to residential areas during the night [4]. In the current cellular networks, the power consumption of the radio access network (RAN) does not effectively scale with the traffic variations as shown in Figure 18.2. The traffic variations create the opportunities for the design of an adaptive network paradigm that can dynamically scale its power consumption according to the traffic variations.
Generally speaking, the power consumption of the RAN scales with the number of deployed base stations (BSs), each with offset power consumption. In cellular networks, only 10% of the overall power consumption stems from the user equipments (UEs) whereas nearly 90% of power consumption is incurred by the operator networks [5]. Figure 18.3 gives an idea on how the power consumption is distributed across the different parts of a typical cellular network. It is obvious that the RAN and the operation of data centers that provide computations, storage, applications, and data transfer are the most energy intensive parts of the entire network.
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