Objective: To determine the factors associated with good knowledge and safe practices regarding occupational hazards among textile workers.
Methods: The cross-sectional study was conducted in Karachi from September 2015 to February 2016, and comprised male workers from seven textile mills. A 45-item structured questionnaire was developed and pretested in Urdu, the local language. One part of the questionnaire comprised 31 items related to good knowledge, and the other part comprised 14 items related to safe practices. Data was analysed using SPSS 19.
Results: Of the 300 subjects, 123(41%) were aged 18-27 years, 183(63%) were educated, 184(61.3%) worked more than eight hours daily, 170(57%) were employed in the weaving section and 164(55%) as machine operators. Besides, 231(77%) had good knowledge, and 62(21%) reported safe practices. Educated workers were more likely (p<0.05), and machine operators were less likely (p<0.05) to have good knowledge, while educated workers, those in the spinning section and those working less than 8-hours daily (p<0.05) were more likely to report safe practices.
Conclusions: There was a high knowledge level, but a wide gap in adopting safety practices, which indicates need for focused interventions targeting high-risk workers and regulation of working hours.
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http://dx.doi.org/10.5455/JPMA.302642179 | DOI Listing |
Appl Environ Microbiol
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Department of Ethics, Law & Humanities, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
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View Article and Find Full Text PDFJ Chem Phys
January 2025
Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt Lake, Kolkata 700106, India.
Estimating rare event kinetics from molecular dynamics simulations is a non-trivial task despite the great advances in enhanced sampling methods. Weighted Ensemble (WE) simulation, a special class of enhanced sampling techniques, offers a way to directly calculate kinetic rate constants from biased trajectories without the need to modify the underlying energy landscape using bias potentials. Conventional WE algorithms use different binning schemes to partition the collective variable (CV) space separating the two metastable states of interest.
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