17 resultados para Occupational light vehicle use


Relevância:

30.00% 30.00%

Publicador:

Resumo:

A case-referent study of occupational injuries sustained by 474 workers employed in the heavy equipment machinery industry over a two year period, 1985-1986, was undertaken to examine the association of occupational injuries with non-work-related morbidity. Its specific aim was to evaluate whether employees who experienced a work-related injury had an increased prevalence of non-work-related morbidity, specifically for injuries, cardiovascular disease, mental disorders, all other disease outcomes and total morbidity, compared to employees who did not experience a work-related injury. In order to determine the direction of the relationship, the use of the previous calendar year was employed to assess non-work-related morbidity. A secondary objective of the study was the evaluation of the utility of two existing data sources, workers' compensation and group health insurance claims, and the feasibility of conducting studies based on these data.^ The association of non-work-related non-back injuries and subsequent occupational injury was statistically significant (OR = 1.31, 95% CI 1.02-1.67) for all WC claims. The strength of the association was supported by the elevated odds ratio for non-work-related injuries when severity of occupational injury was assessed by WC claim costs of $100 and greater (OR = 1.47, 1.09--1.97), and by lost workdays (OR = 1.37). Factors that predispose an individual to a non-back injury, such as personal attributes and lifestyle characteristics, also influence that individual's risk of subsequent occupational injury. These factors may be reflected in an employee's reaction to life stressors which influence susceptibility to injury. The role of employee assistance programs as a component of injury prevention strategies is suggested.^ An increased but nonsignificant prevalence of non-work-related injuries, cardiovascular disease, mental disorders, and other morbidity conditions was noted among cases. These findings do not provide support of a causal factor in the etiology of occupational injuries. In contrast to non-back injuries, these conditions are chronic in nature and their influence on risk of occupational injuries uncertain.^ In general, cases tended to file more group health insurance claims for other morbidity than did referents. The association with increased total morbidity was consistent whether worker compensation claims were analyzed by total number of claims, claims with costs of $100 and greater, or by lost workdays. Whether persons who sustained an occupational injury were in fact in poor general health than referents, warrant further investigation. ^

Relevância:

30.00% 30.00%

Publicador:

Resumo:

The Health Belief Model (HBM) provided the theoretical framework for examining Universal Precautions (UP) compliance factors by Firefighter, EMTs and Paramedics (prehospital care providers). A convenient sample of prehospital care providers (n = 4000) from two cities (Houston and Washington DC), were surveyed to explore the factors related to their decision to comply with Universal Precautions. Eight hundred and sixty-five useable questionnaires were analyzed. The responders were primarily male (95.7%) eight hundred and twenty-eight and thirty-seven were female, prehospital based (100%), EMTs (60.0%) and paramedics (12.8%) who had a mean 13 years of prehospital care experience. ^ Linear regression was used to evaluate the four hypotheses. The first hypothesis evaluating perceived susceptibility and seriousness with reported UP use was statistically significant (p = < .05). Perceived susceptibility, when considered independently, did not make a significant contribution (t = −4.2852; p = 0.0000) to the stated use of Universal precautions. The hypothesis is not supported as stated. The data indicates the opposite effect. Supported is the premise that as perceived susceptibility and perceived seriousness increase the use of Universal Precautions decreases. Hypothesis two tested perceived benefits with internal and external barriers. Both perceived benefits and internal and external barriers as well as the overall regression were significant (F = 112.6, p = 0.0000). The contribution of internal and external barriers was statistically significant (t = 0.0175; p = 0.0000) and (t = 0.0128; p = 0.0000). Hypothesis three which tested modifying factors, cues to action, select demographic variables, and the main effects of the HBM with self reported UP compliance overall was significant. The variables gender, birth, education, job type, EMS certification, years of service, years of experience providing patient care, Universal Precautions training hours, type of apparatus assigned to and the number of EMS related incidents responded to in a month were found to have a significant contribution to the stated use of Universal Precautions. ^ The additive effects were tested by use of a stepwise regression that assessed the contribution of each of the significant variables. Three variables in the equation were statistically significant. Internal barriers (t = −8.5507; p = 0.0000), external barriers (t = −6.2862; p = 0.000) and job type 2 & 3. Job type two (t = −2.8464; p = 0.0045 is titled Engineer/Operator. Job type three (t = −2.5730; p = 0.0103) is titled captain. The overall regression was significant (F = 24.06; p = 0.000). The Hypothesis is supported in the certain demographic variables do influence the stated use of Universal precautions and that as internal and external barriers are decreased, there is an increase in the stated use of Universal Precautions. ^ In summary, this study demonstrated that internal and external barriers have a significant impact on the stated use of Universal Precautions. Internal barriers are those factors within the individual that require an internal change (i.e., forgetfulness, freedom, perception of the urgency of the patient's needs etc.) and external barriers are things in the environment that can be altered (i.e., equipment design, availability of equipment, ease of use). These two model variables explained 23%–30% of the variance. ^