지체장애근로자의 직업성공 요인에 관한 연구 (A study on the factors to affect the career success among workers with disabilities)
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- 한국사회복지학회:학술대회논문집
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- 한국사회복지학회 2003년도 추계학술대회 자료집
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- pp.185-216
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- 2003
본 연구에서 지체장애근로자들의 직업성공을 구성하는 요인들을 분석하고 이들 요인이 직업성공과 이직에 영향을 미치는 정도를 조사하여 가설적 이론모형을 검증하려는 목적을 두었다. 이를 위해서 지체장애를 가진 근로자 374명과 일반근로자 453명을 대상으로 구조화된 설문지를 통해 나타난 주요 연구결과들은 다음과 같이 요약되었다. 첫째, 장애근로자와 일반근로자의 직업성공을 구성하는 요인은 개인, 가정, 조직의 측면에서 나타났다. 개인적인 측면은 자아존중감과 자아효능감으로 구성되었고, 가족적인 측면은 다중역할스트레스와 자녀의 수로 구성되었다. 조직적인 측면은 자원활용능력, 네트워킹, 그리고 조언자로 구성되었다 이 밖에도 주관적인 직업성공과 객관적인 직업성공이라는 잠재변수에서 종 10개의 측정변수가 도출되었다. 둘째, 장애근로자와 일반근로자 집단 모두 직종이 직업성공에 영향을 미치는 것으로 조사되었다. 관측변수에서는 두 집단에서 직업성공에 영향을 주는 변수가 서로 다르게 나왔다. 장애근로자집단은 이직을 했을 때 평균적으로 근속한 년수와 임금을 제외하고 나머지 모든 관측변수에서 영향을 미치는 것으로 나타났으며, 일반집단은 조언자와 근속년수를 제외하고 나머지 모든 관측변수에서 영향을 미치는 것으로 조사되었다. 셋째, 장애근로자와 일반근로자 집단 모두 연령과 이직 경험이 이직 (이직횟수)에 영향을 미치는 것으로 나타났다. 그러나 장애집단은 친구의 수, 일반집단은 직업선택 시 중요하게 고려하는 사망이 각각 이직에 강한 영향을 미치는 것으로 조사되어 두 집단의 자이를 보여주었다. 또한 관측변수에서도 장애집단은 배우자의 직업과 근속년수, 일반집단은 다중역할 스트레스와 이직평균 근무년수에서 각기 다르게 이직에 영향을 미치는 것으로 나타났다. 넷째, 가설적 경로모형을 검증한 결과 제 1모형은 어느 정도 타당하고 직업성공을 예측할 수 있는 것으로 나타났으며, 제 2모형은 카이스퀘어와 자유도 (
도시 보건소 직원의 보건소 업무에 대한 인식 및 태도를 파악하기 위하여 대구직할시 7개 보건소 직원 310명을 대상으로 1994년 8월 15일부터 9월 30일까지 설문조사를 실시하여 252명(회수율 81.3%)의 자료를 분석하여 다음과 같은 결과를 얻었다. 조사대상은 남자가 95명(37.3%), 여자가 157명(62.3%)이고, 60.3%가 대졸이상자였다. 현재 근무부서의 시설이 보건사업을 수행하는데 적합하다고 한 의견이 28.6%, 적합하지 않다가 51.1%였고, 보유 기자재가 사업수행에 적합하다가 19.4%, 적합하지 않다가 39.0%였으며, 보건소의 인력수가 적정하다가 28.6%, 적합하지 않다가 44.8%였다. 근무부서의 예산이 보건사업 수행에 적합하다고 한 의견이 13.1%, 적합하지 않다가 38.5%였다. 지방자치제 실시후 사업내용이 바뀌어야 한다고 한 의견이 51.9%, 지방자치제의 실시가 자신의 근무부서의 업무에 도움이 된다고 한 의견이 25.4%, 도움되지 않는다가 24.6%였다. 지방자치제 실시에 따라 보건소의 조직과 기능이 개선되어야 한다는 의견은 78.6%였다. 사업 목표량의 설정이 해당 부서나 지역의 실정에 비추어 맞게 책정되어 있다는 의견이 11.1%, '그렇지 않다'가 43.3%였다. 업무 수행을 위한 전문적인 지식이나 기술에 대한 교육을 더 받아야 한다고 한 의견이 57.5%, 더 받을 필요없다가 20.6%였고, 자신의 업무수행에 자율성이 있다고 생각하는 견해가 35.7%, 자율성이 없다가 25.8%였으며, 현재 하고 일에 만족한다가 39.3%, 만족하지 못한다가 16.3%였다. 보건소의 인사관리에 대해서는 11.5% 합리적이라고 하였고, 47.3%가 불합리적 이라고 하였으며, 보건소가 주민들로부터 신뢰를 받고 있다는 의견이 41.3%, '그렇지 않다'는 의견이 13.1%였다. 보건소에서 지역주민에게 제공하는 서비스 중에서 잘 시행되고 있는 사업은 결핵관리, 일반진료, 모자보건사업의 순이었으며, 부족한 사업은 보건교육, 치과진료, 위생, 통합보건사업의 순이었다. 향후 보건소에서 주민에게 제공해야 할 서비스로는 노인보건사업, 가정의료사업, 재활보건사업, 당뇨병관리, 고혈압관리, 학교보건사업, 정신보건사업의 순으로 지적하였다. 보건소 근무자들은 시설, 기자재, 인력, 예산, 인사관리, 사업목표량의 설정 및 평가, 인사관리 등에 대해서는 부정적인 의견이 많았으며, 업무수행을 위한 보수교육, 지방자치제 실시를 통한 업무의 변화, 업무의 자율성, 업무의 만족도 면에서는 대체로 긍정적인 의견을 가진 것으로 나타났다.
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.