| Project/Area Number |
15592324
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Community health/Gerontological nurisng
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| Research Institution | Tokyo Metropolitan University (2005)
Tokyo Metropolitan University of Health Sciences (2003-2004) |
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Principal Investigator |
NEKODA Yasutoshi Tokyo Metropolitan University, Faculty of Health Sciences, Professor, 健康福祉学部, 教授 (30180699)
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| Project Period (FY) |
2003 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 2005: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 2004: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2003: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | practical training in nursing / incidence / error / longitudinal study / cumulative incidence rate / 累積発生割合 / 看護学生 / 発生 / 調査票 / 分析方法 / 縦断的 |
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| Research Abstract |
In this research, we collected the information of occurrences of errors and incidences during practical training in nursing through longitudinal method. We analyzed the cases and also using epidemiological method, we calculated the cumulative incidences of errors and incidences.
For this objective, we designed the input format of information sheet and training rotation tables in text mode and newly developed a SAS program for calculating the cumulative incidences.
By the analysis of descriptive epidemiology, the new occurrences of errors and incidences were not evenly distributed through training course. Within one or two months just after the training started, we observed the peak of occurrences. Then gradually the occurrences decreased, and again the medium peak of occurrences were observed.
This time, we used the computer programmes for calculation. Using paper sheet or spread sheet of Ms Excel the same counting is possible. By our method, each nursing school could monitor the occurrences and inquire the cause of fluctuations of errors and incidences.
If we collect amount of cases, we can apply a quantitative analytic method developed through error sciences. We might be able to distinguish some structural factors in training. The cased we collected are good materials for learning the prevention of adverse events.
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