DeVivo M J, Rutt R D, Stover S L, Fine P R
Arch Phys Med Rehabil. 1987 Aug;68(8):494-8.
A predictive model for employment after spinal cord injury was developed. The study population consisted of 154 spinal cord injured persons who were treated at our hospital between 1973 and 1979, and followed for seven years after injury. Demographic, social, and injury severity data were abstracted from each subject's hospital record. Motivation to work, employment history, and sources of postinjury financial support, were assessed by a vocational rehabilitation counselor. The study population was divided into four groups: persons continuously unemployed after injury, homemakers, students, and those employed at some time during the seven-year follow-up period. Stepwise discriminant analysis was used to develop a predictive model that ultimately included seven variables: gender, motivation to work, whether the patient's last job required ambulation, race, educational level, a functional ability score, and whether the patient had children. The model correctly classified 82% of those persons who were continuously unemployed, 100% of homemakers, 63% of students, and 72% of employed subjects. Overall, 79% of subjects were classified correctly. The most important classification errors were between the unemployed and employed groups. Seventeen percent of employed patients were incorrectly classified as unemployed, and 11% of unemployed patients were incorrectly classified as employed. Although there are other determinants of postinjury vocational status, individual potential can be assessed by means of a comparatively small set of predictor variables.
开发了一种脊髓损伤后就业的预测模型。研究人群包括154名在1973年至1979年间在我院接受治疗的脊髓损伤患者,并在受伤后随访了七年。从每个受试者的医院记录中提取人口统计学、社会和损伤严重程度数据。工作动机、就业史和伤后经济支持来源由职业康复顾问进行评估。研究人群分为四组:受伤后持续失业者、家庭主妇、学生以及在七年随访期间某个时间就业的人。采用逐步判别分析来开发一个预测模型,该模型最终包括七个变量:性别、工作动机、患者上一份工作是否需要行走、种族、教育水平、功能能力得分以及患者是否有子女。该模型正确分类了82%的持续失业者、100%的家庭主妇、63%的学生和72%的就业受试者。总体而言,79%的受试者被正确分类。最重要的分类错误发生在失业和就业组之间。17%的就业患者被错误分类为失业,11%的失业患者被错误分类为就业。虽然伤后职业状况还有其他决定因素,但可以通过相对较少的一组预测变量来评估个人潜力。