Department of Education, Iowa Vocational Rehabilitation Services Performance Plan, FY 2009

Agency Performance Plan, Department of Education, Iowa Vocational Rehabilitation Services

Iowa Vocational Rehabilitation Services Strategic Plan, 2007-2010

This report outlines the strategic plan for Iowa Vocational Rehabilitation Services, goals and mission.

Electronic Payments for Driver's Licensing Transactions,December 31, 2008

House File 2196 required the Department of Transportation (DOT) to study the acceptance of electronic payments at its customer service sites and sites operated by county treasurers. Specifically the legislation requires the following: “The department of transportation shall review the current methods the department employs for the collection of fees and other revenues at sites operated by county treasurers under chapter 321M and at customer service sites operated by the department. In conducting its review, the department, in cooperation with the treasurer of state, shall consider providing an electronic payment option for all of its customers. The department shall report its findings and recommendations by December 31, 2008, to the senate and house standing committees on transportation regarding the advantages and disadvantages of implementing one or more electronic payment systems.” This review focused on estimating the costs of providing an electronic payment option for customers of the DOT driver’s license stations and those of the 81 county treasurers. Customers at these sites engage in three primary financial transactions for which acceptance of electronic payments was studied: paying for a driver’s license (DL), paying for a non-operator identification card (ID), and paying certain civil penalties. Both consumer credit cards and PIN-based debit cards were reviewed as electronic payment options. It was assumed that most transactions would be made using a consumer credit card. Credit card companies charge a fee for each transaction for which they are used. The amount of these fees varies among credit card companies. The estimates for credit card fees used in this study were based on the State Treasurer of Iowa’s current credit card contract, which is due to expire in September 2009. Since credit card companies adjust their fees each year, estimates were based on the 2008 fee schedule. There is also a fee for the use of PIN-based debit cards. The estimates for PIN-based debit card transactions were based on information provided by Wells Fargo Merchant Services for current fees charged by debit card networks. Credit and debit card transactions would be processed through vendor-provided hardware and software. The costs would be determined through the competitive bidding process since several vendors provide this function; therefore, these costs are not reflected in this document.

Metaheuristics for the bus-driver scheduling problem

We present new metaheuristics for solving real crew scheduling problemsin a public transportation bus company. Since the crews of thesecompanies are drivers, we will designate the problem by the bus-driverscheduling problem. Crew scheduling problems are well known and severalmathematical programming based techniques have been proposed to solvethem, in particular using the set-covering formulation. However, inpractice, there exists the need for improvement in terms of computationalefficiency and capacity of solving large-scale instances. Moreover, thereal bus-driver scheduling problems that we consider can present variantaspects of the set covering, as for example a different objectivefunction, implying that alternative solutions methods have to bedeveloped. We propose metaheuristics based on the following approaches:GRASP (greedy randomized adaptive search procedure), tabu search andgenetic algorithms. These metaheuristics also present some innovationfeatures based on and genetic algorithms. These metaheuristics alsopresent some innovation features based on the structure of the crewscheduling problem, that guide the search efficiently and able them tofind good solutions. Some of these new features can also be applied inthe development of heuristics to other combinatorial optimizationproblems. A summary of computational results with real-data problems ispresented.

Commercial Driver's License in a Nutshell, March 2009

Federal law requires uniform licensing of truck and bus drivers (CDL) in all states and the District of Columbia. Commercial Driver's License (CDL) testing and issuance began in Iowa November 1, 1990. A person applying for a license to operate a commercial motor vehicle must apply for a CDL.

Report of recommendations to the Iowa Department of Education, Division of Vocational Rehabilitation Services, June 30, 2008

Report on the Iowa Department of Education, Division of Vocational Rehabilitation Services for the year ended June 30, 2008

L'Evidence-based nursing, un levier au changement des pratiques infirmières [Evidence-based nursing, a driver for change in nursing practice].

A urology nursing team examined its perioperative practices in the light of scientific data and implemented updated care practices adapted to this context.This experience favours the development of skills essential for interdisciplinary collaboration drawing on the resources of each profession, to work towards a common goal for the benefit of the patient.

Department of Vocational Rehabilitation, FY 2007

These Facts sheets have been developed to provide a multitude of information about executive branch agencies/departments on a single sheet of paper. The Facts provides general information, contact information, workforce data, leave & benefits information, and affirmative action data. This is the most recent update of information for the fiscal year 2007.

Department of Vocational Rehabilitation, FY 2008

These Facts sheets have been developed to provide a multitude of information about executive branch agencies/departments on a single sheet of paper. The Facts provides general information, contact information, workforce data, leave & benefits information, and affirmative action data. This is the most recent update of information for the fiscal year 2007.

INTERMED predicts non-return to work in an occupational rehabilitation setting for individuals with orthopaedic trauma-Part I

Introduction.- Knowledge of predictors of an unfavourable outcome, e.g. non-return to work after an injury enables to identify patients at risk and to target interventions for modifiable predictors. It has been recently shown that INTERMED; a tool to measure biopsychosocial complexity in four domains (biologic, psychologic, social and care, with a score between 0-60 points) can be useful in this context. The aim of this study was to set up a predictive model for non-return to work using INTERMED in patients in vocational rehabilitation after orthopaedic injury.Patients and methods.- In this longitudinal prospective study, the cohort consisted of 2156 consecutively included inpatients with orthopaedic trauma attending a rehabilitation hospital after a work, traffic or sport related injury. Two years after discharge, a questionnaire regarding return to work was sent (1502 returned their questionnaires). In addition to INTERMED, 18 predictors known at baseline of the rehabilitation were selected based on previous research. A multivariable logistic regression was performed.Results.- In the multivariate model, not-returning to work at 2 years was significantly predicted by the INTERMED: odds-ratio (OR) 1.08 (95% confidence interval, CI [1.06; 1.11]) for a one point increase in scale; by qualified work-status before the injury OR = 0.74, CI (0.54; 0.99), by using French as preferred language OR = 0.60, CI (0.45; 0.80), by upper-extremity injury OR = 1.37, CI (1.03; 1.81), by higher education (> 9 years) OR = 0.74, CI (0.55; 1.00), and by a 10 year increase in age OR = 1.15, CI (1.02; 1.29). The area under the receiver-operator-characteristics curve (ROC)-curve was 0.733 for the full model (INTERMED plus 18 variables).Discussion.- These results confirm that the total score of the INTERMED is a significant predictor for return to work. The full model with 18 predictors combined with the total score of INTERMED has good predictive value. However, the number of variables (19) to measure is high for the use as screening tool in a clinic.