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Development of a Steady State Model for Forecasting U.S. Navy Nurse Corps Personnel

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Master's thesis

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This thesis developed a deterministic Markov state model to provide the U.S. Navy Nurse Corps a tool to more accurately forecast recruiting goals and future years force structure. The nurse corps personnel were categorized by length of service and paygrade. The focus of this research was paygrades 0-1 to 0-3, which required lengths of service up to eleven years for aging through the system. 0-4s and 0-5s that appeared in the data were allowed to flow through the system. Nurse Corps data was provided by the Nurse Corps Community Managers office covering fiscal years 1990 to 2003. The transition probabilities used in the Markov model were derived from the fiscal year data. Personnel stay at present grade, move up one grade or exit the system within each year of the model. Backward movement was not allowed and individuals could only move up one grade per year. Logistic regression was then used to investigate the probability of staying in the Nurse Corps to certain career decision points. Nurse Corps cohort data files for fiscal years 90 through 94 were merged for analysis, as was cohort data for fiscal year 96 through 98. Results of the markov model show that the 0-ls and 0-2s reach a steady state at the eight-year mark while the 0-3s reach a steady state at the seventeen-year mark based on provided data. Comparing to nurse corps goals, the current accession plans result in a severe shortage of Lieutenants. There is an overabundance of Ensigns so the overall size of the Nurse Corps is as desired it is just a more junior corps. Scenarios were developed to ascertain the best mix of accessions to attain Nurse Corps goals as well as to examine scenarios for downsizing. Results of the logistic regression show that Recalls, Medical Enlisted Commissioning Program and Nurse Candidate Program were all significant at increasing the probability of staying in the Nurse Corps.

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  • Personnel Management and Labor Relations

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