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Factors Influencing RNs’ Decisions to Work

Factors Influencing RNs’ Decisions to Work. Carol S. Brewer, Ph.D.* Chris T. Kovner, Ph.D.** William Greene, Ph.D.** Yow Wu-Yu, Ph.D.* Liu Yu, Ph.D. (cand.)* This work was supported by a grant from AHRQ R01 HS011320 Presented at AcademyHealth, June 6, 2004

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Factors Influencing RNs’ Decisions to Work

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  1. Factors Influencing RNs’ Decisions to Work Carol S. Brewer, Ph.D.* Chris T. Kovner, Ph.D.** William Greene, Ph.D.** Yow Wu-Yu, Ph.D.* Liu Yu, Ph.D. (cand.)* This work was supported by a grant from AHRQ R01 HS011320 Presented at AcademyHealth, June 6, 2004 *University at Buffalo ** New York University

  2. Participation PT FT • Why important: • If only 10% of the PT RN population worked FT, it would add 31,000 RNs to supply • Part of larger analysis also looking at work/notwork • If the RN works, how much (PT or FT) • FT defined as >35 hrs per week for all jobs

  3. Research questions • What factors are associated with the work decision (WK/NW) and amount of work (FT/PT)? • Are the WK/NW and PT/FT decisions made together or separately?

  4. Data sources • The National Sample Survey of Registered Nurses March 2000 (Spratley et al., 2001) • County level data (some restrictions) • Female RNs in 300 MSAs represented • MSA/County level variables • InterStudy Competitive Edge Part III Regional Market Analysis (2002) • Area Resource File (2002)

  5. Sample • 35,358 registered nurses • Exclusions: • Did not live or work in the USA • Missing MSA codes for job and living location • Did not work (or live) in an MSA • Analytic sample was 21,123 females • Married 14, 898 • Single 6,225.

  6. Economic Environment Variables • Induced demand HYP. Means • Medical/surgical specialists per 1000 pop + 1.74 • Primary care practitioners per 1000 pop + 0 .24 • % of HMO services paid FFS + 17.4% • Managed care/demand • Index of competition - .68 • Penetration rate of managed care - 29.6% • Poverty/demand • % non-HMO Medicaid as % of total MSA pop + 7.4% • % uninsured pop ? 13.6% • % families living in poverty ? 8.1% • Unemployment rate + 1.8%

  7. Demographics Characteristics

  8. Working RNs Characteristics Dominant function direct care 51.6% Staff/general duty nurses 50.9% Work in hospitals 60.5% Satisfaction (mean) 1= extremely satisfied married 2.31 single 2.42

  9. Analysis • Analytic method: bivariate probit regression • Tested hypothesis that WKNW / FTPT decisions are related • Single RNs Rho= -0.45, p= 0.02 • Married RNs, Rho=-0.51, p= 0.00

  10. Results Interpretation of marginal effects Probability of working or working FT changes (+ or -) by amount of marginal effect at mean of variable Ex: The probability of a 25-30 yr old RN working FT decreases by 0.12 compared to a RN < 25

  11. Significant marginal effectsPT/FT regression: Economic variables • Other sig var (very small effects): • Unemployment rate, penetration rate for both • % non HMO M’caid, Specialist ratio for single only

  12. Significant marginal effectsPT/FT regression: Economic variables • Other sig var (very small effects): sig for both • % families in poverty • Size of MSA (small, medium, compare to large)

  13. Significant Demographicvariables in Part-time / Full-time regression • Probability of FT decreases • All age categories: Stronger effect for married, >60 • if any children < 6 • Stronger for married (-0.30 vs -0.17) • Baccalaureate RN vs. AD • Probability of FT increases • Minorities married, ME=0.16 • Total family income, (non linear) NS for married • 0.30 to 0.19 for single • Student statusNS for married • PT student or not a student

  14. Significant organizational variables in PT/FT regression • Probability of FT decreases • Satisfaction:smallME= - 0.01 married, ONLY • Settings:Educators, student health, ambulatory care SIG vs. hospital RNs • Probability of FT increases • Function: Supervisors, teachers, administrators vs. direct care RNs: ME=0.09-0.21 married, ONLY • Positions: ALL other (NP, CNS, administrator, etc) vs. staff RNs, Stronger for married

  15. Conclusions • MSA level economic variables • Influential on PT/FT decision, but not decision WK/NW • Influence of demographic variables • Age, children, minority, income and student status • more effect on FT work decision than WK • Education (BSN-married, Master’s single) • weak but negative = concern • Organization variables • satisfaction significant, neg, if married • Hospital, direct care and staff RNs most likely to be PT • Functions and positions indicating career path more likely to be sig

  16. Implications • Need to target single vs married RNs • What organizations can change: • Career orientation may influence PT/FT • chicken or egg ? Develop career paths early • Age related work conditions, esp after age 55 • Improve satisfaction • Recruit minorities • Work decision different from how much to work

  17. Implications • Government policy • Clarify education: rewards need to be clear • Economic variables-need to understand • What can Govt manipulate? • May help in predicting regional variability in shortages. • Job market or health of population? • For ex: IOC- perhaps hospitals are competing for nurses and end up with more full-time workers due to higher wages

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