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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

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


Participation pt ft
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


Research questions
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?


Data sources
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)


Sample
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.


Economic environment variables
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%



Working rns characteristics
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


Analysis
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


Results
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


Significant marginal effects pt ft regression economic variables
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


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)


Significant demographic variables in part time full time regression
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


Significant organizational variables in pt ft regression
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


Conclusions
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


Implications
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


Implications1
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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