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Factors Influencing Registered Nurses Decisions to Work

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Factors Influencing Registered Nurses Decisions to Work Powered By Docstoc
					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
   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
 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
   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
 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
   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%
  Demographics Characteristics
                 Working      Non working
Modal Age        40-44        50-54
Modal Tot Inc    $50-75,000   $50-75,000
Non-white        16.1%        12.8%
Marital status   69.8%        74.4%
Any kids < 6     18.1%        15.7%
Student          7.4%         2.9%
   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
   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
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
Probability of FT        Married             Single
decreases
  Primary care physician -0.18               -0.23
     ratio

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

Probability of FT            Married       Single
increases
   Index of competition      0.12          0.11

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

   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 status NS for married
        PT student or not a student
    Significant organizational variables in
               PT/FT regression
   Probability of FT decreases
    – Satisfaction: small ME= - 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
   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
 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
                 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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