Impact of Increased Supply of Newly Licensed Nurses on Hospital Nurse Staffing and Policy Implications

Yunmi Kim; Sunju You; Jinhyun Kim

doi:10.4040/jkan.2017.47.6.828

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Kim, You, and Kim: Impact of Increased Supply of Newly Licensed Nurses on Hospital Nurse Staffing and Policy Implications

Original Article

J Korean Acad Nurs 2017;47(6):828-841.

Published online: 15 January 2017

DOI: https://doi.org/10.4040/jkan.2017.47.6.828

Impact of Increased Supply of Newly Licensed Nurses on Hospital Nurse Staffing and Policy Implications

Yunmi Kim¹, Sunju You^2,, Jinhyun Kim³

¹College of Nursing, Eulji University, Seongnam

²Department of Nursing, Mokpo National University, Mokpo

³College of Nursing, Seoul National University, Seoul, Korea

Address reprint requests to : You, Sunju Department of Nursing, Mokpo National University, 61 dorim-ri, 1666 Yeongsan-ro, Cheonggye-myeon, Muan 58554, Korea Tel: +82-61-450-6290 Fax: +82-61-450-2679 E-mail: sunju@mokpo.ac.kr

Received 24 January 2017 Revised 20 November 2017 Accepted 4 December 2017

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License. (http://creativecommons.org/licenses/by-nd/4.0) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

Abstract

Purpose

This study aimed to analyze the impact of increasing the supply of newly licensed nurses on improving the hospital nurse staffing grades for the period of 2009~2014.

Methods

Using public administrative data, we analyzed the effect of newly licensed nurses on staffing in 1,594 hospitals using Generalized Estimating Equation (GEE) ordered logistic regression, and of supply variation on improving staffing grades in 1,042 hospitals using GEE logistic regression.

Results

An increase of one newly licensed nurse per 100 beds in general units had significantly lower odds of improving staffing grades (grades 6~0 vs. 7) (odds ratio=0.95, p=.005). The supply of newly licensed nurses increased by 32% from 2009 to 2014, and proportion of hospitals whose staffing grade had improved, not changed, and worsened was 19.1%, 70.1%, and 10.8% respectively. Compared to 2009, the supply variation of newly licensed nurses in 2014 was not significantly related to the increased odds of improving staffing grades in the region (OR=1.02, p=.870).

Conclusion

To achieve a balance in the regional supply and demand for hospital nurses, compliance with nurse staffing legislation and revisions in the nursing fee differentiation policy are needed. Rather than relying on increasing nurse supply, retention policies for new graduate nurses are required to build and sustain competent nurse workforce in the future.

Keywords: Nurses, Nursing student, Staffing, Hospital

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Table 1.

Operational Definitions and Data Source of Variables

Variables [Reference]	Operational definitions or Categories	Explanation	Data source
Dependent variable
Nurse staffing	Adjusted nurse staffing grades (0~7) in 2015	Interval scale variable indicating nurse	Health insurance
		staffing level in medical institutions	review and
Improvement or not in nurse	Improvement or not in adjusted nurse staffing	Dichotomous variable indicating the	assessment
staffing	grade in 2015 compared with 2010	improvement in nurse staffing level	service
Independent variable
Hospital characteristics
Type [13,15]	Tertiary hospital, general hospital, hospital	Dummy variable reflecting patient	Health insurance
		case-mix and nursing care needs	review and
Urbanization [11,12,15]	Metropolitan cities including Seoul (5% reduction	Dummy variable reflecting hospital’s	assessment
	in nurse staffing grade 7), Small cities (2%	degree of urbanization	service
	reduction), Medically vulnerable area (No
	reduction)
Ownership [11,13]	Public, educational foundation, corporate,	Dummy variable reflecting hospital’s
	individual	profit-seeking behavior
Number of beds [15]	Number of beds in general units	Variable reflecting the size of hospitals
		and diversity in health care services
Rate of premier beds [13]	Number of premier beds/number of total beds	Proxy variable reflecting the price level
	×100 in general units	of hospitalization services
Medical specialist staffing	Number of medical specialist per 100 general	Doctor variable that reflect the
[13,15]	unit beds	diversity of health care services and
		complementary relationship with
		nurses
General physicians staffing	Number of general physicians per 100 general	Training doctor variable that can be an
[13,15]	unit beds	alternative to nurses
Operation of special care unit	Intensive care unit [13] Yes or no	Variables that reflect the diversity of
	Neonatal intensive care unit [13] Yes or no	medical services and the severity of
	Emergency room [16] Yes or no	patients’ conditions
External environmental variables
Munificence
Rate of those aged [11,12]	Number of people above 65years/total population	Variable reflecting the extent of aging	Korean statistical
	×100 in 2015	of consumers	information
Rate of university graduate [17]	] Number of university graduate/total population	Variable reflecting income level and	service
	×100 in 2015	the ability to obtain information of
		the consumer
Number of beds per 1,000	Number of general unit beds/population ×1,000	Unit variable representing the supply	Health insurance
population [11]	in 2015	of hospitalization services	review and
Number of nurses per 100 beds	Number of hospital RNs in 2010 /general unit	Unit variable indicating the level of	assessment
[12,16,18]	beds ×100 in 2010	working nurses in hospital	service and
Number of newly licensed	Number of newly licensed nurses in 2009 (2014)	Unit variable indicating the amount of	Korea health
nurses per 100 beds in 2009	/general unit beds ×100 in 2010 (2015)	newly licensed nurses’supply	personnel
(2014) [16]			licensing
Dynamism			examination
Differences in newly licensed	Number of newly licensed RNs per 100 beds in	Unit variable representing the supply	institute
nurses between 2009 and	2014-Number of newly licensed RNs per 100	of change in newly licensed nurses
2014 per 100 beds	beds in 2009
Complexity
Herfindahl index [11-13]		Variable indicating the degree of	Health insurance
		competition between medical	review and
	(Number of general unit beds/regional total	institutions	assessment
	beds)²		service

Table 2.

Adjusted Nurse Staffing Grades by Hospital and External Environmental Characteristics in 2015 (N=1,594)

Variables	Categories	Adjusted nurse staffing grades				χ²	p
		0~1 (n=36)	2~3 (n=253)	4~5 (n=162)	6~7 (n=1,143)
		n (%)
Hospital characteristics
Type	Tertiary hospital	26 (59.1)	18 (40.9)	0 (0.0)	0 (0.0)	978.94	<.001
	General hospital	4 (1.4)	122 (43.6)	56 (20.0)	98 (35.0)
	Hospital	6 (0.5)	113 (8.9)	106 (8.4)	1,045 (82.3)
Urbanization	Medically vulnerable area	0 (0.0)	1 (1.3)	1 (1.3)	77 (97.5)	62.59	.001
	Small cities	11 (1.4)	97 (12.2)	84 (10.6)	604 (75.9)
	Metropolitan cities including Seoul	25 (3.5)	155 (21.6)	77 (10.7)	462 (64.3)
Ownership	Public	8 (9.5)	15 (17.9)	23 (27.4)	38 (45.2)	364.77	<.001
	Educational foundation	19 (26.4)	38 (52.8)	4 (5.6)	11 (15.3)
	Corporate	5 (1.1)	75 (17.1)	36 (8.2)	323 (73.6)
	Individual	4 (0.4)	125 (12.5)	99 (9.9)	771 (77.2)
Number of beds	≥300	29 (17.7)	91 (55.5)	14 (8.5)	30 (18.3)	458.90	<.001
	100~299	2 (0.4)	82 (14.3)	69 (12.0)	420 (73.3)
	50~99	3 (0.6)	46 (8.4)	54 (9.9)	444 (81.2)
	30~49	2 (0.7)	34 (11.0)	25 (8.1)	249 (80.3)
Rate of premier beds	≥30%	10 (2.3)	78 (17.7)	52 (11.8)	301 (68.3)	55.32	<.001
	20~<30%	13 (4.8)	64 (23.8)	27 (10.0)	165 (61.3)
	10~<20%	11 (2.5)	71 (16.3)	47 (10.8)	307 (70.4)
	<10%	2 (0.5)	40 (8.9)	36 (8.0)	370 (82.6)
Number of medical	≥15	34 (10.3)	123 (37.4)	41 (12.5)	131 (39.8)	532.88	<.001
specialists per 100 beds	10~<15	1 (0.3)	96 (28.6)	67 (19.9)	172 (51.2)
of general units	5~<10	1 (0.2)	32 (6.2)	52 (10.1)	430 (83.5)
	<5	0 (0.0)	2 (0.5)	2 (0.5)	410 (99.0)
Number of general	≥2	31 (15.1)	91 (44.4)	20 (9.8)	63 (30.7)	348.71	<.001
physicians per 100 beds	1~<2	1 (0.9)	13 (12.0)	13 (12.0)	81 (75.0)
of general units	<1	4 (0.3)	149 (11.6)	129 (10.1)	999 (78.0)
Operation of special care	Intensive care unit Yes	30 (9.3)	124 (38.3)	52 (16.1)	118 (36.4)	301.28	<.001
unit	No	6 (0.5)	129 (10.2)	110 (8.7)	1,025 (80.7)
	Neonatal intensive Yes	28 (30.8)	58 (63.7)	3 (3.3)	2 (2.2)	556.16	<.001
	care unit No	8 (0.5)	195 (13.0)	159 (10.6)	1,141 (75.9)
	Emergency room Yes	31 (4.7)	153 (23.4)	77 (11.8)	393 (60.1)	93.47	<.001
	No	5 (0.5)	100 (10.6)	85 (9.0)	750 (79.8)
External environmental variables
Region	Seoul	16 (6.4)	61 (24.4)	26 (10.4)	147 (58.8)	106.84	<.001
	Busan	3 (2.1)	28 (19.7)	12 (8.5)	99 (69.7)
	Daegu	1 (0.9)	20 (17.4)	10 (8.7)	84 (73.0)
	Incheon	2 (3.2)	12 (19.1)	7 (11.1)	42 (66.7)
	Gwangju	2 (2.4)	19 (22.6)	12 (14.3)	51 (60.7)
	Daejeon	0 (0.0)	9 (22.5)	8 (20.0)	23 (57.5)
	Ulsan	1 (2.4)	7 (17.1)	3 (7.3)	30 (73.2)
	Gyeonggi	6 (2.0)	49 (16.3)	32 (10.6)	214 (71.1)
	Gangwon	0 (0.0)	4 (8.3)	3 (6.3)	41 (85.4)
	Chungbuk	0 (0.0)	6 (13.3)	3 (6.7)	36 (80.0)
	Chungnam	2 (3.9)	1 (1.9)	3 (5.8)	46 (88.5)
	Jeonbuk	1 (1.3)	4 (5.3)	4 (5.3)	66 (88.0)
	Jeonnam	0 (0.0)	12 (12.1)	12 (12.1)	75 (75.8)
	Gyeongbuk	1 (1.3)	6 (7.5)	7 (8.8)	66 (82.5)
	Gyeongnam	1 (0.7)	11 (7.6)	16 (11.0)	117 (80.7)
	Jeju	0 (0.0)	4 (28.6)	4 (28.6)	6 (42.9)

Table 3.

Changes in Adjusted Nurse Staffing Grades between 2010 and 2015 by Hospital and External Environmental Characteristics (N=1,042)

Variables	Categories	Worsened (n=113)	No change (n=730)	Improved (n=199)	χ²	p
		n (%)
Hospital characteristics
Type	Tertiary hospital	0 (0.0)	27 (61.4)	17 (38.6)	100.95	<.001
	General hospital	31 (12.3)	127 (50.2)	95 (37.6)
	Hospital	82 (11.0)	576 (77.3)	87 (11.7)
Urbanization	Medically vulnerable area	0 (0.0)	62 (98.4)	1 (1.6)	48.01	<.001
	Small cities	48 (9.4)	381 (74.9)	80 (15.7)
	Metropolitan cities including Seoul	65 (13.8)	287 (61.1)	118 (25.1)
Ownership	Public	4 (5.8)	37 (53.6)	28 (40.6)	44.44	<.001
	Educational foundation	6 (8.8)	39 (57.4)	23 (33.8)
	Corporate	26 (7.8)	240 (72.3)	66 (19.9)
	Individual	77 (13.4)	414 (72.3)	82 (14.3)
Number of beds	≥300	12 (7.7)	81 (52.3)	62 (40.0)	71.91	<.001
	100~299	45 (10.8)	282 (67.6)	90 (21.6)
	50~99	35 (11.2)	243 (77.6)	35 (11.2)
	30~49	21 (13.4)	124 (79.0)	12 (7.6)
Rate of premier beds	≥30%	41 (15.1)	187 (68.8)	44 (16.2)	33.22	<.001
	20~<30%	18 (9.5)	120 (63.5)	51 (27.0)
	10~<20%	35 (11.9)	191 (65.0)	68 (23.1)
	<10%	19 (6.6)	232 (80.8)	36 (12.5)
Number of medical	≥15	30 (12.2)	137 (55.7)	79 (32.1)	115.63	<.001
specialists per 100	10~<15	38 (16.1)	135 (57.2)	63 (26.7)
beds of general units	5~<10	33 (10.3)	235 (73.0)	54 (16.8)
	<5	12 (5.0)	223 (93.7)	3 (1.3)
Number of general	≥2	17 (9.9)	87 (50.6)	68 (39.5)	60.16	<.001
physicians per 100	1~<2	5 (6.4)	56 (71.8)	17 (21.8)
beds of general units	<1	91 (11.5)	587 (74.1)	114 (14.4)
Operation of special	Intensive care unit Yes	27 (9.4)	158 (54.9)	103 (35.8)	71.80	<.001
care unit	No	86 (11.4)	572 (75.9)	96 (12.7)
	Neonatal intensive Yes	2 (2.3)	47 (54.0)	38 (43.7)	39.91	<.001
	care unit No	111 (11.6)	683 (71.5)	161 (16.9)
	Emergency room Yes	47 (8.5)	371 (66.7)	138 (24.8)	28.61	<.001
	No	66 (13.6)	359 (73.9)	61 (12.6)
External environmental variables
Region	Seoul	27 (16.0)	101 (59.8)	41 (24.3)	76.06	<.001
	Busan	6 (7.0)	52 (60.5)	28 (32.6)
	Daegu	14 (17.5)	53 (66.3)	13 (16.3)
	Incheon	3 (7.7)	24 (61.5)	12 (30.8)
	Gwangju	11 (23.9)	27 (58.7)	8 (17.4)
	Daejeon	3 (10.7)	17 (60.7)	8 (28.6)
	Ulsan	1 (3.2)	22 (71.0)	8 (25.8)
	Gyeonggi	10 (5.9)	122 (72.2)	37 (21.9)
	Gangwon	4 (11.4)	29 (82.9)	2 (5.7)
	Chungbuk	7 (21.2)	24 (72.7)	2 (6.1)
	Chungnam	4 (9.8)	33 (80.5)	4 (9.8)
	Jeonbuk	1 (2.0)	45 (90.0)	4 (8.0)
	Jeonnam	5 (6.6)	60 (79.0)	11 (14.5)
	Gyeongbuk	8 (14.3)	42 (75.0)	6 (10.7)
	Gyeongnam	8 (8.6)	73 (78.5)	12 (12.9)
	Jeju	1 (10.0)	6 (60.0)	3 (30.0)

Table 4.

Supply of Newly Licensed Nurses and External Environment Variables

Region	Munificence								Dynamism	Complexity
	Rate of those aged in 2015	Rate of university graduates in 2015	Number of beds per 1,000 population in 2015	Number of nurses per 100 beds in 2010	Number of newly licensed nurses^†		Number of newly licensed nurses per 100 beds		Differences in newly licensed nurses between 2009 and 2014 per 100 beds	Herfindahl index in 2015
					2009	2014	2009	2014
Whole	13.2	18.7	4.8	46.4	11,709	15,411	4.7	6.2	1.6	−
country
Seoul	12.6	25.8	4.3	71.7	1,416	1,578	3.4	3.6	0.2	0.009
Busan	14.7	20.3	6.7	42.1	505	852	2.2	3.6	1.4	0.033
Daegu	12.7	19.7	6.9	27.2	823	748	4.8	4.3	−0.5	0.043
Incheon	10.7	14.6	3.9	60.6	247	395	2.3	3.5	1.2	0.062
Gwangju	11.3	22.9	8.5	43.6	931	1,157	8.4	9.2	0.8	0.050
Daejeon	10.9	23.0	4.8	56.2	555	760	7.7	10.4	2.7	0.163
Ulsan	8.8	14.8	4.7	34.5	410	430	6.1	7.8	1.7	0.141
Gyeonggi	10.5	18.2	3.4	49.6	864	1,141	2.2	2.7	0.5	0.027
Gangwon	16.9	15.8	5.2	45.6	1,167	1,430	13.1	17.8	4.7	0.087
Chungbuk	14.8	15.4	4.3	40.0	206	604	3.1	8.9	5.8	0.080
Chungnam	16.4	15.0	3.6	41.7	403	896	5.0	12.1	7.1	0.105
Jeonbuk	17.8	16.5	6.0	38.2	691	1,036	5.9	9.2	3.3	0.084
Jeonnam	20.5	10.8	8.0	34.3	926	1,189	5.8	7.8	2.0	0.042
Gyeongbuk	17.8	13.3	4.3	38.9	1,519	1,834	9.9	15.9	6.0	0.034
Gyongnam	13.8	14.4	6.2	29.4	843	1,162	3.5	5.6	2.1	0.048
Jeju	13.8	13.2	4.5	64.8	203	199	7.5	7.1	−0.4	0.192

^†Excluded new licensed nurses who had graduated from a foreign nursing education institution.

Table 5.

Logistic Regression Analysis of the Nurse Staffing Grades

Variables	Categories	Ordered logistic regression of adjusted nurse staffing grades in 2015 (reference=grade 7) (N=1,594)			Logistic regression of adjusted nurse staffing grade changes (reference=no change or worsened) (N=1,042)
		OR	(95%CI)	p	OR	(95%CI)	p
Hospital characteristics
Type	Hospital	0.32	(0.16~0.63)	.001	1.77	(0.74~4.26)	.202
	General hospital	0.07	(0.03~0.18)	<.001	0.59	(0.14~2.56)	.481
	Tertiary hospital	1.00		−	1.00		−
Urbanization	Medically vulnerable area	0.09	(0.03~0.28)	<.001	0.06	(0.01~0.59)	.016
	Small cities	1.04	(0.66~1.62)	.874	0.46	(0.28~0.75)	.002
	Metropolitan cities including Seoul	1.00		−	1.00		−
Ownership	Public	1.22	(0.79~1.90)	.370	2.03	(0.75~5.48)	.164
	Educational foundation	1.33	(0.70~2.51)	.385	0.84	(0.38~1.84)	.668
	Corporate	0.76	(0.60~0.96)	.022	1.02	(0.72~1.45)	.913
	Individual	1.00	1.00	−	1.00	1.00	−
Number of beds		1.00	1.00	<.001	1.00	1.00	.526
Rate of premier beds		1.02	(1.00~1.03)	.023	1.00	(0.99~1.02)	.489
Number of medical specialists per 100 beds of		1.09	(1.05~1.14)	<.001	1.03	(1.00~1.07)	.032
general units
Number of general physicians per 100 beds of		1.02	(0.98~1.06)	.385	1.00	(0.95~1.05)	.911
general units
Operation of	Intensive care unit Yes	0.89	(0.59~1.34)	.564	1.35	(0.74~2.46)	.322
special care unit	No	−	−	−	−	−	−
	Neconatal intensive Yes	1.35	(0.68~2.69)	.395	1.48	(0.88~2.51)	.139
	care unit No	−	−	−	−	−	−
	Emergency room Yes	0.80	(0.54~1.17)	.246	1.19	(0.72~1.96)	.505
	No	−	−	−	−	−	−
External environmental	l variables
Rate of those aged		0.97	(0.91~1.03)	.310	1.01	(0.94~1.09)	.805
Rate of university graduates		0.98	(0.90~1.06)	.607	0.96	(0.90~1.02)	.186
Number of beds per 1,000 population		1.19	(1.05~1.36)	.007	1.00	1.00	.901
Herfindahl index		4.14	(0.06~267.07)	.504	12.08	(0.10~1480.60)	.310
Number of nurses per 100 beds		1.02	(1.00~1.04)	.028	1.00	(0.98~1.02)	.933
Number of newly licensed nurses per 100 beds in 2014		0.95	(0.91~0.98)	.005	0.90	(0.83~0.97)	.009
Differences in newly licensed nurses between 2009 and		−	−	−	1.02	(0.83~1.24)	.870
2014 per 100 beds

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