Thrita

Quality of Life in Nurses Working in Neyshabur Hospitals

Ali Gholami ^{1 , *} , Morteza Farsi ¹ , Zahra Hashemi ¹ and Pegah Lotfabadi ¹

Authors Information

¹ Students’ Research Committee, Neyshabur Faculty of Medical Sciences, Neyshabur, IR Iran

Article information

Thrita: March 30, 2013, 2 (1); 94-100
Published Online: February 2, 2013
Article Type: Research Article
Received: July 25, 2012
Revised: August 20, 2012
Accepted: August 28, 2012
DOI: 10.5812/thrita.7384

To Cite: Gholami A, Farsi M, Hashemi Z, Lotfabadi P. Quality of Life in Nurses Working in Neyshabur Hospitals, Thrita. 2013 ;2(1):94-100. doi: 10.5812/thrita.7384.

Abstract

Copyright © 2013, Thrita. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

1.Background

The world health organization (WHO) has defined quality of life (QOL) as “an individual’s perception of his/her position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” (1). Recently, many general instruments have been used to measure QOL in different groups (i.e., population, patients, workers, etc). One of such instruments is the SF-36 questionnaire, a generic instrument translated and validated in Iran by Montazeri (2). In this regard, QOL of people with a health problem (i.e., cancer, diabetes, hypertension, etc) was evaluated in many studies (3-29); But there are few studies that have evaluated the QOL of health care workers, for example nurses, who provide health care services for patients.

Nurses are among the greatest providers of services in the health care system and in their work settings, they may be exposed to a wide array of chemical, biological, psychosocial (i.e., stress, depression, etc) and physical hazards (i.e., injuries, transmission of infectious diseases, etc). Consecutively, such conditions may affect the QOL of nurses. It is noteworthy that nurses provide higher quality services for their patients when they are healthy and possess desirable QOL. Therefore, it is important to pay particular attention to nurses’ general health conditions and QOL.

2. Objectives

3. Materials and Methods

In this cross-sectional study, data were collected from February to April 2012 in Neyshabur hospitals (22 Bahman and Hakim) in Neyshabur (A city in Northeast of Iran). Of all nurses (n = 220), 22 were excluded because they refused to participate in the study. Therefore, 198 questionnaires were collected (response rate = 90%). All of participants provided informed consent after the purpose of study was explained for them. Inclusion criteria for participating in the study included the nurses who work in Neyshabur hospitals in addition to their agreement for participation.

3.1. Procedure and Study Instrument

In this study, questionnaires have been filled out by participants and for enhancing the accuracy, participants were explained that their responses would remain confidential. A trained person explained how to complete the questionnaires for each participant. We used the Iranian version of the SF-36 questionnaire in this study that was validated in Iran by Montazeri (2). This questionnaire contains 36 items that are divided into eight subscales: physical functioning (PF), role limitations due to physical problems (RP), bodily pain (BP), vitality (VT), general health (GH), social functioning (SF), role limitations due to emotional problems (RE) and mental health (MH). Three subscales (MH, RE, and SF) correlate most highly with the mental aspect of QOL and are associated with subscale of VT, contribute most to the scoring of mental component summery (MCS) measure (30). The physical aspect of QOL correlates most highly with the subscales including PF, RP, and BP, which are associated with sub-scale of GH, contribute most to the scoring of physical component summery (PCS) measure (31). This study also focuses on analyzing and reporting PCS and MCS. The SF-36 was scored according to the recommendations by Ware et al (31). The scores of each item are transformed to a 0–100 scale, where 100 is the best and zero is the worst score.

3.2. Dependent and Independent Variables

Eight subscales of SF-36 questionnaire were considered as dependent variables. Other collected data including sex, age, marital status, employment status, income level (per month), years in occupation, existence of any chronic disease morbidity, family size, number of children, shift type and house ownership were considered as independent variables. The age of participants was classified into two categories of ≤ 35 years and > 35 years. Nurses were also categorized into two groups of single/divorced and married according to marital status. Employment status was categorized into two categories including official and contractual. Income level was classified into two categories including ≤ 5 million Rials and > 5 million Rials per month. Also, years in occupation was divided into two categories including < 10 years and ≥ 10 years. Family size was divided into two categories including ≤ 4 and > 4 . Number of children was divided into two categories including 1 and ≥ 2 . Shift type was categorized into two categories including fixed and circular. House ownership was categorized into two categories including tenant and private house.

3.3. Statistical Analyses

Descriptive analyses were performed including the frequencies, percentages, ranges, means, and standard deviations (SD). Pearson’s correlation coefficient was used to determine the level of agreement between eight subscales of SF-36. To investigate the association between participants’ characteristics and their QOL, t-independent test was performed. Multiple Linear Regression model with backward method was performed to control confounding effects. Transformed scores were used for statistical analyses in all subscales. SPSS software, version 16 was used to analyze the collected data. P values less than 0.05 were considered statistically significant.

4. Results

In total, 198 nurses filled out the SF-36 questionnaire in this study. Ten questionnaires had more than 20% of missing data and thus were excluded from the study. The analysis was restricted for the remaining 188 respondents. Some of the characteristics of study population are shown in Table 1. The mean age of female and male participants was 29.5 and 36.2 years respectively and this difference was statistically significant (P < 0.001) .

Table 1 Characteristics of Study Population (n = 188)

Characteristics	No.	%
Sexa
Male	42	22.3
Female	146	77.7
Marital Status
Single/Divorced	43	22.9
Married	145	77.1
Employment Statusa
Official	35	18.8
Contractual	151	81.2
Mean ± SD Age, y	31.02 ± 6.74
Mean ± SD Years in Occupation	6.1 ± 6.31

^aSome data were missing

Table 2 presents correlations between eight subscales of SF36; as observed, there were statistically significant correlations between all subscales (P < 0.05). There was also relations between all subscales (P < 0.05). There was also a significant correlation between PCS and eight subscales in addition to MCS and eight subscales of SF36. As Table 3 shows, the total mean score of SF-36 was 64.7 and among the different subscales of SF-36, the lowest and the highest mean scores were found for RE subscale (Mean = 55.9) and PF subscale (Mean = 77.3 ), respectively. The mean scores of the PCS, MCS, eight subscales and total of SF-36 according to sex, age, marital status, employment status, income level, years in occupation, chronic disease existence, family size, number of children, shift type and house ownership are presented in Table 3. As Table 3 shows, there were significant differences between different states of some variables (age, marital status, employment status, years in occupation, chronic disease existence and house ownership) in six subscales of SF-36 (P < 0.05) . Table 4 presents the results of Backward Multiple Linear Regression model; variables with significant relations were as follows: years in occupation, employment status and house ownership. Table 4 shows the positive and negative relations in this study. Positive relations included: between RP subscale and employment status, between the PCS and employment status. Negative relations included: between PF, VT, SF, BP, GH subscales and years in occupation, between the MCS and years in occupation, between RP subscale and house ownership. We did not observe any significant relation between various factors and RE or MH subscales after use of regression model.

Table 2 Correlation Coefficients for the PCS, MCS and Eight Subscales of SF-36

	PF	RP	RE	VT	MH	SF	BP	GH	PCS	MCS
PF
CC	1	0.433	0.203	0.422	0.361	0.379	0.503	0.454	0.750	0.367
Sig		<0.001	0.005	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001
RP
CC		1	0.387	0.315	0.312	0.417	0.458	0.274	0.826	0.440
Sig			<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001
RE
CC			1	0.538	0.548	0.556	0.336	0.361	0.438	0.899
Sig				<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001
VT
CC				1	0.759	0.523	0.404	0.556	0.528	0.790
Sig					<0.001	<0.001	<0.001	<0.001	<0.001	<0.001
MH
CC					1	0.528	0.388	0.448	0.479	0.494
Sig						<0.001	<0.001	<0.001	<0.001	<0.001
SF
CC						1	0.510	0.490	0.584	0.759
Sig							<0.001	<0.001	<0.001	<0.001
BP
CC							1	0.407	0.765	0.470
Sig								<0.001	<0.001	<0.001
GH
CC								1	0.624	0.525
Sig									<0.001	<0.001
PCS
CC									1	0.588
Sig										<0.001
MCS
CC										1
Sig

Abbreviations: BP, bodily pain; CC, Correlation coefficients; GH, general health; MCS, mental component summery; MH, mental health; PCS, physical component summery; PF; physical functioning; RE, role limitations due to emotional problems; RP, physical problems; SF, social functioning; Sig, Significant. (2-tailed); VT, vitality

Table 3 Comparison of the Mean Scores in the PCS, MCS, Eight Subscales and Total of SF-36 According to Nurses’ Characteristics

Subscales
	PF Mean(SD)	RP Mean(SD)	RE Mean(SD)	VT Mean(SD)	MH Mean(SD)	SF Mean(SD)	BP Mean(SD)	GH Mean(SD)	PCS Mean(SD)	MCS Mean(SD)	Total Mean(SD)
Total	77.3(21.4)	59.8(39.8)	55.9(44.9)	57.7(18.4)	62.6(17.6)	69.4(19.8)	70.1(23.4)	65 (18.9)	68 (19.7)	61.4 (21)	64.7 (18.1)
Sex
Male	75.8(24.2)	62.5(41)	62.7(46.7)	60.9(21)	67.1(16.7)	70.5(18.3)	72.4(21.1)	62.1(20.7)	68.2(19.1)	65.3(21.5)	67.4 (17.5)
Female	77.8(20.6)	59.1(39.5)	53.9(44.3)	56.8(17.5)	61.3(17.7)	69.1(20.2)	69.4(24.1)	65.8(18.3)	68 (19.9)	60.3(20.8)	64.3 (18.2)
P-value	0.606	0.624	0.263	0.191	0.060	0.678	0.469	0.276	0.951	0.168	0.341
Age
≤ 35 yr	79.6(20.9)	62.8(38.4)	56.2(44.6)	59 (16.6)	62.7(16.4)	69.4(18.7)	71.2(23.5)	67.6(16.9)	70.3 (19)	61.8(19.5)	66.1 (16.8)
> 35 yr	71.8(20.5)	51.8(41.5)	54.8(45.3)	53 (22.2)	60.1(20.1)	69.9(21.9)	66.6(23.8)	58.8(20.6)	62.2(19.5)	59.4(59.4)	60.8 (19.9)
P-value	0.036	0.113	0.855	0.111	0.403	0.883	0.271	0.006	0.018	0.562	0.095
Marital Status
Single/Divorced	86.4(16.6)	75 (35.4)	65.9 (42)	62.9(18.3)	65.7(18.3)	77.1(17.7)	79.7(20.1)	74.5(16.7)	78.9(16.8)	67.9(19.2)	73.4 (15.7)
Married	75.1(21.6)	55.9(39.9)	53.3(45.4)	56.3(18.2)	61.7(17.5)	67.4(19.8)	67.4(23.7)	62.3(18.6)	65.2(19.2)	59.7(21.1)	62.4 (17.9)
P-value	0.002	0.006	0.099	0.041	0.197	0.005	0.003	<0.001	<0.001	0.025	<0.001
Employment Status
Official	67.6(21.3)	47.9(41.7)	61.9(45.1)	53.6(21.7)	63.8(21.5)	71.4 (22)	64.5(21.9)	59.1 (20)	59.8(19.2)	62.7(23.4)	61.2 (19.5)
Contractual	80.1(20.4)	62.8(38.7)	54.5(44.8)	58.7(17.5)	62.1(16.6)	69 (19.2)	71.3(23.2)	66.4(18.4)	70.1(19.1)	61.1(20.4)	65.6 (17.6)
P-value	0.001	0.045	0.382	0.135	0.673	0.520	0.123	0.40	0.004	0.692	0.194
Income Level (mo)
≤ 5000000 Rial	79.2(26.2)	63.9(40.4)	59.3(46.5)	60.6 (20)	64 (19.6)	72.9(15.6)	69.3(20.3)	72.8(17.6)	71.3(22.3)	64.2(21.7)	67.7 (21)
> 5000000 Rial	76.8(76.8)	56.8(39.2)	55.8(44.8)	57.2(18.6)	61.3(18.1)	68.9(19.6)	70 (22.8)	64.9(19.1)	67.1 (19)	60.8(20.9)	64 (17.5)
P-value	0.668	0.472	0.762	0.476	0.559	0.413	0.897	0.100	0.396	0.525	0.406
Years in occupation
< 10 yr	80.5(20.7)	62.1 (40)	56.1(45.2)	60.2(18.6)	63.5(16.7)	72.9(19.8)	72.6(24.7)	67.9(19.3)	70.8(20.8)	63.2(20.8)	67 (18.8)
≥ 10 yr	65 (22.3)	55.7(40.8)	42.4(43.9)	49.3 (18)	56.2(22.1)	58.5(20.2)	56.9(28.2)	52.3 (21)	57.5(19.3)	51.6(22.3)	54.5 (18.9)
P-value	0.003	0.508	0.207	0.015	0.090	0.003	0.011	0.001	0.008	0.024	0.007
Chronic Disease
No	68.2(22.8)	45.5(37.5)	63.6(43.5)	52.7(17.8)	59.6(20.4)	67.6(22.4)	56.1(23.6)	54.6(23.6)	56.1(17.1)	60.9(21.8)	58.5 (17.5)
Yes	79.2(20.5)	62.3(39.1)	54.2(45.1)	58.1(18.6)	62.8(17.5)	69.6(19.3)	71.5(22.9)	66.7(18.1)	69.9(19.1)	61.2(20.9)	65.6 (17.8)
p-value	0.021	0.058	0.358	0.203	0.431	0.654	0.004	0.004	0.001	0.952	0.082
Family size
≤ 4	74.4(21.2)	55.2(39.2)	53.6(45.2)	56 (17.5)	61.2(17.4)	68.2(20.6)	67 (23.8)	62.4(18.8)	64.7(18.8)	59.7(21.1)	62.2 (17.7)
> 4	80 (26.5)	73.2(39.8)	61.9 (45)	59.6(26.4)	61.7(20.8)	69.6(18.2)	72.7(24.6)	70.4(16.7)	74.1(20.8)	63.2(24.8)	68.1 (21)
P-value	0.365	0.108	0.518	0.627	0.910	0.796	0.399	0.133	0.086	0.569	0.212
Children number
1	73.5(20.6)	66.1 (37)	50.8(46.1)	54.1 (19)	57.9(19.1)	64 (20.9)	67.3(25.5)	58.8(20.3)	66.4(20.7)	56.7(22.6)	61.5 (19.6)
≥ 2	71 (23.2)	56.7(40.4)	54.1(46.8)	55.1(20.8)	63.5(19.5)	70.3(18.5)	66.6(23.7)	60.7(18.8)	63.7(17.4)	60.7(22.7)	62.2 (17.5)
P-value	0.605	0.261	0.743	0.804	0.184	0.140	0.894	0.658	0.514	0.407	0.863
Shift type
Fix	71.7 (24)	52.1 (42)	61 (42.5)	59.4(16.5)	63 (17.4)	68.1(19.1)	68.4(22.6)	64.6(19.9)	64.2(21.6)	62.9(19.6)	63.5 (18.1)
Circular	79.7(20.1)	63 (38.7)	53.8(45.9)	57.1(19.2)	62.3(17.7)	69.8 (20)	70.3(23.9)	65.3(18.5)	69.6(18.7)	60.8(21.5)	65.2 (18)
P-value	0.430	0.108	0.349	0.474	0.808	0.606	0.626	0.831	0.133	0.555	0593
House ownership
Private house	78.9(19.8)	63 (39.8)	53.6(44.5)	55.9(17.4)	62.3(16.9)	68.6 (20)	69.3(24.2)	63.5 (19)	68.7(18.9)	60.1(20.4)	64.4 (17.2)
Tenant	72.5(22.4)	45.7(37.3)	56.4(44.5)	57.4(19.2)	58.7(18.2)	68.8(18.9)	66.8(21.8)	64.9(17.2)	62.5(19.2)	60.3(21.1)	61.4 (17.9)
P-value	0.064	0.009	0.708	0.610	0.220	0.961	0.524	0.644	0.052	0.947	0.306

Table 4 Comparison of the Mean Scores in the PCS, MCS, Eight Subscales and Total of SF-36 According to Nurses’ Characteristics a

Variables	Unstandardized Coefficients		Standardized Coefficients	t	P value
	B	SE	Beta
PF Subscale
Years in occupation	- 12.13	5.37	- 0.227	- 2.26	0.026
RP Subscale
Employment Status	19.29	7.55	0.196	2.56	0.012
House Ownership	- 20.92	6.56	- 0.244	- 3.19	0.002
VT Subscale
Years in occupation	- 10.92	4.42	S	- 2.47	0.015
SF Subscale
Years in occupation	- 14.42	4.76	- 0.283	- 3.03	0.003
BP Subscale
Years in occupation	- 15.05	6.12	- 0.236	- 2.46	0.016
GH Subscale
Years in occupation	- 15.60	4.45	- 0.337	- 3.51	0.001
PCS
Employment Status	14.94	5.50	0.264	2.72	0.008
MCS
Years in occupation	- 11.58	5.05	- 0.218	- 2.29	0.024
Total
Years in occupation	-12.44	4.51	-0.260	-2.76	0.007

Abbreviations: BP, bodily pain; GH, general health; MCS, mental component summery; PCS, physical component summery; PF; physical functioning; RP, physical problems; SF, social functioning; VT, vitality

5. Discussion

The present study was designed to assess the QOL in nurses working in Neyshabur hospitals and some factors associated with it, with the use of the SF-36 scale. According to our findings, the total mean score of SF-36 was 64.7 that indicates a relatively moderate QOL in Neyshabur’ nurses. In a study conducted by Assarrodi, which was conducted to investigate the relation between spiritual well-being and QOL in nurses, they observed that the mean score of QOL was 64.38 (32). In another study conducted by Allaf Javadi in order to compare the QOL in nurses of special care and internal surgical wards, they observed that mean scores of QOL were 69.66 and 62.17, respectively (33).

Among the different subscales of SF-36, the lowest mean score was found for RE subscale (55.9%), implying that more than 44% of Neyshabur’ nurses, had problems with their work or other regular daily activities as a result of emotional problems (such as feeling depressed or anxious) in the last four weeks. Moreover, the highest mean score was shown for PF subscale (77.3%) indicating that lower than 23% of physical activities of Neyshabur’ nurses, was limited a lot or limited a little. In a study conducted by Aghamolaei in order to investigate the determinants of health-related quality of life (HRQOL) in general population living in Bandar Abbas, the lowest and the highest mean scores were found for GH and RE subscales, respectively (34). Additionally, assarrodi showed that the lowest and the highest mean scores were observed for RE and RP subscales, respectively (32). Daher et al performed another study to assess the HRQOL among Iraqis living in Malaysia, and they observed that the lowest and the highest mean scores were found for VT and PF subscales, respectively (35). In this study, we observed that the mean score of PCS is more than the mean score of MSC, implying that participants in this study had fewer problems in physical component in compare with mental component. This findings confirm the results of Assarrodi, Allaf Javadi and Daher’ studies (32, 33, 35). But the report of Aghamolaei showed that the mean score of PCS is lower than the mean score of MSC (34).

As Table 3 shows, single/divorced participants reported higher QOL in compare to married participants in the PCS, MCS and eight subscales of SF-36, and the differences were statistically significant (except in RE and MH sub-scales). In the report of Aghamolaei, they observed that marital status could not significantly decrease the scores of SF-36 in both mental and physical aspects (34). But in Tajvar’s report married participants had higher HRQOL in compare to single people (36).

As Table 4 shows, years in occupation was the most important factor affecting the QOL of study population in total and also in MCS and five subscales (PF, VT, SF, BP and GH) of SF-36. Years in occupation was negatively related to MCS and subscales such as: PF, VT, SF, BP and GH. This subject indicates that having more work experience reduced QOL in some physical and mental subscales. Employment status was the other factor that was associated with PCS and RP subscales. Such results indicate that employment status was related only with physical subscale. House ownership was another factor that was associated with RP subscale. In the study conducted by Aghamolaei, sex, age, education and employment status were significantly related to the PCS and MCS (34). Sex was significantly associated with PCS and marital status was associated with MCS in the study conducted by Daher (35). Thumboo observed that educational level and housing type (markers of socio-economic status) were also associated with SF-36 scores (37).

This study has several limitations: First, causality between the compared variables could not be concluded due to the cross-sectional design of the study. Second, most variables were assessed by self-report and desirable responses might have been given. Third, 22 persons were excluded from the study because of refusal to participate in the study and 10 persons were also excluded because their questionnaires had more than 20% of missing data.

From the findings of this study, it appears that Neyshabur’ nurses have SF-36 scores that might indicate a relatively moderate QOL and the findings also confirm that years in occupation is the major factor that affects their QOL. Thereby, interventional programs should be designed and implemented by Neyshabur Faculty of Medical Science, in order to help nurses to improve their QOL and general well-being, especially among nurses with more work experience.

Acknowledgements

Footnotes

Implication for health policy/practice/research/medical education Nurses provide higher quality services for their patients when they are healthy and possess desirable Quality Of Life (QOL); So, by identifying the Quality Of Life in nurses we would be able to help them and their patients.

Please cite this paper as Gholami A, Farsi M, Hashemi Z, Lotfabadi P. Quality of Life in Nurses Working in Neyshabur Hospitals. Thrita J Med Sci.2012;1(3): 94-100. DOI: 10.5812/thrita.7384

Authors’ Contribution A.G carried out the design and coordinated the study and prepared the manuscript. MF and ZH participated in data collection. PL participated in data analyses. All authors read and approved the final manuscript.

Financial Disclosure The authors have no conflicts of interest.

Funding/Support This research was funded by Neyshabur Faculty of Medical Sciences.

References

1. World Health Organization Quality of Life Assessment. World Health Forum. 1996; 17(4): 354-6[PubMed]
2. Montazeri A, Goshtasebi A, Vahdaninia M, Gandek B. The Short Form Health Survey (SF-36): translation and validation study of the Iranian version. Qual Life Res. 2005; 14(3): 875-82[DOI][PubMed]
3. Siddiqui F, Konski AA, Movsas B. Quality-of-life concerns in lung cancer patients. Expert Rev Pharmacoecon Outcomes Res. 2010; 10(6): 667-76[DOI][PubMed]
4. Sherman AC, Simonton S. Advances in quality of life research among head and neck cancer patients. Curr Oncol Rep. 2010; 12(3): 208-15[DOI][PubMed]
5. Opara JA, Jaracz K, Brola W. Quality of life in multiple sclerosis. J Med Life. 2010; 3(4): 352-8[PubMed]
6. Chen CM, Cano SJ, Klassen AF, King T, McCarthy C, Cordeiro PG, et al. Measuring quality of life in oncologic breast surgery: a systematic review of patient-reported outcome measures. Breast J. 2010; 16(6): 587-97[DOI][PubMed]
7. Danquah FV, Wasserman J, Meininger J, Bergstrom N. Quality of life measures for patients on hemodialysis: a review of psychometric properties. Nephrol Nurs J. 2010; 37(3): 255-69; quiz 70[PubMed]
8. Al-Batran SE, Ajani JA. Impact of chemotherapy on quality of life in patients with metastatic esophagogastric cancer. Cancer. 2010; 116(11): 2511-8[PubMed]
9. Pizzo E, Pezzoli A, Stockbrugger R, Bracci E, Vagnoni E, Gullini S. Screenee perception and health-related quality of life in colorectal cancer screening: a review. Value Health. 2011; 14(1): 152-9[DOI][PubMed]
10. Chen JJ. Parkinson's disease: health-related quality of life, economic cost, and implications of early treatment. Am J Manag Care. 2010; 16-93[PubMed]
11. Taphoorn MJ, Sizoo EM, Bottomley A. Review on quality of life issues in patients with primary brain tumors. Oncologist. 2010; 15(6): 618-26[DOI][PubMed]
12. Antoniu SA. Effects of inhaled therapies on health-related quality of life in stable chronic obstructive pulmonary disease. Expert Rev Pharmacoecon Outcomes Res. 2010; 10(2): 155-62[DOI][PubMed]
13. Chase DM, Watanabe T, Monk BJ. Assessment and significance of quality of life in women with gynecologic cancer. Future Oncol. 2010; 6(8): 1279-87[DOI][PubMed]
14. Porter AC, Vijil JC, Jr., Unruh M, Lora C, Lash JP. Health-related quality of life in Hispanics with chronic kidney disease. Transl Res. 2010; 155(4): 157-63[DOI][PubMed]
15. Taylor RM, Wray J, Gibson F. Measuring quality of life in children and young people after transplantation: methodological considerations. Pediatr Transplant. 2010; 14(4): 445-58[DOI][PubMed]
16. Reimer T, Gerber B. Quality-of-life considerations in the treatment of early-stage breast cancer in the elderly. Drugs Aging. 2010; 27(10): 791-800[DOI][PubMed]
17. Singh J, Trabulsi EJ, Gomella LG. The quality-of-life impact of prostate cancer treatments. Curr Urol Rep. 2010; 11(3): 139-46[DOI][PubMed]
18. Lemieux J, Goodwin PJ, Bordeleau LJ, Lauzier S, Theberge V. Quality-of-life measurement in randomized clinical trials in breast cancer: an updated systematic review (2001-2009). J Natl Cancer Inst. 2011; 103(3): 178-231[DOI][PubMed]
19. Goretti B, Portaccio E, Zipoli V, Razzolini L, Amato MP. Coping strategies, cognitive impairment, psychological variables and their relationship with quality of life in multiple sclerosis. Neurol Sci. 2010; 31-30[DOI][PubMed]
20. Fan SY, Eiser C, Ho MC. Health-related quality of life in patients with hepatocellular carcinoma: a systematic review. Clin Gastroenterol Hepatol. 2010; 8(7): 559-64[PubMed]
21. Chambers A, Routledge T, Pilling J, Scarci M. In elderly patients with lung cancer is resection justified in terms of morbidity, mortality and residual quality of life? Interact Cardiovasc Thorac Surg. 2010; 10(6): 1015-21[DOI][PubMed]
22. Rogers SN. Quality of life perspectives in patients with oral cancer. Oral Oncol. 2010; 46(6): 445-7[DOI][PubMed]
23. Miller DM, Allen R. Quality of life in multiple sclerosis: determinants, measurement, and use in clinical practice. Curr Neurol Neurosci Rep. 2010; 10(5): 397-406[DOI][PubMed]
24. Landreneau K, Lee K, Landreneau MD. Quality of life in patients undergoing hemodialysis and renal transplantation--a metaanalytic review. Nephrol Nurs J. 2010; 37(1): 37-44[PubMed]
25. Brown EA. Can quality of life be improved for the increasing numbers of older patients with end-stage kidney disease? Expert Rev Pharmacoecon Outcomes Res. 2010; 10(6): 661-6[DOI][PubMed]
26. Elliott S, Latini DM, Walker LM, Wassersug R, Robinson JW. Androgen deprivation therapy for prostate cancer: recommendations to improve patient and partner quality of life. J Sex Med. 2010; 7(9): 2996-3010[DOI][PubMed]
27. Luckett T, King M, Butow P, Friedlander M, Paris T. Assessing health-related quality of life in gynecologic oncology: a systematic review of questionnaires and their ability to detect clinically important differences and change. Int J Gynecol Cancer. 2010; 20(4): 664-84[DOI][PubMed]
28. Jansen L, Koch L, Brenner H, Arndt V. Quality of life among longterm (>/=5 years) colorectal cancer survivors--systematic review. Eur J Cancer. 2010; 46(16): 2879-88[DOI][PubMed]
29. Soni RK, Porter AC, Lash JP, Unruh ML. Health-related quality of life in hypertension, chronic kidney disease, and coexistent chronic health conditions. Adv Chronic Kidney Dis. 2010; 17(4)-26[PubMed]
30. Ware JE, Jr., Gandek B. Overview of the SF-36 Health Survey and the International Quality of Life Assessment (IQOLA) Project. J Clin Epidemiol. 1998; 51(11): 903-12[DOI][PubMed]
31. Ware JE, Kosinski M, Dewey JE, Gandek B. SF-36 health survey: manual and interpretation guide. 2000;
32. Assarrodi A, Golafshani A Akaberi S. [The relationship between spiritual well-being and quality of life in nurses]. J North Khorasan Univ Med Sci. 2012; 3(4 (10)): 79-86
33. Allaf Javadi M, Parandeh A, Ebadi A, Haji Amini Z. Comparison of life quality between special care units and internal-surgical nurses. Iran J Critic Care Nurs. 2010; 3(3): 9-10
34. Aghamolaei T, Tavafian SS. Determinants of Health Related Quality of Life on People Living in Bandar Abbas, Iran. Iran J Public Health. 2011; 40(3)[PubMed]
35. Daher AM, Ibrahim HS, Daher TM, Anbori AK. Health related quality of life among Iraqi immigrants settled in Malaysia. BMC Public Health. 2011; 11: 407[DOI][PubMed]
36. Tajvar M, Arab M, Montazeri A. Determinants of health-related quality of life in elderly in Tehran, Iran. BMC Public Health. 2008; 8: 323[DOI][PubMed]
37. Thumboo J, Fong KY, Machin D, Chan SP, Soh CH, Leong KH, et al. Quality of life in an urban Asian population: the impact of ethnicity and socio-economic status. Soc Sci Med. 2003; 56(8): 1761-72[DOI][PubMed]

Published by: Kowsar

Quality of Life in Nurses Working in Neyshabur Hospitals

Article information

Abstract

1.Background

2. Objectives

3. Materials and Methods

4. Results

5. Discussion

Acknowledgements

Footnotes

References

Readers' Comments

Thrita

Published by: Kowsar

Quality of Life in Nurses Working in Neyshabur Hospitals

Article information

Abstract

1.Background

2. Objectives

3. Materials and Methods

4. Results

5. Discussion

Acknowledgements

Footnotes

References

Share on:

Readers' Comments:

Readers' Comments