ORIGINAL ARTICLE
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Predictors of the Health-Promoting Behaviors of Nepalese Migrant Workers Pratibha Bhandari1* & MiYoung Kim2 1
PhD, Assistant Professor, Department of Nursing, Woosong University, South Korea & 2PhD, Associate Professor, Division of Nursing Science, Ewha Womans University, South Korea.
ABSTRACT Background: Health-promoting behaviors assist individuals to prevent disease, promote health, increase longevity, and enjoy a better quality of life. A number of interpersonal, social, and environmental factors have been shown to influence healthpromoting behaviors. Little empirical evidence exists about the predictors of health-promoting behaviors among migrant workers. Purpose: This study uses Pender’s health promotion model to describe and identify the predictors of health-promoting behaviors in Nepalese migrant workers in Korea. Methods: A cross-sectional research design was used. Nepalese migrants who had been working in South Korea (n = 169) for over 6 months were surveyed between July and December 2012. Self-efficacy was measured using the Perceived Health Competence Scale, the Health-Promoting Lifestyle Profile II was used to measure health-promoting lifestyle behaviors, and perceived health status was measured using a single-item question. Descriptive statistics, correlation analysis, and multiple regression analysis were used to analyze data. Results: Spiritual activity was the highest reported healthpromoting behavior, whereas physical activity was the least practiced behavior. Self-efficacy was the only significant predictor of health-promoting behavior. Conclusions: The results of this study suggest that future health-promoting interventions should enhance the selfefficacy of target populations for individual health behaviors. Factors such as working conditions, culture, and economic background that may affect the health-promoting behaviors of migrant workers must be considered when planning nursing interventions. Multicultural nursing structures and policies are needed to reach out proactively to all adult migrant groups.
KEY WORDS: health-promoting behavior, migrant workers, health promotion model, self-efficacy, multicultural nursing.
Introduction Health-promoting behavior (HPB) was defined theoretically by Nola Pender as ‘‘an expression of human actualizing tendency that is directed toward optimal wellbeing, personal
fulfillment, and productive living’’ (Pender, Murdaugh, & Parsons, 2011). Adoption of HPB assists individuals to prevent disease and promote health and thereby increase longevity and enjoy a better quality of life (Khaw et al., 2008; Mo & Winnie, 2009; Pierce, 2005). In addition, HPB reduces the risks of chronic and communicable diseases and of occupational health hazards (Badr & Moody, 2005; Brown, Burton, & Rowan, 2007) and reduces healthcare costs for individuals and families. For many decades, health promotion has been recognized as an important element of health development for all age groups. However, despite the known benefits of HPB, evidence shows that people are hesitant or ignorant in terms of adopting and maintaining desired behaviors (Szmedra, Sharma, & Rozmus, 2009). A number of interpersonal, social, and environmental factors such as perceived health, social support, past experiences, past habits, selfefficacy, and knowledge regarding health-promoting lifestyles have been shown to influence HPBs (Chamroonsawasdi, Phoolphoklang, Nanthamongkolchai, & Munsawaengsub, 2010; Shin, Hur, Pender, Jang, & Kim, 2006; Thanavaro, Moore, Anthony, Narsavage, & Delicath, 2006). Adults who migrate to a foreign country for the purpose of employment are prone to experiencing problems that may be attributed to differences in culture and language, the lack of a social life, harsh working conditions, and high work demands (Wong & Chang, 2010). Nepal has contributed migrant workers to South Korea since the 1980s. Previous research conducted on these migrant workers have highlighted their poor or unsafe physical and mental working conditions (Bhattarai, 2005; Thapa, 2009). The lure of overtime payment further contributes to negligent health behaviors. On Accepted for publication: December 28, 2014 *Address correspondence to: Pratibha Bhandari, Department of Nursing, Building W5, Room 504, Woosong University, #17-2 Jayang-dong, Dong-gu, Daejeon 300-718, Korea. Tel: +82 1043907774; E-mail: [email protected] The authors declare no conflicts of interest. Cite this article as: Bhandari, P., & Kim, M. (2015). Predictors of the health-promoting behaviors of Nepalese migrant workers. The Journal of Nursing Research, 00(0), 00Y00. doi:10.1097/jnr.0000000000000120
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many occasions, remaining illegally in South Korea to continue working further limits the access of migrant workers to healthcare institutions. Hence, to promote physical and mental well-being, practicing HPB is of utmost importance for this group. Using Pender’s Health Promotion Model (HPM; Pender et al., 2011) as a guide, this study aims to describe the health-promoting lifestyle profile of adult Nepalese workers in South Korea and to identify the predictors of HPB among this population. It is expected that the findings of this research will provide a better understanding of the health promotion lifestyle profile of Nepalese workers and directions for designing related nursing interventions and policies to promote their health.
Pratibha Bhandari et al.
nized as an important factor in HPBs such as smoking cessation (Badr & Moody, 2005), exercise (Shin et al., 2006), healthy sexual practices (Burns & Dillon, 2005), healthy dietary behavior (Kreausukon, Gellert, Lippke, & Schwarzer, 2012), and the management of chronic diseases (King et al., 2010). Highly self-efficacious individuals are relatively more able to maintain desired HPBs (Kye & Park, 2012; Loeb, Steffensmeier, & Kassab, 2011). The aims of this study were to describe the HPB status of Nepalese workers in Korea, to identify factors (individual characteristics and behavior-specific cognition and affect) that affect HPB, and to identify the predictors of HPB.
Methods Background Health promotion is the process of enabling people to increase control over their health and its determinants and thereby improving their health (World Health Organization, 2009). The revised HPM developed by Pender et al. was used as a guide for this study (Pender et al., 2011). The model consists of three conceptual components: ‘‘individual characteristics and experiences,’’ ‘‘behavior-specific cognitions and affect,’’ and ‘‘behavioral outcome.’’ Individual characteristics and behavior-specific cognition and affect are viewed as predictors of desired behaviors, whereas HPB is the action or behavioral outcome. In the current study, based on available evidence, components from the HPM were parsimoniously selected. Age, gender, perceived health status, occupation, education, and employment status were included as individual characteristic variables, and perceived selfefficacy was measured as behavior-specific cognition and affect variables. In line with the HPM, the outcome variable in this study was HPB, which included six domains: health responsibility, physical activity, nutrition, spiritual growth, interpersonal relations, and stress management. Disparities in the various health behaviors among adult workers have been attributed to differences in demographics, whereas higher age and higher income have been positively associated with better HPBs (Bes¸er, Bahar, & Bu¨yu¨kkaya, 2007); similarly, office workers and service workers with higher levels of education have been shown to have better health-promoting practices than manual laborers (Huang, Li, & Tang, 2010). Perceived health status refers to a subjective measurement of a person’s well-being and physical health and is recognized as a valid health monitoring indicator (Jylha¨, 2009; Miilunpalo, Vuori, Oja, Pasanen, & Urponen, 1997; Ng, Tengku-Aizan, & Tey, 2011). Positive perception of one’s health status leads to active engagement in HPB (Lim, Ma, Heng, Bhalla, & Chew, 2007; Wald, 2010) and fewer contacts with physicians per year (Miilunpalo et al., 1997; Ng et al., 2011). Perceived self-efficacy addresses the belief of an individual in his or her personal control over self-motivation, personal behavior, and his or her social environment (Bandura, 1982, 1990). Perceived self-efficacy has been increasingly recog-
Study Design A cross-sectional research design was used in this study.
Setting and Samples A convenience sample of Nepalese adults who were currently living and working in South Korea were recruited for this study. To ensure that the participants had at least some time to acclimate to their new environment and work, adults who had been residing in Korea for less than 3 months and who were not yet employed were excluded from the study. GPower was used to calculate the necessary sample size. An effect size of .15, a power of .95, and an alpha of .05 were used for the calculation. The required sample size was 146. The sample size in the current study was 169.
Data Collection Data were collected using self-reported questionnaires between July and December 2012. Participants were recruited from religious organizations and during social events organized by religious organizations from four cities in South Korea. All participants who were approached agreed to participate in the study. Permission was sought from the heads of the concerned organizations.
Measurements General demographic information was collected using a questionnaire that was developed by the researchers. Perceived health status was measured using a single item question: ‘‘How is your health in general?’’ Responses were scored on a Likert scale of ‘‘very bad,’’ ‘‘bad,’’ ‘‘fair,’’ ‘‘good,’’ and ‘‘very good,’’ with scores ranging, respectively, from 1 to 5 and higher scores associated with better perceived health. Perceived self-efficacy was measured using the Perceived Health Competence Scale (Smith, Wallston, & Smith, 1995). Responses were scored on a Likert scale, and possible total scores for the scale ranged from 8 to 40, with higher values indicating stronger perceived self-efficacy. This instrument has been identified as a valid and reliable instrument for assessing self-efficacy in the domain of health behavior, with
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Predictors of Health-Promoting Behaviors
an internal consistency reported between .82 and .90 (Smith et al., 1995). The Health-Promoting Lifestyle Profile II (HPLP II) was used to measure health-promoting lifestyle behaviors. The HPLP II contains 52 items that are used to measure the frequency of HPBs in six domains: health responsibility (nine items), physical activity (eight items), nutrition (nine items), spiritual growth (nine items), interpersonal relations (nine items), and stress management (eight items). Total score ranges from 52 to 208, with higher scores indicating more desirable HPBs. Responses are scored using a 4-point response format of never, sometimes, often, and routinely. The alpha coefficient for internal consistency for the total scale has been reported as .94, and the 3-week testYretest stability coefficient has been reported as .89 (Walker & Hill-Polerecky, 1996; Walker, Sechrist, & Pender, 1987). The questionnaires were translated into the Nepalese language (Nepali) using the standard forward and backward translation method. The questionnaire was first translated from English into Nepali by a bilingual researcher, which was then checked for consistency in meaning by another bilingual person. Corrections were made wherever required. The corrected version was then back translated into English by another unrelated bilingual, nonnursing person. The translated version was then compared against the original English version. Any discrepancy in meaning was identified, retranslated, and back translated until a satisfactory result was obtained. The Cronbach’s alphas for the perceived self-efficacy scale and for the HPLP in our study were .71 and .93, respectively. The Cronbach’s alphas for the HPLP domains of health responsibility, physical activity, nutrition, spiritual growth, interpersonal relationships, and stress management were .82, .82, .72, .71, .74, and .65, respectively.
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The detailed demographic information for the subjects is provided in Table 1. The mean perceived health score was 3.42 (0.80), and the mean perceived self-efficacy was 26.98 (4.89). There were no significant differences in perceptions of health and self-efficacy based on gender, marital status, work type, or educational background (results not displayed). The mean HPLP score was 137.71 (20.19). ‘‘Spiritual growth’’ was the highest reported HPB practiced (27.3 T 3.93), whereas physical activity was the least practiced HPB (17.9 T 4.95; please refer to Table 2 for details).
Correlation Between Health-Promoting Behaviors and Components of the Health Promotion Model As shown in Table 3, correlational analysis was conducted among the HPLP components and the components of TABLE 1.
General Demographic Variables (N = 169) n Gender Male Female
Ethical Considerations
Data Analysis Data analysis was conducted using IBM SPSS Version 19. Descriptive statistics, correlation, and multiple linear regression analysis were used to analyze the collected data. A p value of .05 or less was considered statistically significant.
Results Sample Characteristics The total number of subjects in this study was 169. The subjects were between the ages of 21 and 55 years; most were male, married, and working in Korean companies as manual laborers. Almost half of the subjects perceived their health as ‘‘fair,’’ and around 37% perceived their health as ‘‘good.’’
150 19
88.8 11.2
Marital statusa Married Unmarried Separated
80 85 3
47.3 50.3 1.8
Religiona Buddhist Hindu Christian No religion Others
6 86 66 1 5
3.6 50.9 39.1 0.6 3.0
1 13 26 125
0.6 7.7 15.4 74.0
Work areaa Manual labor Domestic work Construction Office Othersb
60 7 38 7 50
35.5 4.1 22.5 4.1 29.6
Perceived health Very bad Bad Fair Good Very good
1 17 74 63 14
0.6 10.1 43.8 37.3 8.3
Age (M, SD)
29.51
Daily working hours (M, SD)
11.46
Educationa Primary Secondary Higher secondary College and above
Permission was obtained from the institution review board of the concerned university. Informed individual consent was obtained from all participants. Participation was fully voluntary and anonymous.
%
a
6.28 1.18 b
All data may not total up to 100% because of missing data. Agriculture; animal husbandry.
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Pratibha Bhandari et al.
TABLE 2.
Health Promoting Lifestyle Profile (HPLP), Perceived Health, and Perceived Self-Efficacy (N = 169) Possible Range
Observed Range
Mean
SD
52Y208 9Y36 8Y32 9Y36 9Y36 9Y36 8Y32
91Y195 9Y36 8Y32 13Y36 17Y36 18Y36 12Y32
137.70 22.05 17.91 23.47 27.30 25.90 21.41
20.19 4.61 4.95 4.22 3.93 3.84 3.85
Total self-efficacy
8Y40
15Y40
26.98
4.89
Perceived health
1Y5
1Y5
3.42
0.80
Total HPLP Health responsibility Physical activity Nutrition Spiritual growth Interpersonal relationships Stress management
TABLE 3.
Correlations Among Individual Characteristics, Behavior-Specific Cognition, and HPLP (N = 169) Age
Gender
Educational Status
Marital Status
Perceived Health
Perceived Self-Efficacy
Age Gender
.130
Educational status
j.243**
j.108
Marital status
j.501**
j.081
j.033
.034
.068
.194*
j.145
j.109
j.006
.099
j.056
Total HPLP
.016
j.007
j.102
.010
j.065
Health responsibility
.016
.042
j.091
.010
j.149
Physical activity
.055
.010
j.149
j.029
.002
.214**
j.001
.139
j.081
j.073
.040
.197*
.047
.061
j.012
j.012
.056
.154*
Perceived health Perceived self-efficacy
Nutrition Spiritual growth
.388** .180* .090
Interpersonal relationships
j.052
j.032
.073
.073
j.039
.129
Stress management
j.025
j.038
j.015
j.015
j.012
.163*
Note. HPLP = Health Promoting Lifestyle Profile. *p G .05. **p G .001.
HPM, including individual characteristics (age, gender, marital status, educational level, and perceived health) and behavior-specific cognition (perceived self-efficacy), using the Pearson’s correlation test. The results showed that selfefficacy was weakly but significantly and positively correlated with physical activity, nutrition, spiritual growth, and stress management. Self-efficacy was also moderately, positively, and significantly correlated with perceived health status. No significant correlation was detected between individual characteristics and HPLP.
observed among the variables. As shown in Table 4, individual characteristics (age, gender, educational status, and perceived health) were entered in the first block, and perceived self-efficacy was entered in the second block. The first model (block) explained only about 1.4% (p 9 .05) of the total variance in HPLP. The addition of perceived selfefficacy in the second model increased the explained variance to 4.1% (p G .05). Perceived self-efficacy was the only significant predictor of HPLP (" = .22, p G .05) in our study.
Predictors of Health-Promoting Behaviors
Using the HPM as a theoretical framework, the aim of our study was to describe the HPB and identify the predictors of HPB in Nepalese adults working in South Korea. Most of our study samples were manual workers who were working
Discussion Multiple regression analysis was conducted to identify the predictors of HPB. The assumptions for normality, linearity, and homoscedasticity were met, and no multicollinearity was 4 Copyright © 2015 Taiwan Nurses Association. Unauthorized reproduction of this article is prohibited.
Predictors of Health-Promoting Behaviors
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TABLE 4.
Predictors of Health-Promoting Behaviors (N = 169) Model 1 B
Variable Age
SE B
Model 2 "
B
SE B
0.12
0.28
.035
0.176
Gender
j0.87
5.24
j.013
j0.187
5.15
j.003
Educational level
j2.69
2.63
j.085
j2.74
2.58
j.087
Perceived health
j1.20
2.03
j.048
j3.37
2.16
j.136
Perceived self-efficacy R2 F for change in R2 (df )
0.912 .014 0.53 (4, 155)
0.279
"
0.352 .055 6.71 (1, 154)*
.051
.221*
*p G .05.
in various jobs in the construction and agricultural sectors and who perceived their health as fair. Higher education correlated with better perceived health status, which may be because of the greater access that educated people typically have to health information. However, the meaning of ‘‘perceived health’’ may differ by culture. Hence, further in-depth exploration may be beneficial to obtain culture-specific findings. The total HPLP score reported by our study participants was lower than the score reported by immigrant women in the United Sates (Cha, 2010) and higher than those of workers in the food industry in Turkey (Bes¸er et al., 2007) and of skilled and unskilled workers in Taiwan (Huang et al., 2010). The most frequently practiced health behavior was spiritual growth, followed by interpersonal relationships, nutrition, health responsibility, and stress management. As shown in demographic information (Table 1), most of the participants expressed holding religious beliefs such as Buddhism, Hinduism, or Christianity. Nepalese are generally considered to be religious people who derive inner peace and strength through their connection with their god(s) and deities (Bhandari, 2014). However, no specific evidence currently associates spirituality and health behavior or practices. Further studies may be directed to explore this association. Physical activity was the least practiced health behavior in our study, although most participants were menVa finding that contrasts with previous studies (Bhandari, 2014; Vaidya & Krettek, 2014). This may be because of the heavy workload associated with working in a foreign land. In addition, this feature is characteristic of most Nepalese adults who, despite being aware of the ill effects of physical inactivity, often fail to put this knowledge into practice (Simkhada, Poobalan, Simkhada, Amalraj, & Aucott, 2011). The causes for this may be multifactorial and include factors such as ignorance about preventive and promotive healthcare, limited health literacy, inadequate infrastructures, and the obesogenic environment (Vaidya, Shakya, & Krettek, 2010). Therefore, nurses should also consider the working conditions and economic backgrounds of migrant workers when planning interventions and policies. In contrast to previous studies, it is interesting to note that, despite higher educational status, participants in this
study still exhibited a limited practice of health behaviors. Knowledge regarding health behavior is an important predictor for engaging in health behavior (Chamroonsawasdi et al., 2010). Therefore, it is important that nurses provide adequate health education about HPB. One reason for the low level of HPB in our study may be lack of adequate time (Bhattarai, 2005). Most participants worked an average of 11.4 hours on weekdays. Another reason may be prior lifestyle habits that participants continued to follow in Korea (Gadd, Sundquist, Johansson, & Wa¨ndell, 2005). In view of long working hours (11.4 hours), it is recommended that the working environment be provided with equipment and infrastructures for physical exercise for the use of workers during rest hours. Access to health resources in this way may also provide critical cues necessary for adopting HPBs. Another important nursing implication that may be derived is that nurses must proactively reach out to community-dwelling adults who may or may not have any diagnosed illnesses. The innovative use of media, public campaigns, and other public awareness programs may be used for this purpose (Long et al., 2013). The involvement of the media in promoting health behaviors is currently largely limited in developing countries to the prevention of communicable diseases. Most of the study participants perceived their health as being fair or good. Therefore, perceived health did not predict health behaviors. This finding is in contrast to a previously reported study (Bes¸er et al., 2007). It was interesting to note that, although most participants perceived their health as fair to good, their engagement in health-promoting self-care behavior was comparatively low. Health illiteracy is common among Nepalese adults, and preventive care is often neglected (Vaidya et al., 2010). Obesity or undernutrition among adults is not looked upon as ‘‘unhealthy,’’ and only chronic or life-threatening illnesses are counted as ‘‘being sick.’’ Griffith, Lovett, Pyle, and Miller (2011) reported on the incongruences among perceived health, health behaviors, and objective health. This underscores the need to target specific health behaviors such as physical activity, nutrition, and individual health responsibilities and to not cluster all under the catch-all phrase of ‘‘health behaviors.’’ 5
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This study was based on Pender’s revised HPM. The HPM describes individual characteristics as important components that affect and predict subsequent health-related behaviors. Similarly, behavior-specific cognition is another important predictor of HPB. In our study, the selected variables for individual characteristics such as age, educational level, and perceived health did not predict HPB. However, it should be pointed out that not all of the components of the model were included. Future model-testing studies that use all the variables from the model may better explain HPBs. In line with the HPM and previous studies (Badr & Moody, 2005; Gutie´rrez-Don˜a, Lippke, Renner, Kwon, & Schwarzer, 2009; King et al., 2010), self-efficacy, which was selected as a behavior-specific cognition variable in our study, was found to significantly predict HPBs. The HPM regards behaviorspecific cognition variables as the ‘‘core’’ toward which nursing interventions must be directed. Healthcare professionals must use creative ways to promote self-efficacy among adults, even when language is a barrier to communication. Technology may be used to facilitate effective interventions (King et al., 2013; Wolfenden et al., 2012). With the recent advancements in technology and the availability of high-speed Internet in most countries, it is also possible to design educational nursing interventions through the use of videos to promote self-efficacy as an adjunct to other methods. Although the essence of one-to-one communication cannot be substituted completely with videos or other similar aids, it nevertheless calls for creativity on the nurses’ part in dealing with multicultural clients. The total variance explained by our regression model was significant but quite low (5.5%). This suggests that there may be other unidentified variables that explain the HPBs in this group of adults. As mentioned above, the variables for this research were parsimoniously selected based on the available evidence (Bes¸er et al., 2007; Chamroonsawasdi et al., 2010; Huang et al., 2010). Thus, not all of the variables of the HPM were included in our study. Inclusion of other variables such as social support, health literacy, perceived benefits, and perceived barriers may provide better insights about the relationships between the factors that influence health promotion behaviors. Hence, future studies should include these other variables. In addition, in-depth analysis through qualitative inquiries may provide insights about the cultural meaning that specific groups of adults associate with health promotion, which may facilitate the planning of further research and subsequent development of nursing interventions. Finally, this research highlights the need for occupational health nurses to be able to play a more significant role in promoting preventive healthcare behaviors to workers. Nurses must understand various work environment factors and the cultural and economic background of migrant workers that may influence their HPBs. Nurse leaders and policy makers are urged to actively advocate for the needs of occupational and multicultural nursing structures and facilities, especially in countries where the immigrant population is on the
Pratibha Bhandari et al.
rise, as a way to reduce disparities in health and access to healthcare.
Limitations This study was subject to several limitations. First, the internal consistency of one of the subitems (stress management) of the HPLP was low (.65), which may have lowered the reliability of the measure. Second, this was a crosssectional study. Therefore, no causation may be implied. Third, the study sample was mostly limited to unskilled workers, so the results should be generalized carefully. However, most of the participants held college degrees. Moreover, because the study samples were predominantly men, generalization of the study findings may not be applicable to women. Finally, the use of self-report surveys may have introduced bias.
Conclusions Adopting HPBs is associated with disease prevention and a better quality of life. A number of individual and behavioral factors influence the choice and adoption of HPBs. Using HPM, this study examined the various individual and behaviorspecific variables that predicted HPBs in a group of Nepalese migrant workers who were employed in South Korea. The results suggest that self-efficacy significantly predicts HPBs. Therefore, future health promotion programs should include strategies that are designed to maximize the self-efficacy of these workers. However, provision of care for multicultural immigrant populations is a challenge in terms of human and material resources. Hence, nurses are encouraged to use innovative ways to facilitate this process. In addition, nurse leaders and policy makers should actively advocate for the expansion of promotional and preventive healthcare services to all groups of the population. It is recommended that future studies be conducted with other variables from the HPM to assess the role of HPM variables in influencing health behaviors. Furthermore, studies to identify the influence of culture on health-promoting behaviors will be useful in planning tailored and culturally sensitive nursing interventions and policies.
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Kreausukon, P., Gellert, P., Lippke, S., & Schwarzer, R. (2012). Planning and self-efficacy can increase fruit and vegetable consumption: A randomized controlled trial. Journal of Behavioral Medicine, 35(4), 443Y451. doi:10.1007/s10865011-9373-1 Kye, S. Y., & Park, K. (2012). Psychosocial factors and health behavior among Korean adults: A cross-sectional study. Asian Pacific Journal of Cancer Prevention, 13(1), 49Y56. doi:10.7314/ APJCP.2012.13.1.049 Lim, W. Y., Ma, S., Heng, D., Bhalla, V., & Chew, S. K. (2007). Gender, ethnicity, health behaviour & self-rated health in Singapore. BMC Public Health, 7, 184. doi:10.1186/1471-24587-184 Loeb, S. J., Steffensmeier, D., & Kassab, C. (2011). Predictors of self-efficacy and self-rated health for older male inmates. Journal of Advanced Nursing, 67(4), 811Y820. doi:10.1111/ j.1365-2648.2010.05542.x Long, K. N., Gren, L. H., Rees, C. A., West, J. H., Hall, P. C., Gray, B., & Crookston, B. T. (2013). Determinants of better health: A cross-sectional assessment of positive deviants among women in West Bengal. BMC Public Health, 13, 372. doi:10.1186/1471-2458-13-372 Miilunpalo, S., Vuori, I., Oja, P., Pasanen, M., & Urponen, H. (1997). Self-rated health status as a health measure: The predictive value of self reported health status on the use of physician services and on mortality in the working-age population. Journal of Clinical Epidemiology, 50(5), 517Y528. doi:10 .1016/S0895-4356(97)00045-0 Mo, P. K. H., & Winnie, W. S. M. (2009). The influence of health promoting practices on the quality of life of community adults in Hong Kong. Social Indicators Research, 95(3), 503Y517. doi:10 .1007/s11205-009-9523-9 Ng, S. T., Tengku-Aizan, H., & Tey, N. P. (2011). Perceived health status and daily activity participation of older Malaysians. Asia-Pacific Journal of Public Health, 23(4), 470Y484. doi:10 .1177/1010539510374751 Pender, N., Murdaugh, C. L., & Parsons, M. A. (2011). Health promotion in nursing practice (6th ed.). Upper Saddle, NJ: Pearson Education. Pierce, C. S. (2005). Health promotion behaviors of rural women with heart failure. Online Journal of Rural Nursing and Health Care, 5(2), 28Y37. Shin, Y. H., Hur, H. K., Pender, N. J., Jang, H. J., & Kim, M. S. (2006). Exercise self-efficacy, exercise benefits and barriers, and commitment to a plan for exercise among Korean women with osteoporosis and osteoarthritis. International Journal of Nursing Studies, 43(1), 3Y10. doi:10.1016/j.ijnurstu.2004.10.008 Simkhada, P., Poobalan, A., Simkhada, P. P., Amalraj, R., & Aucott, L. (2011). Knowledge, attitude, and prevalence of overweight and obesity among civil servants in Nepal. AsiaPacific Journal of Public Health, 23(4), 507Y517. doi:10.1177/ 1010539509348662 Smith, M. S., Wallston, K. A., & Smith, C. A. (1995). The development and validation of the Perceived Health Competence Scale. Health Education Research, 10(1), 51Y64. doi:10 .1093/her/10.1.51 Szmedra, P., Sharma, K. L., & Rozmus, C. L. (2009). Health promoting behavior among chronically ill Pacificians living with non-communicable disease in Fiji, Nauru, and Kiribati. Pacific Health Dialog, 15(2), 55Y65.
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Thanavaro, J. L., Moore, S. M., Anthony, M., Narsavage, G., & Delicath, T. (2006). Predictors of health promotion behavior in women without prior history of coronary heart disease. Applied Nursing Research, 19(3), 149Y155. doi:10.1016/j.apnr .2005.07.006 Thapa, D. (2009). The role of NGOs in supporting migrant workers in South Korea: The case study of Nepalese migrant workers (Unpublished master’s thesis). Suwon, South Korea: Ajou University. Vaidya, A., & Krettek, A. (2014). Physical activity level and its sociodemographic correlates in a peri-urban Nepalese population: A cross-sectional study from the JhaukhelYDuwakot health demographic surveillance site. The International Journal of Behavioral Nutrition and Physical Activity, 11, 39. doi:10.1186/1479-5868-11-39 Vaidya, A., Shakya, S., & Krettek, A. (2010). Obesity prevalence in Nepal: Public health challenges in a low-income nation during an alarming worldwide trend. International Journal of Environmental Research and Public Health, 7(6), 2726Y2744. doi:10.3390/ijerph7062726 Wald, A. (2010). Relationship of key health-promoting behaviors with self rated health and academic performance in college students (Doctoral dissertation). Ann Arbor, MI: Columbia University.
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Walker, S. N., & Hill-Polerecky, D. M. (1996). Psychometric evaluation of the Health-Promoting Lifestyle Profile II (Unpublished manuscript). Michigan, US: University of Nebraska Medical Center. Walker, S. N., Sechrist, K. R., & Pender, N. J. (1987). The HealthPromoting Lifestyle Profile: Development and psychometric characteristics. Nursing Research, 36(2), 76Y81. Wolfenden, L., Wiggers, J., Paul, C., Freund, M., Lecathelinais, C., Wye, P., & Gillham, K. (2012). Increasing the use of preventative health services to promote healthy eating, physical activity and weight management: The acceptability and potential effectiveness of a proactive telemarketing approach. BMC Public Health, 12, 953. doi:10.1186/1471-2458-12-953 Wong, D. F. K., & Chang, Y. L. (2010). Mental health of Chinese migrant workers in factories in Shenzhen, China: Effects of migration stress and social competence. Social Work in Mental Health, 8(4), 305Y318. doi:10.1080/15332980903217768 World Health Organization. (2009). Milestones in health promotion: Statements from global conferences. Retrieved from http://www.who.int/healthpromotion/Milestones_Health_Promotion_ 05022010.pdf
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Predictors of the Health-Promoting Behaviors of Nepalese Migrant Workers Pratibha Bhandari1* & MiYoung Kim2 1
PhD, Assistant Professor, Department of Nursing, Woosong University, South Korea & 2PhD, Associate Professor, Division of Nursing Science, Ewha Womans University, South Korea.
ABSTRACT Background: Health-promoting behaviors assist individuals to prevent disease, promote health, increase longevity, and enjoy a better quality of life. A number of interpersonal, social, and environmental factors have been shown to influence healthpromoting behaviors. Little empirical evidence exists about the predictors of health-promoting behaviors among migrant workers. Purpose: This study uses Pender’s health promotion model to describe and identify the predictors of health-promoting behaviors in Nepalese migrant workers in Korea. Methods: A cross-sectional research design was used. Nepalese migrants who had been working in South Korea (n = 169) for over 6 months were surveyed between July and December 2012. Self-efficacy was measured using the Perceived Health Competence Scale, the Health-Promoting Lifestyle Profile II was used to measure health-promoting lifestyle behaviors, and perceived health status was measured using a single-item question. Descriptive statistics, correlation analysis, and multiple regression analysis were used to analyze data. Results: Spiritual activity was the highest reported healthpromoting behavior, whereas physical activity was the least practiced behavior. Self-efficacy was the only significant predictor of health-promoting behavior. Conclusions: The results of this study suggest that future health-promoting interventions should enhance the selfefficacy of target populations for individual health behaviors. Factors such as working conditions, culture, and economic background that may affect the health-promoting behaviors of migrant workers must be considered when planning nursing interventions. Multicultural nursing structures and policies are needed to reach out proactively to all adult migrant groups.
KEY WORDS: health-promoting behavior, migrant workers, health promotion model, self-efficacy, multicultural nursing.
Introduction Health-promoting behavior (HPB) was defined theoretically by Nola Pender as ‘‘an expression of human actualizing tendency that is directed toward optimal wellbeing, personal
fulfillment, and productive living’’ (Pender, Murdaugh, & Parsons, 2011). Adoption of HPB assists individuals to prevent disease and promote health and thereby increase longevity and enjoy a better quality of life (Khaw et al., 2008; Mo & Winnie, 2009; Pierce, 2005). In addition, HPB reduces the risks of chronic and communicable diseases and of occupational health hazards (Badr & Moody, 2005; Brown, Burton, & Rowan, 2007) and reduces healthcare costs for individuals and families. For many decades, health promotion has been recognized as an important element of health development for all age groups. However, despite the known benefits of HPB, evidence shows that people are hesitant or ignorant in terms of adopting and maintaining desired behaviors (Szmedra, Sharma, & Rozmus, 2009). A number of interpersonal, social, and environmental factors such as perceived health, social support, past experiences, past habits, selfefficacy, and knowledge regarding health-promoting lifestyles have been shown to influence HPBs (Chamroonsawasdi, Phoolphoklang, Nanthamongkolchai, & Munsawaengsub, 2010; Shin, Hur, Pender, Jang, & Kim, 2006; Thanavaro, Moore, Anthony, Narsavage, & Delicath, 2006). Adults who migrate to a foreign country for the purpose of employment are prone to experiencing problems that may be attributed to differences in culture and language, the lack of a social life, harsh working conditions, and high work demands (Wong & Chang, 2010). Nepal has contributed migrant workers to South Korea since the 1980s. Previous research conducted on these migrant workers have highlighted their poor or unsafe physical and mental working conditions (Bhattarai, 2005; Thapa, 2009). The lure of overtime payment further contributes to negligent health behaviors. On Accepted for publication: December 28, 2014 *Address correspondence to: Pratibha Bhandari, Department of Nursing, Building W5, Room 504, Woosong University, #17-2 Jayang-dong, Dong-gu, Daejeon 300-718, Korea. Tel: +82 1043907774; E-mail: [email protected] The authors declare no conflicts of interest. Cite this article as: Bhandari, P., & Kim, M. (2015). Predictors of the health-promoting behaviors of Nepalese migrant workers. The Journal of Nursing Research, 00(0), 00Y00. doi:10.1097/jnr.0000000000000120
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many occasions, remaining illegally in South Korea to continue working further limits the access of migrant workers to healthcare institutions. Hence, to promote physical and mental well-being, practicing HPB is of utmost importance for this group. Using Pender’s Health Promotion Model (HPM; Pender et al., 2011) as a guide, this study aims to describe the health-promoting lifestyle profile of adult Nepalese workers in South Korea and to identify the predictors of HPB among this population. It is expected that the findings of this research will provide a better understanding of the health promotion lifestyle profile of Nepalese workers and directions for designing related nursing interventions and policies to promote their health.
Pratibha Bhandari et al.
nized as an important factor in HPBs such as smoking cessation (Badr & Moody, 2005), exercise (Shin et al., 2006), healthy sexual practices (Burns & Dillon, 2005), healthy dietary behavior (Kreausukon, Gellert, Lippke, & Schwarzer, 2012), and the management of chronic diseases (King et al., 2010). Highly self-efficacious individuals are relatively more able to maintain desired HPBs (Kye & Park, 2012; Loeb, Steffensmeier, & Kassab, 2011). The aims of this study were to describe the HPB status of Nepalese workers in Korea, to identify factors (individual characteristics and behavior-specific cognition and affect) that affect HPB, and to identify the predictors of HPB.
Methods Background Health promotion is the process of enabling people to increase control over their health and its determinants and thereby improving their health (World Health Organization, 2009). The revised HPM developed by Pender et al. was used as a guide for this study (Pender et al., 2011). The model consists of three conceptual components: ‘‘individual characteristics and experiences,’’ ‘‘behavior-specific cognitions and affect,’’ and ‘‘behavioral outcome.’’ Individual characteristics and behavior-specific cognition and affect are viewed as predictors of desired behaviors, whereas HPB is the action or behavioral outcome. In the current study, based on available evidence, components from the HPM were parsimoniously selected. Age, gender, perceived health status, occupation, education, and employment status were included as individual characteristic variables, and perceived selfefficacy was measured as behavior-specific cognition and affect variables. In line with the HPM, the outcome variable in this study was HPB, which included six domains: health responsibility, physical activity, nutrition, spiritual growth, interpersonal relations, and stress management. Disparities in the various health behaviors among adult workers have been attributed to differences in demographics, whereas higher age and higher income have been positively associated with better HPBs (Bes¸er, Bahar, & Bu¨yu¨kkaya, 2007); similarly, office workers and service workers with higher levels of education have been shown to have better health-promoting practices than manual laborers (Huang, Li, & Tang, 2010). Perceived health status refers to a subjective measurement of a person’s well-being and physical health and is recognized as a valid health monitoring indicator (Jylha¨, 2009; Miilunpalo, Vuori, Oja, Pasanen, & Urponen, 1997; Ng, Tengku-Aizan, & Tey, 2011). Positive perception of one’s health status leads to active engagement in HPB (Lim, Ma, Heng, Bhalla, & Chew, 2007; Wald, 2010) and fewer contacts with physicians per year (Miilunpalo et al., 1997; Ng et al., 2011). Perceived self-efficacy addresses the belief of an individual in his or her personal control over self-motivation, personal behavior, and his or her social environment (Bandura, 1982, 1990). Perceived self-efficacy has been increasingly recog-
Study Design A cross-sectional research design was used in this study.
Setting and Samples A convenience sample of Nepalese adults who were currently living and working in South Korea were recruited for this study. To ensure that the participants had at least some time to acclimate to their new environment and work, adults who had been residing in Korea for less than 3 months and who were not yet employed were excluded from the study. GPower was used to calculate the necessary sample size. An effect size of .15, a power of .95, and an alpha of .05 were used for the calculation. The required sample size was 146. The sample size in the current study was 169.
Data Collection Data were collected using self-reported questionnaires between July and December 2012. Participants were recruited from religious organizations and during social events organized by religious organizations from four cities in South Korea. All participants who were approached agreed to participate in the study. Permission was sought from the heads of the concerned organizations.
Measurements General demographic information was collected using a questionnaire that was developed by the researchers. Perceived health status was measured using a single item question: ‘‘How is your health in general?’’ Responses were scored on a Likert scale of ‘‘very bad,’’ ‘‘bad,’’ ‘‘fair,’’ ‘‘good,’’ and ‘‘very good,’’ with scores ranging, respectively, from 1 to 5 and higher scores associated with better perceived health. Perceived self-efficacy was measured using the Perceived Health Competence Scale (Smith, Wallston, & Smith, 1995). Responses were scored on a Likert scale, and possible total scores for the scale ranged from 8 to 40, with higher values indicating stronger perceived self-efficacy. This instrument has been identified as a valid and reliable instrument for assessing self-efficacy in the domain of health behavior, with
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Predictors of Health-Promoting Behaviors
an internal consistency reported between .82 and .90 (Smith et al., 1995). The Health-Promoting Lifestyle Profile II (HPLP II) was used to measure health-promoting lifestyle behaviors. The HPLP II contains 52 items that are used to measure the frequency of HPBs in six domains: health responsibility (nine items), physical activity (eight items), nutrition (nine items), spiritual growth (nine items), interpersonal relations (nine items), and stress management (eight items). Total score ranges from 52 to 208, with higher scores indicating more desirable HPBs. Responses are scored using a 4-point response format of never, sometimes, often, and routinely. The alpha coefficient for internal consistency for the total scale has been reported as .94, and the 3-week testYretest stability coefficient has been reported as .89 (Walker & Hill-Polerecky, 1996; Walker, Sechrist, & Pender, 1987). The questionnaires were translated into the Nepalese language (Nepali) using the standard forward and backward translation method. The questionnaire was first translated from English into Nepali by a bilingual researcher, which was then checked for consistency in meaning by another bilingual person. Corrections were made wherever required. The corrected version was then back translated into English by another unrelated bilingual, nonnursing person. The translated version was then compared against the original English version. Any discrepancy in meaning was identified, retranslated, and back translated until a satisfactory result was obtained. The Cronbach’s alphas for the perceived self-efficacy scale and for the HPLP in our study were .71 and .93, respectively. The Cronbach’s alphas for the HPLP domains of health responsibility, physical activity, nutrition, spiritual growth, interpersonal relationships, and stress management were .82, .82, .72, .71, .74, and .65, respectively.
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The detailed demographic information for the subjects is provided in Table 1. The mean perceived health score was 3.42 (0.80), and the mean perceived self-efficacy was 26.98 (4.89). There were no significant differences in perceptions of health and self-efficacy based on gender, marital status, work type, or educational background (results not displayed). The mean HPLP score was 137.71 (20.19). ‘‘Spiritual growth’’ was the highest reported HPB practiced (27.3 T 3.93), whereas physical activity was the least practiced HPB (17.9 T 4.95; please refer to Table 2 for details).
Correlation Between Health-Promoting Behaviors and Components of the Health Promotion Model As shown in Table 3, correlational analysis was conducted among the HPLP components and the components of TABLE 1.
General Demographic Variables (N = 169) n Gender Male Female
Ethical Considerations
Data Analysis Data analysis was conducted using IBM SPSS Version 19. Descriptive statistics, correlation, and multiple linear regression analysis were used to analyze the collected data. A p value of .05 or less was considered statistically significant.
Results Sample Characteristics The total number of subjects in this study was 169. The subjects were between the ages of 21 and 55 years; most were male, married, and working in Korean companies as manual laborers. Almost half of the subjects perceived their health as ‘‘fair,’’ and around 37% perceived their health as ‘‘good.’’
150 19
88.8 11.2
Marital statusa Married Unmarried Separated
80 85 3
47.3 50.3 1.8
Religiona Buddhist Hindu Christian No religion Others
6 86 66 1 5
3.6 50.9 39.1 0.6 3.0
1 13 26 125
0.6 7.7 15.4 74.0
Work areaa Manual labor Domestic work Construction Office Othersb
60 7 38 7 50
35.5 4.1 22.5 4.1 29.6
Perceived health Very bad Bad Fair Good Very good
1 17 74 63 14
0.6 10.1 43.8 37.3 8.3
Age (M, SD)
29.51
Daily working hours (M, SD)
11.46
Educationa Primary Secondary Higher secondary College and above
Permission was obtained from the institution review board of the concerned university. Informed individual consent was obtained from all participants. Participation was fully voluntary and anonymous.
%
a
6.28 1.18 b
All data may not total up to 100% because of missing data. Agriculture; animal husbandry.
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Pratibha Bhandari et al.
TABLE 2.
Health Promoting Lifestyle Profile (HPLP), Perceived Health, and Perceived Self-Efficacy (N = 169) Possible Range
Observed Range
Mean
SD
52Y208 9Y36 8Y32 9Y36 9Y36 9Y36 8Y32
91Y195 9Y36 8Y32 13Y36 17Y36 18Y36 12Y32
137.70 22.05 17.91 23.47 27.30 25.90 21.41
20.19 4.61 4.95 4.22 3.93 3.84 3.85
Total self-efficacy
8Y40
15Y40
26.98
4.89
Perceived health
1Y5
1Y5
3.42
0.80
Total HPLP Health responsibility Physical activity Nutrition Spiritual growth Interpersonal relationships Stress management
TABLE 3.
Correlations Among Individual Characteristics, Behavior-Specific Cognition, and HPLP (N = 169) Age
Gender
Educational Status
Marital Status
Perceived Health
Perceived Self-Efficacy
Age Gender
.130
Educational status
j.243**
j.108
Marital status
j.501**
j.081
j.033
.034
.068
.194*
j.145
j.109
j.006
.099
j.056
Total HPLP
.016
j.007
j.102
.010
j.065
Health responsibility
.016
.042
j.091
.010
j.149
Physical activity
.055
.010
j.149
j.029
.002
.214**
j.001
.139
j.081
j.073
.040
.197*
.047
.061
j.012
j.012
.056
.154*
Perceived health Perceived self-efficacy
Nutrition Spiritual growth
.388** .180* .090
Interpersonal relationships
j.052
j.032
.073
.073
j.039
.129
Stress management
j.025
j.038
j.015
j.015
j.012
.163*
Note. HPLP = Health Promoting Lifestyle Profile. *p G .05. **p G .001.
HPM, including individual characteristics (age, gender, marital status, educational level, and perceived health) and behavior-specific cognition (perceived self-efficacy), using the Pearson’s correlation test. The results showed that selfefficacy was weakly but significantly and positively correlated with physical activity, nutrition, spiritual growth, and stress management. Self-efficacy was also moderately, positively, and significantly correlated with perceived health status. No significant correlation was detected between individual characteristics and HPLP.
observed among the variables. As shown in Table 4, individual characteristics (age, gender, educational status, and perceived health) were entered in the first block, and perceived self-efficacy was entered in the second block. The first model (block) explained only about 1.4% (p 9 .05) of the total variance in HPLP. The addition of perceived selfefficacy in the second model increased the explained variance to 4.1% (p G .05). Perceived self-efficacy was the only significant predictor of HPLP (" = .22, p G .05) in our study.
Predictors of Health-Promoting Behaviors
Using the HPM as a theoretical framework, the aim of our study was to describe the HPB and identify the predictors of HPB in Nepalese adults working in South Korea. Most of our study samples were manual workers who were working
Discussion Multiple regression analysis was conducted to identify the predictors of HPB. The assumptions for normality, linearity, and homoscedasticity were met, and no multicollinearity was 4 Copyright © 2015 Taiwan Nurses Association. Unauthorized reproduction of this article is prohibited.
Predictors of Health-Promoting Behaviors
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TABLE 4.
Predictors of Health-Promoting Behaviors (N = 169) Model 1 B
Variable Age
SE B
Model 2 "
B
SE B
0.12
0.28
.035
0.176
Gender
j0.87
5.24
j.013
j0.187
5.15
j.003
Educational level
j2.69
2.63
j.085
j2.74
2.58
j.087
Perceived health
j1.20
2.03
j.048
j3.37
2.16
j.136
Perceived self-efficacy R2 F for change in R2 (df )
0.912 .014 0.53 (4, 155)
0.279
"
0.352 .055 6.71 (1, 154)*
.051
.221*
*p G .05.
in various jobs in the construction and agricultural sectors and who perceived their health as fair. Higher education correlated with better perceived health status, which may be because of the greater access that educated people typically have to health information. However, the meaning of ‘‘perceived health’’ may differ by culture. Hence, further in-depth exploration may be beneficial to obtain culture-specific findings. The total HPLP score reported by our study participants was lower than the score reported by immigrant women in the United Sates (Cha, 2010) and higher than those of workers in the food industry in Turkey (Bes¸er et al., 2007) and of skilled and unskilled workers in Taiwan (Huang et al., 2010). The most frequently practiced health behavior was spiritual growth, followed by interpersonal relationships, nutrition, health responsibility, and stress management. As shown in demographic information (Table 1), most of the participants expressed holding religious beliefs such as Buddhism, Hinduism, or Christianity. Nepalese are generally considered to be religious people who derive inner peace and strength through their connection with their god(s) and deities (Bhandari, 2014). However, no specific evidence currently associates spirituality and health behavior or practices. Further studies may be directed to explore this association. Physical activity was the least practiced health behavior in our study, although most participants were menVa finding that contrasts with previous studies (Bhandari, 2014; Vaidya & Krettek, 2014). This may be because of the heavy workload associated with working in a foreign land. In addition, this feature is characteristic of most Nepalese adults who, despite being aware of the ill effects of physical inactivity, often fail to put this knowledge into practice (Simkhada, Poobalan, Simkhada, Amalraj, & Aucott, 2011). The causes for this may be multifactorial and include factors such as ignorance about preventive and promotive healthcare, limited health literacy, inadequate infrastructures, and the obesogenic environment (Vaidya, Shakya, & Krettek, 2010). Therefore, nurses should also consider the working conditions and economic backgrounds of migrant workers when planning interventions and policies. In contrast to previous studies, it is interesting to note that, despite higher educational status, participants in this
study still exhibited a limited practice of health behaviors. Knowledge regarding health behavior is an important predictor for engaging in health behavior (Chamroonsawasdi et al., 2010). Therefore, it is important that nurses provide adequate health education about HPB. One reason for the low level of HPB in our study may be lack of adequate time (Bhattarai, 2005). Most participants worked an average of 11.4 hours on weekdays. Another reason may be prior lifestyle habits that participants continued to follow in Korea (Gadd, Sundquist, Johansson, & Wa¨ndell, 2005). In view of long working hours (11.4 hours), it is recommended that the working environment be provided with equipment and infrastructures for physical exercise for the use of workers during rest hours. Access to health resources in this way may also provide critical cues necessary for adopting HPBs. Another important nursing implication that may be derived is that nurses must proactively reach out to community-dwelling adults who may or may not have any diagnosed illnesses. The innovative use of media, public campaigns, and other public awareness programs may be used for this purpose (Long et al., 2013). The involvement of the media in promoting health behaviors is currently largely limited in developing countries to the prevention of communicable diseases. Most of the study participants perceived their health as being fair or good. Therefore, perceived health did not predict health behaviors. This finding is in contrast to a previously reported study (Bes¸er et al., 2007). It was interesting to note that, although most participants perceived their health as fair to good, their engagement in health-promoting self-care behavior was comparatively low. Health illiteracy is common among Nepalese adults, and preventive care is often neglected (Vaidya et al., 2010). Obesity or undernutrition among adults is not looked upon as ‘‘unhealthy,’’ and only chronic or life-threatening illnesses are counted as ‘‘being sick.’’ Griffith, Lovett, Pyle, and Miller (2011) reported on the incongruences among perceived health, health behaviors, and objective health. This underscores the need to target specific health behaviors such as physical activity, nutrition, and individual health responsibilities and to not cluster all under the catch-all phrase of ‘‘health behaviors.’’ 5
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This study was based on Pender’s revised HPM. The HPM describes individual characteristics as important components that affect and predict subsequent health-related behaviors. Similarly, behavior-specific cognition is another important predictor of HPB. In our study, the selected variables for individual characteristics such as age, educational level, and perceived health did not predict HPB. However, it should be pointed out that not all of the components of the model were included. Future model-testing studies that use all the variables from the model may better explain HPBs. In line with the HPM and previous studies (Badr & Moody, 2005; Gutie´rrez-Don˜a, Lippke, Renner, Kwon, & Schwarzer, 2009; King et al., 2010), self-efficacy, which was selected as a behavior-specific cognition variable in our study, was found to significantly predict HPBs. The HPM regards behaviorspecific cognition variables as the ‘‘core’’ toward which nursing interventions must be directed. Healthcare professionals must use creative ways to promote self-efficacy among adults, even when language is a barrier to communication. Technology may be used to facilitate effective interventions (King et al., 2013; Wolfenden et al., 2012). With the recent advancements in technology and the availability of high-speed Internet in most countries, it is also possible to design educational nursing interventions through the use of videos to promote self-efficacy as an adjunct to other methods. Although the essence of one-to-one communication cannot be substituted completely with videos or other similar aids, it nevertheless calls for creativity on the nurses’ part in dealing with multicultural clients. The total variance explained by our regression model was significant but quite low (5.5%). This suggests that there may be other unidentified variables that explain the HPBs in this group of adults. As mentioned above, the variables for this research were parsimoniously selected based on the available evidence (Bes¸er et al., 2007; Chamroonsawasdi et al., 2010; Huang et al., 2010). Thus, not all of the variables of the HPM were included in our study. Inclusion of other variables such as social support, health literacy, perceived benefits, and perceived barriers may provide better insights about the relationships between the factors that influence health promotion behaviors. Hence, future studies should include these other variables. In addition, in-depth analysis through qualitative inquiries may provide insights about the cultural meaning that specific groups of adults associate with health promotion, which may facilitate the planning of further research and subsequent development of nursing interventions. Finally, this research highlights the need for occupational health nurses to be able to play a more significant role in promoting preventive healthcare behaviors to workers. Nurses must understand various work environment factors and the cultural and economic background of migrant workers that may influence their HPBs. Nurse leaders and policy makers are urged to actively advocate for the needs of occupational and multicultural nursing structures and facilities, especially in countries where the immigrant population is on the
Pratibha Bhandari et al.
rise, as a way to reduce disparities in health and access to healthcare.
Limitations This study was subject to several limitations. First, the internal consistency of one of the subitems (stress management) of the HPLP was low (.65), which may have lowered the reliability of the measure. Second, this was a crosssectional study. Therefore, no causation may be implied. Third, the study sample was mostly limited to unskilled workers, so the results should be generalized carefully. However, most of the participants held college degrees. Moreover, because the study samples were predominantly men, generalization of the study findings may not be applicable to women. Finally, the use of self-report surveys may have introduced bias.
Conclusions Adopting HPBs is associated with disease prevention and a better quality of life. A number of individual and behavioral factors influence the choice and adoption of HPBs. Using HPM, this study examined the various individual and behaviorspecific variables that predicted HPBs in a group of Nepalese migrant workers who were employed in South Korea. The results suggest that self-efficacy significantly predicts HPBs. Therefore, future health promotion programs should include strategies that are designed to maximize the self-efficacy of these workers. However, provision of care for multicultural immigrant populations is a challenge in terms of human and material resources. Hence, nurses are encouraged to use innovative ways to facilitate this process. In addition, nurse leaders and policy makers should actively advocate for the expansion of promotional and preventive healthcare services to all groups of the population. It is recommended that future studies be conducted with other variables from the HPM to assess the role of HPM variables in influencing health behaviors. Furthermore, studies to identify the influence of culture on health-promoting behaviors will be useful in planning tailored and culturally sensitive nursing interventions and policies.
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