Health Education Research, Vol. 17, No. 3, 305-314,
June 2002
© 2002 Oxford University Press
Psychosocial correlates of sun-protective practices of preschool staff toward their students
UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7295, 1 Department of Behavioral Science, University of Texas M. D. Anderson Cancer Center, Houston, TX 77030-4009, and 2 Center for Health Promotion and Prevention Research and 3 Department of Epidemiology, University of Texas-Houston Health Science Center, School of Public Health, Houston, TX 77030, USA
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Abstract |
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The skin cancer rate in the US has been increasing faster than that for other cancers. Most skin cancers are related to sun exposure and the majority of exposure occurs before adulthood. Thus, children are an important target group to study and preschools can be useful avenues for delivering sun-protection messages. The current study examines the behaviors of preschool staff in protecting students from sun exposure and investigates factors related to sun-protective practice. Preschool staff (n = 245) were surveyed about their sun-protective practices toward students as the cross-sectional baseline measurement for a larger project. The primary aim of this study was to investigate correlates of staff's sun-protective behavior toward students. A theoretical model of psychosocial constructs that combined components of the Theory of Planned Behavior and Social Cognitive Theory was evaluated using structural equation modeling. Self-efficacy and perceived norms were the strongest correlates of behavior. A hypothesized link between expectancy and behavior was not supported. The roles of self-efficacy and perceived norms in the preschool context are discussed as they relate to staff's behavior.
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Introduction |
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Skin cancer, including melanoma and non-melanoma cancers, is the most commonly diagnosed cancer in the US. The incidence rate of melanoma has increased dramatically over the past 25 years and is increasing faster than that of most other cancers (Wingo et al., 1999
). Approximately 47 700 incident cases of melanoma and more than 1 million cases of non-melanoma skin cancer were expected in 2000 (Greenlee et al., 2000). While all groups are at risk, the skin cancer rate is higher among Caucasians than among African-Americans and Hispanics. Family or personal history of skin cancer is a risk factor (Ford et al., 1995), in addition to phenotypic characteristics such as light colored eyes (i.e. blue), blonde or red hair color, light or fair skin type (i.e. one that tends to burn) and a tendency to freckle (Carli et al., 1999). Excessive sun exposure is the primary environmental and behavioral risk factor for skin cancer (Autier et al., 1997).
The majority of sun exposure, some experts estimate up to 80%, occurs before adulthood (Preston and Stern, 1992; Vail-Smith et al., 1997) and severe sunburn during childhood may increase the risk of melanoma later in life (Zanetti et al., 1992). Sunburn rates are relatively high during childhood; studies have found that up to 50% of children were sunburned during the study observation period (Dixon et al., 1999; Robinson et al., 2000). Due to the amount of sun exposure accumulated during the younger years, it is reasonable and advisable that sun protection begin at early ages. Young children have a potential to benefit greatly from early initiation of sun protection and early initiation may facilitate maintenance of these behaviors over the lifetime.
Reduction of sun exposure through behavioral intervention has been proposed as the most effective way to reduce skin cancer rates [e.g. (Rigel and Carucci, 2000)]. The following measures are recommended to reduce skin cancer risk: application of broad spectrum sunscreens with sun-protection factors (SPF) of 15+; reapplication of sunscreen after 1.5–2 h of exposure or after swimming or perspiring; avoidance of sunlight during midday hours; use of protective clothing such as wide-brimmed hats, long-sleeved shirts and long shorts or pants; and the use of shade (American Cancer Society, 2000). It is clear that among young children, parents and caregivers must play an active role, either direct or indirect, in providing and facilitating sun protection.
Several studies have shown that parents are aware of the benefits of sun protection, yet do not routinely engage in sun-protective behavior for their children (Vail-Smith et al., 1997). They are slightly more likely to use it while at the beach or on vacation than while at home (Bennetts et al., 1991; Bourke and Graham-Browne, 1995). The factors predictive of sun-protective behaviors are unclear; however, several psychosocial variables have been identified as potential determinants. Consistent with social psychological theories, such as the Theory of Planned Behavior (Ajzen, 1991) and Social Cognitive Theory (Bandura, 1986), psychosocial constructs such as attitude/expectancy, perceived norms, perceived behavioral control/self-efficacy and impediments to behavior are often found to be related to sun-protective actions (Arthey and Clarke, 1995; Glanz et al., 1999).
Many young children spend some portion of their weekdays in childcare outside the home and children in preschools usually have scheduled playtimes outside during midday hours. Targeting childcare staff could prove to be an important mechanism for implementing and maintaining practices aimed at reducing sun exposure among young children who are in daycare. Despite the important role of children's caregivers, few studies have examined childcare staff's sun-protective actions toward students. The current study's goal is to understand the practices that preschool staff engage in with respect to protecting their students from excessive sun exposure. First, the frequency and nature of sunscreen-use and sun-avoidance practices by preschool staff toward students are examined. Second, linear structural equation modeling (SEM) is used to identify correlates of the targeted behaviors by evaluating a theoretical model that combines components of the Theory of Planned Behavior and Social Cognitive Theory.
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Method |
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Setting and procedure
Data for this study were collected during a cross-sectional baseline (pre-intervention) assessment for the evaluation of the Sun Protection is Fun! program. This preschool skin cancer prevention and sun safety education program for staff, parents and children is described in detail elsewhere (Tripp et al., 2000
). Preschools from Houston and the surrounding area were identified from a list provided by a local agency that maintains records on childcare facilities. After meeting with schools that expressed interest, the research staff recruited 20 preschools to participate in the study. Preschools were diverse with respect to the enrollment size and the racial/ethnic distribution of children and staff. Children in the preschools ranged in age from newborn to 5 years. The Institutional Review Board at the University of Texas M. D. Anderson Cancer Center approved all parts of the study.
Data collection
Preschool staff (teachers, teacher assistants and administrative staff) and parents were surveyed as part of the larger study; however, only the staff data are analyzed and reported here. Staff who had responsibility for sun protection at the preschool were given the surveys at the end of the summer in 1996. Surveys were sealed in individually labeled packets containing the survey, a postage-paid return envelope, and a cover letter explaining the confidential and voluntary nature of the survey. Staff members were instructed to return the surveys to collection boxes at school or by mail to the research team in the postage-paid pre-addressed return envelope. Reminder cards and additional surveys were delivered to the schools to increase the response rate. Once surveys were returned, they were coded, double-entered and checked by the data manager to ensure that there were no duplicates or errors.
Measures
The survey instrument was a paper-and-pencil self-report questionnaire intended to evaluate demographic characteristics, sun-protective practices, and relevant psychosocial constructs. Five-point Likert-type response scales were used for all behavioral and psychosocial questions. Sunscreen-use questions addressed staff practices and beliefs related to using a sunscreen with a SPF 15+ and reapplication of sunscreen when necessary. Sun-avoidance questions addressed staff practices and beliefs regarding dressing children in protective clothing and providing shaded areas for outside play.
The psychosocial constructs were drawn from the Theory of Planned Behavior and Social Cognitive Theory. The Theory of Planned Behavior (Ajzen, 1991) is a predictive model of behavior for instances in which behavior may not be under complete self-control (i.e. the behavior may involve skills or access to resources). Planned behavior hypothesizes that intention, and subsequently behavior, are determined by attitude, subjective norms and perceived behavioral control. Perceived behavioral control (similar to most conceptualizations of self-efficacy) also is hypothesized to directly influence behavior in many uses of the model. Both planned behavior and its predecessor, Theory of Reasoned Action (Ajzen and Fishbein, 1980), previously have been applied to tanning and sunbathing (Hillhouse et al., 1997). Social Cognitive Theory (Bandura, 1986) shares many of the planned behavior constructs but places a higher emphasis on self-efficacy and does not typically include intention. In Social Cognitive Theory, self-efficacy predicts not only behavior, but also influences other model determinants. Impediments to behavior, outcome expectations and perceived norms are also part of the theory. The psychosocial factors used in the current study include expectancies, perceived norms, self-efficacy and impediments. Table I more fully describes the items and scales. Information on scale validation is presented in the Results section.
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Analyses
Descriptive statistics in SPSS 10.0 were used to determine the frequency of staff efforts to reduce sun exposure among their students and to characterize the population on variables of interest. Levels of missing data were within acceptable limits, so listwise deletion was used. For two individual behavior items, however, the levels of missing data were higher than other items. Further examination revealed that one of these items dealt with bringing sunscreen on field trips (11% missing) and the other asked about bringing canopies or umbrellas outside or on field trips (13.9% missing). While these missing data rates are higher than ideal, they seem reasonable. An analysis of missing data did not reveal significant differences between respondents and non-respondents on sociodemographic variables (i.e. age, sex, ethnicity and staff position) or the model's psychosocial constructs.
Confirmatory factor analyses were conducted to examine the validity of scales and the appropriateness of their use in a structural equation model. SEM was used to test the proposed theoretical models (composed of the behavioral and psychosocial scales) and evaluate the strength of various psychosocial constructs in the models. Theoretical models tailored specifically to sunscreen use and sun avoidance behaviors were derived from the combination of the Theory of Planned Behavior and Social Cognitive Theory (Figure 1). It was hypothesized that perceived norms, attitudes/expectancies and impediments would have direct effects on behavior. It was further hypothesized that self-efficacy would have a direct effect on behavior, and indirect effects on behavior through norms, attitudes and impediments. The models were entered into PRELIS 2.14/LISREL 8.14 software (Joreskog and Sorbom, 1996) and the covariance matrices were analyzed using maximum likelihood estimation. After listwise deletion, the effective sample sizes for the models were 180 (sunscreen) and 194 (sun avoidance).
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Multiple indices were considered in assessing the fit of the data to the hypothesized model. The
2 statistic was interpreted with caution; more attention was directed to the ratio of 2 to the degrees of freedom. While the utility of this ratio (hereafter referred to as the relative 2) has been questioned [e.g. (Steiger, 2000)], its use has been recommended by several researchers [e.g. (Joreskog and Sorbom, 1996; Mueller, 1996)]. Lower ratio values suggest better fit and values lower than 2 or 3 are typically agreed upon as indicative of `good' fit (Kline, 1998). In addition to the relative 2, the other indices used to assess fit in this study included the Goodness of Fit Index (GFI), Non-normed Fit Index (NNFI), Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Squared Residual (SRMR). For NNFI, GFI and CFI, values above 0.90 tend to suggest a good fit. RMSEA values less than 0.08 indicate an adequate fit; a RMSEA less than 0.05 indicates a very good fit (Brown and Cudeck, 1993). SRMR values around or below 0.05 are considered suggestive of good fit. The significance of the relationships between the independent (exogenous) latent variables, mediating latent variables and dependent (endogenous) latent variables was assessed by t-tests calculated by the LISREL software. |
Results |
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Sample demographics
The response rate was 78%. Staff respondents (n = 245) ranged in age from 20 to 68 years, the average age was 34 years (SD = 10.7). The majority of the responding staff was female (92%) and most respondents were teachers (48%) or teacher assistants (22%); the remainder were administrative staff. Ethnically, the respondents were diverse; 38% of the staff were African-American, 34% were Caucasian and 19% were Hispanic. A quarter of the responding staff had a high-school level education, nearly half of the sample had some college but no degree, 11% had an associate's degree and 8% had a bachelor's degree. About half of the respondents reported that they had children living in their home. Preschools provided the research team with a roster of names of staff and students. These lists contained no demographic information and therefore, even within the boundaries of confidentiality (as opposed to anonymity), no comparison could be made between respondents to the survey and non-respondents.
Sunscreen-use and sun-avoidance behavior
A minority of staff engaged in sun-protective practices to reduce their students' sun exposure. Specifically, 33% of staff reported `always' or `frequently' using sunscreen with the recommended SPF 15+ and 18% applied sunscreen to their students 30 min before going outside. Approximately one-fourth of staff reported that their students wore shirts with sleeves (26%) or long shorts (23%) when playing outside; even fewer (14%) reported that students wore hats or caps. About one in 10 staff (12%) took canopies or umbrellas to create shaded areas while outside or on field trips.
To discover whether sun-protective behavior was accounted for by sociodemographic factors, analysis of variance (ANOVA) tests were conducted. The sunscreen behavior scale did not vary significantly by level of education (F[6,196] = 1.72, P > 0.05), number of years employed at the school (F[6,158] = 0.42, P > 0.05), or the staff member's position at the school (F[3,200] = 0.86, P > 0.05). There were small but significant differences in sunscreen behavior by race/ethnicity (F[4,196] = 3.4, P < 0.05). Ethnicity did not demonstrate significant variance in sun avoidance behavior (F[4,205] = 2.01, P > 0.05). Sun avoidance also did not vary significantly by education (F[6,205] = 0.44, P > 0.05), years at the school (F[6,164] = 0.45, P > 0.05) or position (F[3,209] = 0.25, P > 0.05). While sunscreen use appears to be associated somewhat with ethnicity, it appears that the staff's behavior toward their students was not necessarily a product of these demographic variables. Therefore, demographics were not entered into the LISREL models.
Model of sunscreen use and strength of correlates
Confirmatory factor analyses demonstrated good levels of fit for each scale in the sunscreen use model. The relative 2 for every scale was below 2.0. Also, the CFI, GFI and NNFI values were above 0.95, higher than the recommended value for good fit. We thus concluded that the scales were unidimensional and acceptable for use with this staff sample. Cronbach's 
coefficients suggested that the scales were reliable; coefficients were 0.75 and above. We were encouraged by the validation of analogous scales in the parent sample (staff and parent survey items were similar in theoretical basis, content and wording) (Tripp et al., 2002). The theoretical model, described previously (Figure 1
), was applied to sunscreen and evaluated in LISREL. All indicators, with the exception of 2, demonstrated adequate to good fit of the hypothesized combined social cognitive-planned behavior model (Table II).
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Figure 2
displays the sunscreen use model and the standardized (maximum likelihood) path estimates. Significance of paths was determined by a t-score calculated for each path; t-values above 1.96 were considered significant. Self-efficacy had the strongest association with behavior (t = 5.55), followed by perceived norms of sunscreen use (t = 3.60) and impediments (t = –2.34). The relationship between expectancy and behavior was non-significant (t = 0.15). Higher levels of confidence in providing and applying sunscreen to students, perception of positive norms for sunscreen use, and lower scores on the perceived impediments scale were associated with a higher likelihood of sunscreen practices. There were also significant relationships between self-efficacy and perceived norms, and between self-efficacy and impediments, suggesting that the relationship between self-efficacy and behavior may be partially mediated by perceived norms and impediments.
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Model of sun-avoidance practices and strength of correlates
The sun avoidance scales also were considered to be unidimensional and sufficiently valid for use in identifying correlates. A confirmatory factor analysis demonstrated excellent fit of all the scales. The relative
2 for every sun avoidance scale was below 2.0. Also, the CFI, GFI and NNFI values were above 0.95. Further support of scale reliability was supplied by Cronbach's coefficients, most of these were 0.65 and above. The hypothesized sun avoidance theoretical model produced slightly higher levels of fit than that for sunscreen use (Table II). 2 was significant but the relative 2 and the rest of the indices demonstrated excellent fit.
Standardized path estimates for the sun-avoidance model are shown in Figure 3. The paths from self-efficacy to norms and from self-efficacy to behavior were both significant, suggesting that self-efficacy was associated with both perceived norms and behavior. As was found for sunscreen use, self-efficacy had the strongest association with behavior, and expectancies failed to exhibit a significant path to behavior (t = 0.33). In contrast to the sunscreen model, perceived norms did not have a significant association with sun-avoidance behaviors (t = 1.25). Thus, self-efficacy had the only significant path to behavior.
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Discussion |
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Both sunscreen and sun-avoidance practices are recommended for effective protection from sun-exposure and prevention of skin cancer. The aim of this study was to assess correlates of sun-protective practices by preschool staff to reduce sun exposure among their students. The hypothesized models, consisting of self-efficacy, expectancies, perceived norms, impediments (sunscreen only) and behavior, demonstrated good levels of fit when analyzed using SEM, enabling us to test both the theory and the path relationships to identify correlates of behavior. Although the cross-sectional nature of the data set prohibits interpolating the results to predict future behavior, the results do suggest a strong link between the psychosocial constructs identified here and sun protective behavior, thereby reinforcing the utility of Social Cognitive Theory and the Theory of Planned Behavior for use in skin cancer prevention research.
As hypothesized, self-efficacy emerged as a valuable construct in sunscreen use and sun avoidance, demonstrating positive and significant statistical effects on behavior. Sunscreen self-efficacy reflected the staff member's confidence that she or he could obtain sunscreen and apply it to students as recommended. Sun-avoidance self-efficacy reflected the staff member's confidence that he or she could dress students in protective clothing (and keep them dressed), adjust play times to avoid peak hours of the day and bring canopies or umbrellas to set up shade on field trips. Self-efficacy also had significant effects on perceived norms and, for sunscreen, perceived impediments. In the sunscreen model, the association between self-efficacy and behavior is mediated by perceived norms and perceived impediments. Levels of self-efficacy among staff members were moderate and were normally distributed, suggesting that self-efficacy is a construct that not only is important to the targeted behavior, but also has the potential to be increased through intervention strategies.
In addition to self-efficacy, perceived norms emerged as an important correlate of sunscreen use (but not sun avoidance). Perceived norms were specific to sunscreen use but included the teacher's perceptions of teacher and parent norms regarding sunscreen use. Modifying perceived norms is a difficult but realistic task for intervention programs. In the preschool setting where there tends to be a high turnover among staff and students (Crane et al., 1999), norms may be very valuable in predicting or maintaining behaviors and guiding education or intervention efforts. Impediments also emerged as an important correlate of sunscreen behavior. The perception that applying sunscreen to their students was messy and difficult was related to lower reported sunscreen behavior by staff. The association between self-efficacy and sunscreen behavior may be mediated by perceived impediments.
The lack of a significant association between expectancy and either behavior was surprising. This result may be due to the strength of self-efficacy and norms as correlates of behavior, precluding the associative value of expectancies in these models. Alternatively, this finding could be the result of measurement issues as scores on the sunscreen expectancy and tanning expectancy scales tended to be high across respondents. It seems unlikely that this result is the consequence of school policy, since relatively few of the preschools had written policies related to sun exposure and protection at the time of this baseline measurement. However, the situational pressures unique to this preschool environment may have influenced these findings. Even if teachers held a favorable expectancy or attitude toward sun protection, when they did not have high levels of self-efficacy regarding sun-protective practices in the classroom, they were unlikely to use sun-protection practices with their students. The results found here differ slightly from those of other investigators who have found stronger evidence for the importance of expectancy and attitudes in the performance of actions related to sun exposure [e.g. (Martin et al., 1999)]. Future studies might further explore these constructs and their roles in various settings.
There are certain limitations to the findings reported in this study. First, these data are cross-sectional and interpretation must remain within the context of association, not prediction. Future work should also utilize longitudinal designs to determine whether the relationships found here are predictive or simply associative. Second, sample size was of concern in these analyses because the original sample was moderately small (n = 245). Nonetheless, sample sizes of 200 are generally considered acceptable for generating accurate estimates and fit indices in most models (Tabachnick and Fidell, 1996).
Reducing sun exposure during childhood is an essential step toward curbing future skin cancer rates. Young children are typically reliant on adults around them to facilitate sun-protective practices (e.g. provide sunscreen, take canopies when going outside), and daycare centers and preschools may be a promising setting to implement prevention programs for young children and their caregivers. The current study demonstrates the roles of self-efficacy, perceived norms and impediments as correlates of sun protection, with expectancy being less informative. One implication of this set of findings is that while each of these psychosocial correlates may work together in a theoretical model to predict behavior, the individual components vary in the strength of relationships depending on the specific behavior under investigation. There are few empirical studies examining the determinants of preschool staff's sun-protective behavior toward students, and the results of this study are important for identifying valid and useful correlates of such behavior.
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Acknowledgments |
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Data for this study was collected as part of research supported by a grant from the National Cancer Institute (NCI) (R01-CA 62918, E. R. Gritz, PhD, Principal Investigator). This paper reports on the first author's MPH thesis project at the University of Texas-Houston (UT-H) School of Public Health. During the majority of writing, A. S. J. was supported by a training fellowship at the UT-H School of Public Health (NCI 5R25CA57712) and is currently supported by the UNC Lineberger Cancer Control Education Program (NCI CA57726).
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References |
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TopAbstractIntroductionMethodResultsDiscussionReferences |
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Ajzen, I. (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.[Web of Science]
Ajzen, I. and Fishbein, M. (1980) Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, NJ.
American Cancer Society (2000) Cancer Facts & Figures, 2000. American Cancer Society, Atlanta, GA.
Arthey, S. and Clarke, V. A. (1995) Suntanning and sun protection: a review of the psychological literature. Social Science and Medicine, 40, 265–274.
Autier, P., Dore, J. F., Gefeller, O. P., Cesarii, J. P., Lejeaune, F., Koelmel, K. F., Lienard, D. and Kleeberg, U. R. (1997) Melanoma risk and residence in sunny areas. EORTC Melanmoa Co-operative Group. European Organization for Research and Treatment of Cancer. British Journal of Cancer, 76, 1521–1524.[Web of Science][Medline]
Bandura, A. (1986) Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice-Hall, Englewood Cliffs, NJ.
Bennetts, K., Borland, R. and Swerissen, H. (1991) Sun protection behaviour of children and their parents at the beach. Psychology and Health, 5, 279–287.
Bourke, J. F. and Graham-Browne, R. A. C. (1995) Protection of children against sunburn: a survey of parental practice in Leicester. British Journal of Dermatology, 133, 264–266.[Web of Science][Medline]
Browne, M. W. and Cudeck, R. (1993) Alternative ways of assessing model fit. In Bollen, K. A. and Long, J. S. (eds), Testing Structural Equation Models. Sage, Newbury Park, CA, p. 136.
Carli, P., Massi, D., Santucci, M., Biggeri, A. and Giannotti, B. (1999) Cutaneous melanoma histologically associated with a nevus and melanoma de novo have a different profile of risk: results from a case-control study. Journal of the American Academy of Dermatology, 40, 549–557.[Web of Science][Medline]
Crane, L. A., Schneider, L. S., Yohn, J. J., Morelli, J. G. and Plomer, K. D. (1999) `Block the sun, not the fun': evaluation of a skin cancer prevention program for childcare centers. American Journal of Preventive Medicine, 17, 31–37.[Web of Science][Medline]
Dixon, H., Borland, R. and Hill, D. (1999) Sun protection and sunburn in primary school children: the influence of age, gender, and coloring. Preventive Medicine, 28, 119–130.[Web of Science][Medline]
Ford, D., Bliss, J. M., Swerdlow, A. J., Armstrong, B., Franceschi, S., Green, A., Holly, E. A., Mack, T., MacKie, R. M., Osterlind, A., Walter, S. D., Peto, J. and Easton, D. S. for the International Melanoma Analysis Group (IMAGE). Risk of cutaneous melanoma associated with a family history of the disease. International Journal of Cancer, 62, 377–381.
Glanz, K., Carbone, E. and Song, C. (1999) Formative research for developing targeted skin cancer prevention programs for children in multiethnic Hawaii. Health Education Research, 14, 155–166.
Greenlee, R. T., Murray, T., Bolden, S. and Wingo, P. A. (2000) Cancer statistics. CA: Cancer Journal for Clinicians, 50, 7–33.[Abstract]
Hillhouse, J. J., Adler, C. M., Drinnon, J. and Turrisi, R. (1997) Application of Ajzen's theory of planned behavior to predict sunbathing, tanning salon use, and sunscreen use intentions and behaviors. Journal of Behavioral Medicine, 20, 365–378.[Web of Science][Medline]
Joreskog, K. G. and Sorbom, D. (1996) LISREL 8: Reference Guide. Scientific Software International, Chicago, IL.
Kline, R. (1998) Principles and Practice of Structural Equation Modeling. Guilford Press, New York.
Martin, S. C., Jacobsen, P. B., Lucas, D. J., Branch, K. A. and Ferron, J. M. (1999) Predicting children's sunscreen use: application of the theories of reasoned action and planned behavior. Preventive Medicine, 29, 37–44.[Web of Science][Medline]
Mueller, R. O. (1996) Basic Principles of Structural Equation Modeling: An Introduction to LISREL and EQS. Springer-Verlag, New York.
Preston, D. S. and Stern, R. S. (1992) Nonmelanoma cancers of the skin. New England Journal of Medicine, 327, 1649–1662.[Web of Science][Medline]
Rigel, D. S. and Carucci, J. A. (2000) Malignant melanoma: prevention, early detection, and treatment in the 21st century. CA: Cancer Journal for Clinicians, 50, 215–236.[Abstract]
Robinson, J. K., Rigel, D. S. and Amonette, R. A. (2000) Summertime sun protection used by adults for their children. Journal of the American Academy of Dermatology, 42, 746–753.[Web of Science][Medline]
Steiger, J. H. (2000) Point estimation, hypothesis testing, and interval estimation using the RMSEA: some comments and a reply to Hayduk and Glaser. Structural Equation Modeling, 7, 149–162.
Tabachnick, B. and Fidell, L. (1996) Using Multivariate Statistics, 3rd edn. Harper Collins, New York.
Tripp, M. K., Herrmann, N. B., Parcel, G. S., Chamberlain, R. M. and Gritz, E. R. (2000) Sun Protection is Fun! A skin cancer prevention program for preschools. Journal of School Health, 70, 395–401.
Tripp, M. K., Carvajal, S. C., McCormick, L. K., Mueller, N. H., Hu, S. H., Parcel, G. S. and Gritz, E. R. (2002) Validity and reliability of the Parental Sun Protection Scales. Health Education Research, in press.
Vail-Smith, K., Watson, C. L., Felts, M., Parillo, A. V., Knight, S. M. and Hughes, J. L. (1997) Childhood sun exposure: parental knowledge, attitudes, and behaviors. Journal of Health Education, 28, 149–155.
Wingo, P. A., Ries, L. A. G., Giovino, G. A., Miller, D. S., Rosenberg, H. M., Shopland, D. R., Thun, M. J. and Edwards, B. K. (1999) Annual report to the Nation on the status of cancer, 1973–1996, with a special section on lung cancer and tobacco smoking. Journal of the National Cancer Institute, 91, 675–690.
Zanetti, R., Franceschi, S., Rosso, S., Colonna, S. and Bidoli, E. (1992) Cutaneous melanoma and sunburns in childhood in a Southern European population. European Journal of Cancer, 28A, 1172–1176.
Received on November 11, 2000; accepted on June 12, 2001
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