Using a Modified Theory of Planned Behavior to Examine Adolescents’ Workplace Safety and Health Knowledge, Perceptions, and Behavioral Intention: A Structural Equation Modeling Approach
- 255 Downloads
Work, a defining feature of adolescence in the United States, has many benefits. Work also has risks, as adolescents experience a higher rate of serious job-related injuries compared to adults. Talking Safety, a free curriculum from the National Institute for Occupational Safety and Health, is one tool educators may adopt to provide teens with essential workplace safety and health education. Adolescents (N = 2503; female, 50.1%; Hispanic, 50.0%) in a large urban school district received Talking Safety from their eighth-grade science teachers. This study used a modified theory of planned behavior (which included a knowledge construct), to examine students’ pre- and post-intervention scores on workplace safety and health knowledge, attitude, self-efficacy, and behavioral intention to enact job safety skills. The results from confirmatory factor analyses indicate three unique dimensions reflecting the theory, with a separate knowledge factor. Reliability estimates are ω ≥ .83. The findings from the structural equation models demonstrate that all paths, except pre- to posttest behavioral intention, are statistically significant. Self-efficacy is the largest contributor to the total effect of these associations. As hypothesized, knowledge has indirect effects on behavioral intention. Hispanic students scored lower at posttest on all but the behavioral intention measure, possibly suggesting the need for tailored materials to reach some teens. Overall the findings support the use of a modified theory of planned behavior to evaluate the effectiveness of a foundational workplace safety and health curriculum. This study may inform future efforts to ensure that safe and healthy work becomes integral to the adolescent experience.
KeywordsAdolescents Young workers Theory of planned behavior Occupational safety and health Injury prevention Structural equation modeling
We thank our partners in the Miami-Dade Public Schools (M-DCPS) for making this research possible: Mr. Cristian Carranza, Administrative Director, Division of Academics (STEAM); Dr. Ava D. Rosales, Executive Director, and Mr. Dane Jaber, Instructional Supervisor, Department of Mathematics and Science; the M-DCPS School Board. For their reviews of the manuscript, we thank Jeff Reese, PhD, and Fred Danner, PhD, University of Kentucky. For editorial comments, we thank John Lechliter and Jeanette Novakovich, NIOSH.
R.G. conceived of the study, collected the data, conducted the statistical analyses and drafted the manuscript. M.T. performed the statistical analyses and assisted with drafting the manuscript. A.O. assisted with the research design, coordination, data collection, and manuscript review. L.R.G. and A.B. participated in the interpretation of the data and manuscript review. All authors read and approved the final manuscript.
This work was funded with internal NIOSH research funds.
Data Sharing Declaration
This manuscript’s data will not be deposited.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
This research was conducted in accordance with the ethical standards of the NIOSH Institutional Review Board (IRB)/NIOSH Human Research Protection Program (HRPP) and with the 1975 Helsinki declaration as revised in 2000.
- Ajzen, I. (2002). Perceived behavioral control, self‐efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665–683. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x.CrossRefGoogle Scholar
- Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: Freeman.Google Scholar
- Boini, S., Colin, R., & Grzebyk, M. (2017). Effect of occupational safety and health education received during schooling on the incidence of workplace injuries in the first 2 years of occupational life: A prospective study. BMJ Open, 7(7). https://doi.org/10.1136/bmjopen-2016-015100.
- Bureau of Labor Statistics (2005). Work activity of high school students: data from the National Longitudinal Survey of Youth 1997. U.S. Department of Labor. http://www.bls.gov/news.release/archives/nlsyth_04272005.pdf.
- Bureau of Labor Statistics (2017). 2016 Survey of occupational injuries & illnesses charts package. U.S. Department of Labor. https://www.bls.gov/iif/osch0060.pdf.
- Bureau of Labor Statistics (2018). Labor force statistics from the current population survey. U.S. Department of Labor. https://www.bls.gov/cps/cpsaat11b.htm.
- Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Hillsdale, N.J: L. Erlbaum Associates.Google Scholar
- DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). Thousand Oaks, CA: SAGE Publications.Google Scholar
- Fisher, J. D., Fisher, W. A., Misovich, S. J., Kimble, D. L., & Malloy, T. E. (1996). Changing AIDS risk behavior: Effects of an intervention emphasizing AIDS risk reduction information, motivation, and behavioral skills in a college student population. Health Psychology, 15(2), 114–123. https://doi.org/10.1037/0278-622.214.171.124.CrossRefPubMedGoogle Scholar
- Gaskin, J. (2011). Common method bias [Video file]. https://www.youtube.com/watch?v=w7zZCBlRXog.
- Greenberger, E., & Steinberg, L. D. (1986). When teenagers work: The psychological and social costs of teenage employment. New York, NY: Basic Books.Google Scholar
- Guerin, R. J., Okun, A. H., & Kelley, P. (2016). Development and validation of an assessment tool for a national young worker curriculum: Assessment development for a young worker curriculum. American Journal of Industrial Medicine, 59(11), 969–978. https://doi.org/10.1002/ajim.22610.CrossRefPubMedPubMedCentralGoogle Scholar
- Hayduk, L. A. (1987). Structural equation modeling with LISREL: Essentials and advances. Baltimore, MD: Johns Hopkins University Press.Google Scholar
- Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.Google Scholar
- Hox, J. J., Borgers, N., & Dirk, S. (2004). Response effects in surveys on children and adolescents: The effect of number of response options, negative wording, and neutral mid-point. Quality and Quantity, 38(1), 17–33. https://doi.org/10.1023/B:QUQU.0000013236.29205.a6.CrossRefGoogle Scholar
- Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York, NY: The Guilford Press.Google Scholar
- Mardis, A. L., & Pratt, S. G. (2003). Nonfatal injuries to young workers in the retail trades and services industries in 1998. Journal of Occupational and Environmental Medicine, 45(3), 316–323. https://doi.org/10.1097/01.jom.0000052964.43131.8a.CrossRefPubMedGoogle Scholar
- Miami-Dade Public Schools (2018). http://www.dadeschools.net/.
- Montaño, D. E., & Kasprzyk, D. (2015). Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. In K. Glanz, B. K. Rimer & K. Viswanath (Eds.), Health behavior and health education: Theory, research, and practice (5th ed., pp. 95–124). Philadelphia, PA: John Wiley & Sons.Google Scholar
- Murnaghan, D. A., Blanchard, C. M., Rodgers, W. M., LaRosa, J. N., MacQuarrie, C. R., MacLellan, D. L., & Gray, B. J. (2010). Predictors of physical activity, healthy eating and being smoke-free in teens: A theory of planned behavior approach. Psychology and Health, 25(8), 925–941. https://doi.org/10.1080/08870440902866894.CrossRefPubMedGoogle Scholar
- Muthén, L.K., & Muthén, B.O. (1998-2017). Mplus User’s Guide (7th ed.).Google Scholar
- Pisaniello, D. L., Stewart, S. K., Jahan, N., Pisaniello, S. L., Winefield, H., & Braunack-Mayer, A. (2013). The role of high schools in introductory occupational safety education – teacher perspectives on effectiveness. Safety Science, 55, 53–61. https://doi.org/10.1016/j.ssci.2012.12.011.CrossRefGoogle Scholar
- Pössel, P., Baldus, C., Horn, A. B., Groen, G., & Hautzinger, M. (2005). Influence of general self‐efficacy on the effects of a school‐based universal primary prevention program of depressive symptoms in adolescents: A randomized and controlled follow‐up study. Journal of Child Psychology and Psychiatry, 46(9), 982–994. https://doi.org/10.1111/j.1469-7610.2004.00395.x.CrossRefPubMedGoogle Scholar
- Rauscher, K. J., Runyan, C. W., Schulman, M. D., & Bowling, J. M. (2008). U.S. child labor violations in the retail and service industries: Findings from a national survey of working adolescents. American Journal of Public Health, 98(9), 1693–1699. https://doi.org/10.2105/AJPH.2007.122853.CrossRefPubMedPubMedCentralGoogle Scholar
- Rimm-Kaufman, S. E., Larsen, R. A. A., Baroody, A. E., Curby, T. W., Ko, M., Thomas, J. B., & DeCoster, J. (2014). Efficacy of the responsive classroom approach: Results from a 3-year, longitudinal randomized controlled trial. American Educational Research Journal, 51(3), 567–603. https://doi.org/10.3102/0002831214523821.CrossRefGoogle Scholar
- Schulte, P. A., Stephenson, C. M., Okun, A. H., Palassis, J., & Biddle, E. (2005). Integrating occupational safety and health information into vocational and technical education and other workforce preparation programs. American Journal of Public Health, 95(3), 404–411. https://doi.org/10.2105/AJPH.2004.047241.CrossRefPubMedPubMedCentralGoogle Scholar
- Staff, J., Messersmith, E. E., & Schulenberg, J. E. (2009). Adolescents and the world of work. In R. Lerner & L. Steinberg (Eds.), Handbook of adolescent psychology (3rd ed., pp. 270–313). New York, NY: Wiley. https://doi.org/10.1002/9780470479193.adlpsy002009.Google Scholar
- U.S. Centers for Disease Control and Prevention. (2010). Occupational injuries and deaths among younger workers: United States, 1998–2007. Morbidity and Mortality Weekly Report, 59(15), 449–455.Google Scholar
- U.S. Department of Labor, Wage and Hour Division (2013). The hazardous occupations orders (HOs) for nonagricultural employment. https://www.dol.gov/whd/regs/compliance/childlabor101_text.htm.
- U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health. (1999). Promoting safe work for young workers: A community-based approach. (DHHS [NIOSH] Publication No. 1999–141). Cincinnati, OH: National Institute for Occupational Safety and Health.Google Scholar
- U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health. (2015). Youth@Work—talking safety: A safety and health curriculum for young workers, Nebraska edition (DHHS [NIOSH] Publication No. 2015–145). Cincinnati, OH: National Institute for Occupational Safety and Health.Google Scholar
- Weller, N. F., Cooper, S. P., Tortolero, S. R., Kelder, S. H., & Hassan, S. (2003). Work-related injury among south Texas+middle school students: Prevalence and patterns. Southern Medical Journal, 96(12), 1213–1220. https://doi.org/10.1097/01.SMJ.0000077063.17684.6D.CrossRefPubMedGoogle Scholar
- Yu, C. Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcome (Unpublished doctoral dissertation). Los Angeles: University of California.Google Scholar
- Zinbarg, R. E., Yovel, I., Revelle, W., & McDonald, R. P. (2006). Estimating generalizability to a latent variable common to all of a scale’s indicators: A comparison of estimators for ω. Applied Psychological Measurement, 30(2), 121–144. https://doi.org/10.1177/0146621605278814.CrossRefGoogle Scholar