Preparing Teens to Stay Safe and Healthy on the Job: a Multilevel Evaluation of the Talking Safety Curriculum for Middle Schools and High Schools
US adolescents experience a higher rate of largely preventable job-related injuries compared with adults. Safety education is considered critical to the prevention of these incidents. This study evaluates the effectiveness of a foundational curriculum from the National Institute for Occupational Safety and Health (NIOSH), Talking Safety, to change adolescents’ workplace safety and health knowledge, attitude, subjective norm, self-efficacy, and behavioral intention to engage in workplace safety actions. The study also examines the impact of teacher fidelity of curriculum implementation on student outcomes. A multilevel evaluation, based on a modified theory of planned behavior, was conducted in 2016 with 1748 eighth-graders in Miami-Dade, Florida. Post-intervention, students had statistically significant increases (p < .05) in mean scores across outcomes: workplace safety knowledge (34%), attitude (5%), subjective norm (7%), self-efficacy (7%), and behavioral intention (7%). Consistent with theory, gains in attitude (b = 0.25, p < .001), subjective norm (b = 0.07, p < .01), and self-efficacy (b = 0.55, p < .001) were associated with gains in behavioral intention. Higher levels of implementation fidelity were associated with significant gains across outcome measures: knowledge (b = 0.60, p < .001), attitude (b = 0.08, p < .01), subjective norm (b = 0.04, p < .001), self-efficacy (b = 0.07, p < .01) and behavioral intention (b = 0.07, p < .01). Findings demonstrate the effectiveness of Talking Safety, delivered with fidelity, at positively changing measured outcomes, and provide support for using this curriculum as an essential component of any school-based, injury prevention program for young workers.
KeywordsYoung worker Occupational safety and health Injury prevention Middle school Theory of planned behavior Fidelity of implementation Multilevel modeling
We thank the Miami-Dade County Public School System for facilitating this research, especially Mr. Cristian Carranza, Administrative Director, Division of Academics (STEAM); Dr. Ava D. Rosales, Executive Director, Department of Mathematics and Science; Mr. Dane Jaber, Instructional Supervisor, Department of Mathematics and Science; and the School Board of Miami-Dade County, Florida. For assistance with teacher training, we thank Robin Dewey, LOHP, University of California, Berkeley. For reviews of and thoughtful feedback on this manuscript, we thank Dr. Lehua Choy and Dr. Charlene Baker, University of Hawai‘i.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
The NIOSH Institutional Review Board (IRB) waived the documentation of informed consent because the project occurred within a regularly established educational setting, used a publically available curriculum adopted by the school district as part of established and ongoing classroom studies, presented no risk of harm to participants, and involved no procedures for which written consent is normally required outside of the research context.
The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health. This manuscript’s data will not be deposited.
- Bandura, A. (1997). Self-efficacy: The exercise of control. New York: 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. https://doi.org/10.1136/bmjopen-2016-015100.
- Brener, N. D., Kann, L., Shanklin, S., Kinchen, S., Eaton, D. K., Hawkins, J., et al. (2013). Methodology of the youth risk behavior surveillance system—2013. Morbidity and Mortality Weekly Report. Recommendations and Reports, 62(1), 1–20.Google Scholar
- Bureau of Labor Statistics. (2005). Work activity of high school students: Data from the National Longitudinal Survey of Youth 1997. http://www.bls.gov/news.release/archives/nlsyth_04272005.pdf. Accessed 6 July 2018.
- 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
- Centers for Disease Control and Prevention. (2016). National health education standards. https://www.cdc.gov/healthyschools/sher/standards/index.htm. Accessed 18 May 2018.
- Department of Labor, Wage, and Hour Division. (2016). Child labor provisions of the Fair Labor Standards Act (FLSA) for nonagricultural occupations. https://www.dol.gov/whd/regs/compliance/whdfs43.pdf. Accessed 6 July 2018.
- Domina, T., Brummet, Q., Pharris-Ciurej, N., Porter, S. R., Penner, A., Penner, E., et al. (2017). Capturing more than poverty: School free and reduced-price lunch data and household income (CARRA Working Paper Series Working Paper 2017–09). Retrieved from https://www.census.gov/library/working-papers/2017/adrm/carra-wp-2017-09.html. Accessed 9 January 2019.
- Downes, A., Novicki, E., & Howard, J. (2018). Using the contribution analysis approach to evaluate science impact: A case study of the National Institute for Occupational Safety and Health. American Journal of Evaluation. https://doi.org/10.1177/1098214018767046.
- Guerin, R. J., Toland, M. D., Okun, A. H., Rojas-Guyler, L., & Bernard, A. L. (2018). Using a modified theory of planned behavior to examine adolescents’ workplace safety and health knowledge, perceptions, and behavioral intention: A structural equation modeling approach. Journal of Youth and Adolescence. https://doi.org/10.1007/s10964-018-0847-0.
- Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling. https://doi.org/10.1080/10705519909540118.
- MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York: Lawrence Erlbaum Associates.Google Scholar
- M-DCPS. (2017). Statistical highlights 2016–2017. http://drs.dadeschools.net/StatisticalHighlights/SH1617.pdf. Accessed 9 Jan 2019.
- 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: Wiley.Google Scholar
- Mortimer, J. T. (2010). The benefits and risks of adolescent employment. The Prevention Researcher, 17(2), 8–11.Google Scholar
- Muthén, L. K., & Muthén, B. O. (1998–2017). Mplus user’s guide (7th ed.). Los Angeles: Muthén & Muthén.Google Scholar
- NIOSH. (2018a). The work-related injury statistics query system (Work-RISQS). https://wwwn.cdc.gov/wisards/workrisqs/. Accessed 8 July 2018.
- NIOSH (2018b). Talking safety. https://www.cdc.gov/niosh/talkingsafety/default.html. Accessed 6 July 2018.
- Smith, J., Purewal, B. P., Macpherson, A., & Pike, I. (2018). Metrics to assess injury prevention programs for young workers in high-risk occupations: A scoping review of the literature. Health Promotion and Chronic Disease Prevention in Canada: Research, Policy and Practice, 38(5), 191–199.CrossRefGoogle Scholar