| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
RESEARCH AND PRACTICE |
Lei-Shih Chen is with the Department of Public Health, University of North Florida, Jacksonville. Oi-Man Kwok is with the Department of Educational Psychology, Texas A&M University, College Station. Patricia Goodson is with the Department of Health and Kinesiology, Texas A&M University, College Station.
Correspondence: Requests for reprints should be sent to Lei-Shih Chen, Department of Public Health, University of North Florida, 1 UNF Drive, Jacksonville, FL 32224-2673 (l.chen{at}unf.edu).
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Objectives. We examined US health educators’ likelihood of adopting genomic competencies—specific skills and knowledge in public health genomics—into health promotion and the factors influencing such likelihood.
Methods. We developed and tested a model to assess likelihood to adopt genomic competencies. Data from 1607 health educators nationwide were collected through a Web-based survey. The model was tested through structural equation modeling.
Results. Although participants in our study were not very likely to adopt genomic competencies into their practice, the data supported the proposed model. Awareness, attitudes, and self-efficacy significantly affected health educators’ likelihood to incorporate genomic competencies. The model explained 60.3% of the variance in likelihood to incorporate genomic competencies. Participants’ perceived compatibility between public health genomics and their professional and personal roles, their perceptions of genomics as complex, and the communication channels used to learn about public health genomics significantly related to genomic knowledge and attitudes.
Conclusions. Because US health educators in our sample do not appear ready for their professional role in genomics, future research and public health work-force training are needed.
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
An emerging field, public health genomics, focuses on "the study and application of knowledge about the elements of the human genome and their functions, including interactions with the environment, in relation to health and disease in populations."3 This focus signals important "changes in the landscape" of public health2,4 and requires that public health workers develop new professional skills. Health promotion scholars,5,6 alongside many professional organizations and agencies such as the American Public Health Association (APHA),7 the Institute of Medicine,1 the National Coalition for Health Professional Education in Genetics,8 and the Centers for Disease Control and Prevention (CDC),9 have advocated the adoption of specific genomic competencies by the public health workforce. What Caumartin, Baker, and Marrs affirmed of public health students applies invariably to all public health professionals:
Students of Public Health do not need to be geneticists. They should, however, be public health specialists who possess an understanding of how the application of human genetic information and technology is creating a paradigm shift in public health and prevention strategies.10(p569)
The term "genomic competencies" refers to specific skills and knowledge in public health genomics.5,9 According to the CDC,9 as members of the public health workforce, health educators should develop 7 specific genomic competencies (Table 1
). Although these genomic competencies have been defined and proposed, to the best of our knowledge, no studies have examined whether health educators in the United States are ready to adopt them into their health promotion practice.
|
In our previous study, we assessed US health educators’ attitudes toward genomic competencies, their awareness of efforts in the field to promote and incorporate genomics, and their basic and applied genomic knowledge. Findings indicated that the sample espoused negative attitudes toward genomic competencies, low awareness levels, and deficient knowledge. Yet exposure to training in genetics and genomics appeared to influence attitudes, awareness, and knowledge.11
In this study, we examined health educators’ likelihood of adopting genomic competencies into health promotion research and practice and the factors that might influence such likelihood. We proposed a conceptual, theory-based model, grounded in 4 behavior change theories: diffusion of innovations theory,12 the theory of planned behavior,13 the health belief model,14 and social cognitive theory.15 Findings from qualitative, in-depth interviews with 24 health educators also informed the development of this model (Figure 1).
|
We sought to answer 4 specific questions: (1) How likely are health educators to adopt genomic competencies into health promotion research and practice? (2) Does the proposed model adequately explain health educators’ likelihood of adopting genomic competencies? In other words, is the model helpful for understanding what shapes health educators’ likelihood of adopting genomic competencies? (3) How much variance in the likelihood variable is accounted for by the predictor variables in this proposed theoretical model? (4) Which variable in the theoretical model is the best predictor of health educators’ likelihood of adopting genomic competencies into health promotion research and practice? Does this variable differ significantly from other variables?
|
METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
The final version of the questionnaire contained 72 questions and required 15 to 20 minutes for completion. Respondents could enter a drawing for 1 of 4 money order certificates ($50.00 each). Access to educational resources regarding public health genomics1,3,9,18,19 was also provided at the completion of the survey as incentive.
Participants
Because no comprehensive sampling frame for the population of US health educators exists, we surveyed all members of major professional organizations. We sent 3 personalized invitations to 8058 valid e-mail addresses from membership lists of the National Commission for Health Education Credentialing, Inc, the Society for Public Health Education, the School Health Education and Services Section of the American Public Health Association, and the Health Education Directory. A total of 1862 health educators completed the HPGS (an adjusted response rate of 23.1%). After deleting questionnaires containing more than half of missing data (13.7%), 1607 respondents composed the final sample.
Measures
All measures were developed or adapted specifically for this study (Table 2). The table provides the following for each construct: its definition, how it was measured, each item’s response scale, and score interpretation.
|
),21 as well as to obtain descriptive statistics. We used confirmatory factor analysis to assess the construct validity of the latent variables using Analysis of Moment Structures (AMOS) 7.0 (SPSS Inc, Chicago, IL) and evaluated the proposed theoretical model with structural equation modeling techniques. In addition, univariate and multivariate normality were examined.22 |
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Research Questions
Question 1.
The first research question was, How likely are health educators to adopt genomic competencies into health promotion research and practice? Table 1
presents health educators’ likelihood of adopting 7 genomic competencies into health promotion. In general, likelihood of adoption was low (29.3%) and varied according to specific tasks. Percentages of health educators indicating they were somewhat or extremely likely to adopt the competencies ranged from approximately 23%, who indicated they were likely to translate complex genomic information for use in community-based health education programs (competency 1) to nearly 35%, who said they were willing to integrate genomic components into community-based genomic education programs (competency 6).
Question 2. The second research question was, Does the proposed model adequately explain health educators’ likelihood of adopting genomic competencies? Is the model helpful for understanding what shapes health educators’ likelihood of adopting genomic competencies? Statistical testing of the originally proposed model revealed the need for minor modifications: the variable "experiences regarding the use of genomic technologies or information" was deleted; the factors "basic knowledge" and "applied knowledge" were combined into 1 variable (basic and applied knowledge), and Internet channel and interpersonal channels (of communication) were combined. Data validity and reliability for all other model variables were psychometrically sound.
Figure 2 shows the final structural model after the minor changes described previously. The final model indicated that participants’ likelihood to adopt genomic competencies into health promotion was significantly affected by their awareness (B = 0.06; P < .001), their attitudes (B = 0.62; P < .001), and self-efficacy (B = 0.22; P < .001).
|
Similarly, basic and applied genomic knowledge was affected only by the degree of compatibility between respondents’ professional or personal values and public health genomics (B = 0.14; P < .001), their exposure to various communication channels (B = 0.11; P < .001), and their perceptions of the complexity of public health genomics (B = –0.07; P = .012).
Health educators participating in our study had a more positive attitude toward public health genomics if they believed that genomics and public health genomics were complex (B = 0.13; P < .001), if they saw that public health genomics had an advantage over traditional forms of health promotion intervention (B = 16; P < .001), if they perceived consistency between public health genomics and their personal or professional beliefs (B = 0.26; P < .001), if they had been more exposed to public health genomics through various communication channels (B = 0.20; P < .001), and if they were more aware of efforts made in the health promotion field regarding public health genomics (B = 0.19; P < .001). However, their concerns regarding the misuse of genomic discoveries (B = 0.005; P = .816) and basic and applied genomic knowledge (B = 0.03; P = .265) did not significantly influence their attitude.
Weaker perceptions of obstacles to adopt genomic competencies into practice (B = –0.30; P < .001) and favorable attitudes toward genomic competencies (B = 0.46; P < .001) both had a significant impact on respondents’ confidence (self-efficacy) to adopt genomic-related tasks.
To assess whether our proposed theoretical model as a whole adequately explained the survey findings, we examined various fit indexes associated with structural equation modeling techniques. Similar to the notion of effect sizes in regression models, indicating how well the model fit the empirical data, the model fit between our proposed theoretical model and the survey data was assessed initially with the 
2 goodness-of-fit test. Because the 2 statistic is sensitive to sample size,23 we used 3 additional fit indices—the root mean square error of approximation (RMSEA), the standardized root mean square residual (SRMR), and the comparative fit index (CFI)—to evaluate the adequacy of the model. In general, an RMSEA of 0.08 or less, an SRMR of less than 0.10, and a CFI greater than 0.90 indicate adequate fit.24–26 As expected, the 2 statistic was significantly rejected in the structural model (2[37] = 260.304; P < .001) because of the large sample size. The other fit indexes, however, indicated the final model fit the observed data well (RMSEA = 0.061; SRMR = 0.039; and CFI = 0.966). Thus, the proposed theoretical model appears appropriate for explaining this sample of health educators’ likelihood of adopting genomic competencies into health promotion. The model also provides an overall picture of the factors shaping the sample’s likelihood of adoption: awareness, attitudes, and self-efficacy—all 3 as direct influences—and compatibility (with personal and professional beliefs), communication channels, perceived relative advantage of genomics, perceived complexity, and perceived barriers—all 5 as exerting indirect effects on likelihood.
Question 3. The third research question was, How much variance in the likelihood variable is accounted for by the predictor variables in this proposed theoretical model? Altogether, the model’s variables explained 60.3% of the variance in health educators’ likelihood of adopting genomic competencies into health promotion, reinforcing the notion that the proposed model is appropriate as an initial step for understanding these professionals’ willingness to incorporate genomics into public health.
Question 4. The fourth question was, Which variable in the theoretical model is the best predictor of health educators’ likelihood of adopting genomic competencies into health promotion research and practice? Does this variable differ significantly from other variables? Health educators’ likelihood of adopting genomic competencies (likelihood variable) was significantly predicted by their awareness (B = 0.06), attitudes (B = 0.62), and self-efficacy (B = 0.22). Among these 3, attitude was the strongest predictor of likelihood to adopt (B = 0.62), with positive attitudes predicting increased likelihood of adoption.
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
At the same time, results from the test of our conceptual model suggest important pathways for addressing these apparent shortcomings. The structural equation modeling analysis confirmed that the empirical data supported the theoretical model and explained at least 60% of the variance in likelihood of adoption. As a potentially robust framework for understanding health educators’ likelihood of adopting genomic competencies, the model points to 3 important intrapersonal factors that directly affect intentions to adopt: awareness, attitudes, and self-efficacy.
Because these factors can be influenced through educational strategies, relevant training for health educators should gain priority among tactics to improve the public health workforce’s capacity for public health genomics. Our study suggests, in particular, that attitude is the strongest predictor of likelihood; therefore, training focused on forming professional attitudes toward genomics should receive special attention. Furthermore, such training should carefully consider the factors that appear to affect professional attitudes toward public health genomics: compatibility between genomics and professional or personal beliefs, communication channels for learning about genomics, and perceptions of its relative advantage and complexity.
It will be important for leading professional organizations not only to require adoption of specific skills and competencies but also to participate in developing appropriate capacity among its professionals, taking into account that many factors will affect capacity development, besides mere knowledge. In this regard, APHA has taken an important step by establishing a genomics forum29 dedicated exclusively to public health genomics issues. Visionary leadership will emerge from and be nurtured within this group, and further training needs undoubtedly will receive careful attention in the future.
Although attitude proved to be a strong determinant of likelihood in our model, respondents’ basic and applied knowledge of genomics did not influence either their attitudes or their likelihood; only awareness affected these 2 variables. Even though the diffusion of innovations theory maintains "it is usually possible to adopt an innovation without principles-knowledge [or how-to-knowledge],"12(p173) it behooves the health education workforce, in particular, and the public health workforce, broadly, to acquire a basic understanding of the relation between genomics and public health—even if such knowledge were circumscribed to social and behavioral dimensions of public health genomics and not to its biochemical and physiological aspects.
Last, our final model also highlighted 2 factors that affect attitudes and knowledge simultaneously: complexity and communication channels. Although perceptions of public health genomics as a complex topic influenced both knowledge and attitudes, the direction of the associations varied: perceived complexity was negatively associated with genomic knowledge and positively related to health educators’ attitudes. As anticipated, if health educators believed it to be difficult to keep up with genomics and public health genomics, they scored lower on the knowledge scale. Yet despite the diffusion of innovations theory’s suggestion that complexity is a barrier affecting the rate of adopting an innovation,12 it is unclear, in this sample, why a strong perception of public health genomics as complex resulted in more-positive attitudes toward incorporating genomic discoveries into health promotion. Future research efforts would do well to probe into this finding and examine the interaction among perceptions of complexity, genomic knowledge, and attitudes of public health professionals.
In addition, immediate interest and attention should be directed at understanding how best to deliver training and information regarding public health genomics to public health professionals. Our findings suggest the use of mass media, Internet, and continuing education forums might prove invaluable for communication and training purposes. Although some genomic education and training tools have been developed for public health workers,3,19 assessments of various types of delivery channels and determination of the most cost-effective means for delivery are still needed.
Limitations
We used a Web-based survey approach in this study. Although this is a promising alternative to traditional mailed and telephone surveys, we encountered the same constraints found in previous studies.30,31 The first is low response rate (23.1%). Despite traditionally lower response rates for electronically delivered surveys, our rates appear to be in line with other Web-based studies.30–32 Other limitations faced by our Web-based data collection included the inability to assess a representative sample of US health educators because of the absence of a nationwide e-mail listing, participants’ inability or unwillingness to complete a Web-based survey,33 and participants’ forwarding their invitation e-mails to colleagues (to enlist other potentially interested participants) without informing us and thus potentially altering the original sampling frame. These limitations did not allow us to determine how biased the final sample became, given potential differences in interest in and knowledge of genetics and genomics or differences in comfort levels associated with computer use.
Recommendations
Despite the contributions this study makes to the emerging public health genomics field, it also is limited in scope, because our proposed model focused exclusively on individual-level factors. One question that remains unanswered is whether various social, organizational, and environmental factors carry more weight than individuals’ attitudes in shaping health educators’ willingness to adopt genomic competencies. For instance, if specific genomic competencies were required within specific work settings, would health educators be more willing to adopt these competencies, despite their personal views? Inquiry into this and similar issues, along with inquiries into the potential impact of adding genomics-related topics in public health training programs and certification exams, will become essential in the near future to determine the directions in which public health genomics should be steered.
The Associations of Schools of Public Health, for example, has highlighted the importance of genomics in the Master’s Degree in Public Health Core Competency Development Project,34 and the certification examination soon to be offered by the National Board of Public Health Examiners includes public health biology as one of its interdisciplinary and cross-cutting competencies.35 These strategies may help shape public health workers’ intention to develop genomic competencies and may, in themselves, represent sizeable forces directing the public health genomics field. One recommendation we would make for future studies, therefore, would be to examine the potential influence of these strategies, alongside multiple social, organizational, and professional environment factors, and to compare these factors’ relative impact against individual-level variables such as those examined in this study. This determination might prove extremely useful for guiding appropriate public health genomic policies and training programs in the near future.
Finally, this study was limited to health educators because they represent the public health dimension most familiar to the authors. Although the lessons learned from this group might extrapolate well to other members of the public health workforce, this is a hypothesis requiring further testing. Evidence from studies of other health care professionals suggests our findings are not unique.27,28 Therefore, inquiry into various public health work groups is also recommended to foster the development of professional collaboration and the advancement of genomics for the benefit of the public’s health.
|
Acknowledgments |
|---|
Human Participant Protection
The institutional review board for Texas A&M University approved all aspects of this project.
|
Footnotes |
|---|
Contributors
Lei-Shih Chen designed the survey, collected data, performed the data analysis, and drafted the article. Oi-Man Kwok provided statistical analysis consulting. Patricia Goodson supervised the study and revised and edited the final draft.
Accepted for publication January 17, 2008.
|
References |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
2. Khoury MJ, Burke W, Thomson EJ. Genetics and Public Health in the 21st Century. New York, NY: Oxford University Press; 2000.
3. Centers for Disease Control and Prevention. Genomics for Public Health Practitioners 2004. Available at: http://www.cdc.gov/genomics/training/GPHP/default.htm. Accessed July 14, 2007.
4. Khoury MJ. Relationship between medical genetics and public health: changing the paradigm of disease prevention and the definition of a genetic disease. Am J Med Genet.1997;17:289–291.[CrossRef]
5. Chen LS, Goodson P. Entering the public health genomics era: why must health educators develop genomic competencies? Am J Health Educ.2007;38: 158–166.
6. Kardia SL, Wang C. The role of health education and behavior in public health genetics. Health Educ Behav.2005;32:583–588.[Abstract]
7. American Public Health Association. The Role of Genomics in Public Health. Available at: http://www.apha.org/advocacy/policy/policysearch/default.htm?id=275. Accessed July 14, 2007.
8. National Coalition for Health Professional Education in Genetics. Core Competencies in Genetics for Health Professionals, 2007. Available at: http://www.nchpeg.org/core/Core_Comps_English_2007.pdf. Accessed April 9, 2008.
9. Centers for Disease Control and Prevention. Genomic Competencies for the Public Health Workforce, 2001. Available at: http://www.cdc.gov/genomics/training/competencies/comps.htm. Accessed July 14, 2007.
10. Caumartin SM, Baker DL, Marrs CF. Training in public health genetics. In: Khoury MJ, Burke W, Thomson EJ, eds. Genetics and Public Health in the 21st Century. New York, NY: Oxford University Press; 2000: 566–599.
11. Chen LS, Goodson P. Public health genomics knowledge and attitudes: A survey of public health educators in the United States. Genet Med.2007;9: 496–503.[Web of Science][Medline]
12. Rogers EM. Diffusion of Innovations. 5th ed. New York, NY: Free Press; 2003.
13. Montano DE, Kasprzyk D. The theory of reasoned action and the theory of planned behavior. In: Glanz K, Rimer BK, Lewis FM, eds. Health Behavior and Health Education. San Francisco, CA: Jossey-Bass; 2002: 67–98.
14. Janz NK, Champion VL, Strecher VJ. The health belief model. In: Glanz K, Rimer BK, Lewis FM, eds. Health Behavior and Health Education. San Francisco, CA: Jossey-Bass; 2002:45–66.
15. Bandura A. Self-Efficacy: The Exercise of Control. New York, NY: W.H. Freeman & Company; 1997.
16. Buhi ER, Goodson P, Neilands TB. Structural equation modeling: A primer for health behavior researchers. Am J Health Behav.2007;31:74–85.[Web of Science][Medline]
17. Dillman DA. Mail and Internet Surveys: The Tailored Design Method. New York, NY: John Wiley & Sons; 2002.
18. Green LW, Kreuter MW. Health Program Planning: An Educational and Ecological Approach. 4th ed. New York, NY: McGraw-Hill; 2005.
19. Centers for Disease Control and Prevention. Six Weeks to Genomic Awareness. Available at: http://www.cdc.gov/genomics/training/sixwks.htm. Accessed May 3, 2007.
20. Buhi ER, Goodson P, Neilands T. Out of sight, not out of mind: strategies for handling missing data in health behavior research. Am J Health Behav.2008; 32:83–92.[Web of Science][Medline]
21. Brace N, Kemp R, Snelgar R. SPSS for Psychologists. 3rd ed. Mahwah, NJ: Lawrence Erlbaum Associates; 2006.
22. Curran PJ, West SG, Finch JF. The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychol Methods.1996;1: 16–29.[Medline]
23. Bentler PM. On the fit of models to covariances and methodology to the Bulletin. Psychol Bull.1992; 112:400–404.[CrossRef]
24. Hu L-t, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling.1999;6: 1–55.
25. Kline RB. Principles and Practice of Structural Equation Modeling. New York, NY: Guilford; 2005.
26. Browner MW, Cudeck R. Alternative Ways of Assessing Model Fit. Newbury Park, CA: Sage; 1993.
27. Suther SG, Goodson P. Texas physicians’ perceptions of genomic medicine as an innovation. Clin Genet.2004;65:368–377.[CrossRef][Web of Science][Medline]
28. Irwin DE, Millikan RC, Stevens R, et al. Genomics and public health practice: a survey of nurses in local health departments in North Carolina. J Public Health Manag Pract.2004;10:539–544.[Medline]
29. American Public Health Association. Genomics Forum. Available at: http://aphagenomicsforum.org/index.php?note=Home. Accessed February 12, 2008.
30. Braithwaite D, Emery J, De Lusignan S, Sutton S. Using the Internet to conduct surveys of health professionals: a valid alternative? Fam Pract.2003;20: 545–551.
31. McMahon SR, Iwamoto M, Massoudi MS, et al. Comparison of e-mail, fax, and postal surveys of pediatricians. Pediatrics.2003;111(4 Pt 1):e299–e303.
32. Fertman CI. A report on the 2004 SOPHE publications readership survey. Health Promot Pract.2006; 7:376–383.
33. Alvarez RM, VanBeselaere C. Web-based surveys. In: Kempf-Leonard K, eds. Encyclopedia of Social Measurement. London, UK: Academic; 2005:955–962.
34. Associations of Schools of Public Health. Master’s Degree in Public Health Core Competency Development Project. Available at: http://www.asph.org/userfiles/version2.3.pdf. Accessed February 12, 2008.
35. The National Board of Public Health Examiners. CPH Examination. Available at: http://www.publichealthexam.org. Accessed April 9, 2008.
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
