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Purpose. The purpose of this study was to determine the extent of implementation of school health education curricula, to identify factors which enhanced or impeded implementation, and to examine the link between the adoption and implementation phases of the diffusion process. Design. The study used an experimental design; 22 school districts were randomly assigned to intervention and control conditions. Setting. The study was conducted in North Carolina. Subjects. All teachers in the study districts identified by their schools as "eligible to teach health" were included in the sample. Intervention. An in-depth training was conducted on the use of the middle school tobacco prevention curriculum that had been adopted. Measures. Three measures were used to assess implementation of the curricula. Independent variables of interest included organizational size and climate, teacher training, how long it took to make the adoption decision, and attitudes toward tobacco use prevention curricula. Response rates for these measures ranged from 44% to 78%. Results. Nonparametric correlations and regression modeling indicated that larger organizational size and teacher training were the strongest predictors of curricula implementation. A favorable organizational climate within school districts also improved implementation. Conclusions. While interventions to increase adoption of school health education curricula should focus on larger school districts, the majority of efforts to improve implementation should focus on smaller districts.

Once a health promotion program has proven to be effective in one or two initial settings, attempts may be made to transfer the program to new settings. One way to conceptualize the transference of health promotion programs from one locale to another is by considering the programs to be innovations that are being diffused. In this way, diffusion of innovation theory can be applied to guide the process of program transference. This article reports on the development of six questionnaires to measure the extent to which health promotion programs are successfully disseminated: Organizational Climate, Awareness-Concern, Rogers's Adoption Variables, Level of Use, Level of Success, and Level of Institutionalization. The instruments are being successfully used in a study of the diffusion of health promotion/tobacco prevention curricula to junior high schools in North Carolina. The instruments, which measure the four steps of the diffusion process, have construct validity since they were develóped within existing theories and are derived from the work of previous researchers. No previous research has attempted to use instruments like these to measure sequentially the stages of the diffusion process.

Purpose. To examine the effects of 10 youth developmental assets on adolescent tobacco use. Design. Survey of a randomly selected sample using in-home interviewing methodology. Setting. Inner-city areas of two midsized Midwestern cities. Subjects. The researchers studied 1,350 teen-parent pairs. Measures. Demographic information, adolescent self-reported tobacco use, eight developmental asset Likert scales, and two one-item developmental asset measures. Results. The response rate was 51%. Logistic regression results indicate that youth who possess nine of 10 developmental assets examined are significantly less likely to report tobacco use than youth with low levels of assets. Adjusting for youth age, race, gender, parental income and education, and family structure, significant odds ratios include the following: nonparental adult role model, 2.09 (95% confidence interval [CI] = 1.45, 3.02); peer role models, 2.48 (95% CI = 1.87, 3.29); family communication, 1.73 (95% CI = 1.29, 2.31); use of time (organized groups), 1.77 (95% CI = 1.28, 2.44); use of time (religion), 2.49 (95% CI = 1.86, 3.33); good health practices (exercise/nutrition), 1.61 (95% CI = 1.21, 2.14); community involvement, 1.66 (95% CI = 1.07, 2.58); future aspirations, 2.06 (95% CI = 1.42, 2.99); and responsible choices, 2.21 (95% CI = 1.55, 3.15). Conclusions. The findings of this study support the view that certain developmental assets may serve to protect youth from risk-taking behaviors, particularly tobacco use. Limitations include cross-sectional data and three scales with alphas below .7.

AbstractThis chapter reports on the evaluation of state and local level National Standards for Culturally and Linguistically Appropriate Services in Health Care (aka CLAS Standards), specifically those standards addressing the health needs of sexual minority individuals, with an emphasis on the inclusion of bisexual+ communities and the implications of bisexual+ (non)inclusion in CLAS standards. At the state and local levels, bisexual identity is rarely recognized as distinct from other sexual identities. This lack of representation raises an essential issue of how local communities, states, and the federal government struggle with sexual minority data classification and prioritizing health benchmarks for sexual minority populations and subpopulations. We also found that the CLAS cultural competency policy definition at the federal level lacks an appropriate degree of bi‐inclusivity. The findings from this study reveal that the five states in our sample implemented CLAS Standards in ways that demonstrated bi‐erasure. Specifically, states defined gender and sexual minorities through exclusionary categories that place emphasis on the "Other". LGBTQ+ evaluators can rely on the Principles of LGBTQ+ Evaluation to create strategies that demonstrate how to effectively address the intersecting ramifications of bi‐erasure at the policy level.

Purpose. To investigate the possible positive cumulative effects that youth assets may have on youth sexual behaviors. Design. Cross-sectional data were collected from a randomly selected population by in-home, in-person interviews. Mantel-Haenszel chi-square test and logistic regression were conducted to determine the potential cumulative effects that youth assets may have on youth sexual behaviors and to test for significant asset by sexual risk behavior trends. Setting. Racially diverse inner-city neighborhoods in two midwestern cities. Subjects. Teenagers (n = 1350; mean age = 15.2 years) and parents of the teenagers (n = 1350). The response rate was 51%. Measures. Demographic factors, nine youth assets, and five sexual behavior outcomes. Results. Significant (p < .05) trends and odds ratios (ORs) were found for three of the five youth sexual behavior outcomes. Youths with more assets were more likely to have never participated in sexual intercourse (OR = 1.32). Of sexually active youths, those with more assets were more likely to have delayed first intercourse until at least 17 years of age (OR = 1.47) and to have used birth control at last sexual intercourse (OR = 1.18). Nonsignificant (p > .05) results were found for current sexual activity and number of sexual partners. Conclusions. The results generally support the notion that the more assets adolescents possess, the more likely they are to engage in positive behavior. Practitioners planning asset-based teenage pregnancy prevention programs might consider promoting several specific youth assets.

Purpose.To investigate relationships among youth risk behaviors and demographic factors using an adolescent age group approach.Design.Cross-sectional data from a randomly selected population. Risk behaviors were compared within specific demographic factors and by adolescent age groups.Setting.Racially diverse, inner-city neighborhoods in two midwestern cities.Subjects.Teenagers (n = 1350) and parents (n = 1350) of the teenagers.Measures.Truancy; arrested/picked up by police; weapon carrying; fighting; sexual intercourse; tobacco, alcohol, and other drug use; demographic factors; and family structure.Results.Youth mean age was 15.4 (±1.7) years and 52% were female; racial/ethnic characteristics were 47% White, 22% Black, 19% Hispanic, and 10% Native American. Parents' mean age was 42.2 (±8.4) years and 81% were female. χ2analyses indicated numerous significant (p < .05) youth risk behavior differences within the demographic factors and that many of the differences varied by adolescent age group. For example, risk behavior differences within racial/ethnic groups were most profound in the middle and older age groups, whereas risk behavior differences within parent income, education levels, and family structure were most apparent in the younger age groups. Of the demographic factors, family structure was most frequently associated with the risk behaviors.Conclusions.The results generally suggest that the relationships among risk behaviors and demographic factors vary within the adolescent age groups included in this study. The results will be useful for developing age-appropriate prevention programs for youth who fit the profile for these risk behaviors. The study protocol also includes specific sampling methods that may be useful for future studies that intend to collect data from difficult-to-reach populations.