Belonging is an essential aspect of psychological functioning. Schools offer unique opportunities to improve belonging for school-aged children. Research on school belonging, however, has been fragmented and diluted by inconsistency in the use of terminology. To resolve some of these inconsistencies, the current study uses meta-analysis of individual and social level factors that influence school belonging. These findings aim to provide guidance on the factors schools should emphasise to best support students. First, a systematic review identified 10 themes that influence school belonging at the student level during adolescence in educational settings (academic motivation, emotional stability, personal characteristics, parent support, peer support, teacher support, gender, race and ethnicity, extracurricular activities and environmental/school safety). Second, the average association between each of these themes and school belonging was meta-analytically examined across 51 studies (N = 67,378). Teacher support and positive personal characteristics were the strongest predictors of school belonging. Results varied by geographic location, with effects generally stronger in rural than in urban locations. The findings may be useful in improving perceptions of school belonging for secondary students through the design of policy, pedagogy and teacher training, by encouraging school leaders and educators to build qualities within the students and change school systems and processes.

What Schools Need to Know About Fostering School Belonging: a Meta-analysis

Identifying crash “hotspots”, “blackspots”, “sites with promise”, or “high risk” locations is standard practice in departments of transportation throughout the US. The literature is replete with the development and discussion of statistical methods for hotspot identification (HSID). Theoretical derivations and empirical studies have been used to weigh the benefits of various HSID methods; however, a small number of studies have used controlled experiments to systematically assess various methods.

Using experimentally derived simulated data—which are argued to be superior to empirical data, three hot spot identification methods observed in practice are evaluated: simple ranking, confidence interval, and Empirical Bayes. Using simulated data, sites with promise are known a priori, in contrast to empirical data where high risk sites are not known for certain.

To conduct the evaluation, properties of observed crash data are used to generate simulated crash frequency distributions at hypothetical sites. A variety of factors is manipulated to simulate a host of ‘real world’ conditions. Various levels of confidence are explored, and false positives (identifying a safe site as high risk) and false negatives (identifying a high risk site as safe) are compared across methods. Finally, the effects of crash history duration in the three HSID approaches are assessed.

The results illustrate that the Empirical Bayes technique significantly outperforms ranking and confidence interval techniques (with certain caveats). As found by others, false positives and negatives are inversely related. Three years of crash history appears, in general, to provide an appropriate crash history duration.

Experimental evaluation of hotspot identification methods

The current study examined the impact of a brief motivational interviewing (MI) intervention (Project CHAT) on alcohol consumption and drug use for high-risk teens in a primary care clinic that provides health care for underserved populations. Youth (N=42, 48% male) were screened, and those eligible completed a baseline survey. Baseline survey completers were randomly assigned to usual care or to an MI intervention and completed a 3-month follow-up survey. The sample (age 12 to 18 years) was 85.7% Hispanic or Latino, 9.5% African American, and 4.8% White. At the 3-month follow-up, Project CHAT teens reported less marijuana use, lower perceived prevalence of marijuana use, fewer friends who used marijuana, and lower intentions to use marijuana in the next 6 months, as compared to teens assigned to usual care. Providing this type of brief intervention is a viable approach to working with high-risk teens to decrease substance use.

Brief Motivational Interviewing for Teens at Risk of Substance Use Consequences

School connectedness has a significant impact on adolescent outcomes, including reducing risk-taking behavior. This paper critically examines the literature on school-based programs targeting increased connectedness for reductions in risk taking. Fourteen articles describing seven different school-based programs were reviewed. Programs drew on a range of theories to increase school connectedness, and evaluations conducted for the majority of programs demonstrated positive changes in school connectedness, risk behavior, or a combination of the two. Many of the reviewed programs involved widespread school system change, however, which is frequently a complex and time-consuming task. Future research is needed to examine the extent of intervention complexity required to result in change. This review also showed a lack of consistency in the definitions and measurement of connectedness as well as few mediation analyses testing assumptions of impact on risk-taking behavior through increases in school connectedness. Additionally, this review revealed very limited evaluation of the elements of multicomponent programs that are most effective in increasing school connectedness and reducing adolescent risk taking.

School-Based Programs for Increasing Connectedness and Reducing Risk Behavior: A Systematic Review

Aims: Failure to complete high school predicts substantial economic and social disadvantage in adult life. The aim of this study was to determine the longitudinal association of mid‐adolescent polydrug use and high school non‐completion, relative to other drug use profiles. Design: A longitudinal analysis of the relationship between polydrug use in three cohorts at grade 9 (age 14–15 years) and school non‐completion (reported post‐high school). Setting: A State‐representative sample of students across Victoria, Australia. Participants: A total of 2287 secondary school students from 152 high schools. The retention rate was 85%. Measurements: The primary outcome was non‐completion of grade 12 (assessed at age 19–23 years). At grade 9, predictors included 30‐day use of eight drugs, school commitment, academic failure and peer drug use. Other controls included socio‐economic status, family relationship quality, depressive symptoms, gender, age and cohort. Findings: Three distinct classes of drug use were identified—no drug use (31.7%), mainly alcohol use (61.8%) and polydrug use (6.5%). Polydrug users were characterized by high rates of alcohol, tobacco and cannabis use. In the full model, mainly alcohol users and polydrug users were less likely to complete school than non‐drug users [odds ratio (OR) = 1.54, 95% confidence interval (CI) = 1.17–2.03) and OR = 2.51, 95% CI = 1.45–4.33), respectively, P < 0.001]. These effects were independent of school commitment, academic failure, peer drug use and other controls. Conclusions: Mid‐adolescent polydrug use in Australia predicts subsequent school non‐completion after accounting for a range of potential confounding factors. Adolescents who mainly consume alcohol are also at elevated risk of school non‐completion.

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A longitudinal study of the association of adolescent polydrug use, alcohol use, and high school non-completion

The extent to which students feel accepted, valued, respected and included in the school has recently surfaced as one of the most important predictors of adolescent mental health (particularly depressive symptoms). The school environment is an established predictor of school connectedness, but we set out to examine whether parental attachment predicts both adolescents' perception of the school environment and school connectedness. A study of 171 high school students from years 8 to 12 showed that parent attachment strongly predicted both. We also confirmed that the relationship between parent attachment and school connectedness is not a direct one but that parent attachment influences individual differences in the way adolescents perceive the school environment, which in turn influences school connectedness. This finding shows how multiple systems might be interlinked in influencing wellbeing in adolescents, and confirms the importance of intervening at the double level of both the family and the school system.

The Impact of Parental Attachment on Adolescent Perception of the School Environment and School Connectedness

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The impact of parental attachment on adolescent perception of the school environment and school connectedness.

Effects of globally obtained informative priors on bayesian safety performance functions developed for Australian crash data.

The state of the practice in black spot identification uses safety performance functions based on total crash counts to identify high-risk crash sites. This paper postulates that total crash count is a result of multiple distinct risk-generating processes (RGPs), including geometric characteristics of the road, spatial features of the surrounding environment, and driver behavior factors. However, these multiple sources are ignored in current modeling methodologies that try to explain or predict crash frequencies across sites. Instead, current practice uses models that imply that a single RGP exists. This misspecification may lead to correlation of crashes with incorrect sources of contributing factors (e.g., concluding a crash is predominately caused by a geometric feature when the cause is a behavioral issue), which may ultimately lead to inefficient use of public funds and misidentification of true black spots. This study proposes a latent class model consistent with a multiple risk process theory and investigates the influence this model has on correctly identifying crash black spots. The paper presents the theoretical and corresponding methodological approach in which a Bayesian latent class model is estimated with the assumption that crashes arise from two distinct RGPs, including engineering and unobserved spatial factors. The methodology was applied to state-controlled roads in Queensland, Australia. The results were compared with an empirical Bayesian negative binomial (EB-NB) model. A comparison of goodness-of-fit measures illustrated superiority of the proposed model compared with the NB model. The detection of black spots was improved compared with the EB-NB model. In addition, modeling crashes as the result of two fundamentally separate RGPs reveals more detailed information about unobserved crash causes.

Tanya Smyth

Papers

The Impact of Parental Attachment on Adolescent Perception of the School Environment and School Connectedness

The impact of parental attachment on adolescent perception of the school environment and school connectedness.

Effects of globally obtained informative priors on bayesian safety performance functions developed for Australian crash data.

Bayesian latent class safety performance function for identifying motor vehicle crash black spots

Consumer Perceptions of Medication Warnings About Driving: A Comparison of French and Australian Labels