Research has shown that parents of children with autism report higher stress than parents of children with other developmental disabilities. It has been suggested that parent training programs, specifically naturalistic social-communication training, can reduce parental stress and enhance the quality of the parent-child relationship. Although the development of a multilevel assessment has been suggested, much of the research in this area has relied on measures of parent implementation fidelity and specific child target skills such as vocal communication, eye contact, and joint attention. Few have directly measured the parent-child interaction. The purpose of the current study is to examine the effects of an in-home parent training package for toddlers with autism on parent-child social interactions. Within this package, parents are taught to attend to contextual variables, to arrange the environment to set the occasion for child responding, to respond immediately to targeted child approximations, and to respond in ways that are mutually reinforcing, social, and fun. Data were collected during 5-min video-taped assessments, on the number of parent teaching episodes, child target skills (turn taking and social attending), engagement, and synchronous engagement. Results were evaluated in a multiple baseline design across two parent-child dyads and indicated increases in all measures. This study contributes to the current discussion on toddler parent-training programs and extends it in a way that highlights the benefits of using a multi-level assessment to measure the parent-child interaction.

Concrete has proven to be, by far, one of the most reliable materials for the construction of critical infrastructure. However, despite its structural capacity, concrete members are susceptible to damage mechanisms that may decrease its performance and durability throughout its service life. One such mechanism is alkali-silica reaction (ASR), which takes place when unstable siliceous phases present in coarse or fine aggregates react with the alkali hydroxides from the concrete pore solution, generating a secondary product (i.e., ASR gel); this product swells upon moisture uptake from the surrounding environment, leading to cracking and expansion of the affected concrete. In severe cases of ASR-affected infrastructure, structural safety could become a problem, and thus requiring the demolition of affected members. It is, therefore, necessary to adopt effective protocols for the diagnosis and prognosis of aging infrastructure, to ensure its performance over time along with properly planning for rehabilitation strategies, whether required. This work presents a two-stage case study of the S.I.T.E. building at the University of Ottawa for the diagnosis and prognosis of ASR-affected members (i.e., columns) after nearly 20 years in service. The diagnosis phase was conducted with the aim of evaluating the cause and extent of distress and interpreting its impact on the performance of the affected structure. First, a visual inspection was conducted to evaluate potentially damaged members, in order to select the best location for core-drilling. Once ASR was confirmed through petrographic examination, specimens were evaluated through the multi-level assessment (i.e., coupling of microscopic and mechanical assessment). A range of damage was discovered among the examined columns (i.e., 0.03%, 0.05%, and 0.08% expansion). Moreover, evidence of developing freeze and thaw (FT) damage was discovered in columns with greater levels of expansion, raising future concerns regarding the durability and serviceability of members affected by this coupling of damage (i.e., ASR+FT). For the second stage of this project (i.e., prognosis), a novel ASR semi-empirical model was developed with the aim of predicting future ASR-induced expansion and damage in the S.I.T.E. building. The above model was developed and validated (using ASR exposure site data) through the coupling of existing chemo-mechanical macro-models, which were used to predict material behaviour on the structural scale, and novel mathematical relationships for the prediction of anisotropy in the columns. Moreover, the use of the multi-level assessment to predict the mechanical implications of predicted distress was found to enhance the model’s capacity for prognosis and demonstrated important potential for the accurate prediction of multi-level damage in the S.I.T.E. columns.