This study explored mindfulness in eight self–injuring psychiatric adolescents. A concurrent triangulation mixed–method design was used. In–depth semi–structured clinical interviews and clinical records constituted the qualitative data, while quantitative data was gathered using the Five–Facet Mindfulness Questionnaire (FFMQ). Thematic analysis was used to generate themes and subthemes for both selfinjury and mindfulness. Findings regarding self–injury were in line with the literature. In terms of mindfulness, two groups emerged: one sometimes acting mindfully and the other often acting mindfully. Both similarities and differences were found between the groups. Similarities in self–injuring behaviour can be explained by their similar scores on Observe and Nonreact, while the differences can be explained by the differences in their scores on Describe, Act with Awareness and Nonjudge. Those who sometimes act mindfully tend to be more self–critical, report more severe and lethal self–injuring episodes and more often use self–injury for self–punishment. Although those who often act mindfully self–injure more often, they show more selfcompassion and report less severe injuries and less lethal methods. Future research should explore the usefulness of mindfulness–based interventions, especially teaching Nonjudge and Nonreact skills, to not only increase mindfulness, but to decrease selfinjuring behaviour. Seeing that this is an exploratory study on a small sample, the results presented here should be considered to be preliminary until replicated with a larger clinical sample. / Thesis (M.A. (Clinical Psychology))--North-West University, Potchefstroom Campus, 2011.

This study explored mindfulness in eight self–injuring psychiatric adolescents. A concurrent triangulation mixed–method design was used. In–depth semi–structured clinical interviews and clinical records constituted the qualitative data, while quantitative data was gathered using the Five–Facet Mindfulness Questionnaire (FFMQ). Thematic analysis was used to generate themes and subthemes for both selfinjury and mindfulness. Findings regarding self–injury were in line with the literature. In terms of mindfulness, two groups emerged: one sometimes acting mindfully and the other often acting mindfully. Both similarities and differences were found between the groups. Similarities in self–injuring behaviour can be explained by their similar scores on Observe and Nonreact, while the differences can be explained by the differences in their scores on Describe, Act with Awareness and Nonjudge. Those who sometimes act mindfully tend to be more self–critical, report more severe and lethal self–injuring episodes and more often use self–injury for self–punishment. Although those who often act mindfully self–injure more often, they show more selfcompassion and report less severe injuries and less lethal methods. Future research should explore the usefulness of mindfulness–based interventions, especially teaching Nonjudge and Nonreact skills, to not only increase mindfulness, but to decrease selfinjuring behaviour. Seeing that this is an exploratory study on a small sample, the results presented here should be considered to be preliminary until replicated with a larger clinical sample. / Thesis (M.A. (Clinical Psychology))--North-West University, Potchefstroom Campus, 2011.

<p dir="ltr">Automaticity, an essential attribute of skill, is developed when a task is executed repeatedly with minimal attention and can have both good (e.g., productivity, skill acquisitions) and bad (e.g., accident involvement) implications on workers’ performance. However, the implications of automaticity in construction are unknown despite their significance. To address this knowledge gap, this research aimed to examine methods that are indicative of the development of automaticity on construction sites and its implications on construction safety and productivity. The objectives of the dissertation include: 1) examining the development of automaticity during the repetitive execution of a primary task of roofing construction and a concurrent secondary task (a computer-generated audio-spatial processing task) to measure attentional resources; 2) using eye-tracking metrics to distinguish between automatic and nonautomatic subjects and determine the significant factors contributing to the odds of automatic behavior; 3) determining which personal characteristics (such as personality traits and mindfulness dimensions) better explain the variability in the attention of workers while developing automaticity. To achieve this objective, 28 subjects were recruited to take part in a longitudinal study involving a total of 22 repetitive sessions of a simulated roofing task. The task involves the installation of 17 pieces of 25 ft2 shingles on a low-sloped roof model that was 8 ft wide, 8 ft long, and 4 ft high for one month in a laboratory. The collected data was analyzed using multiple statistical and data mining techniques such as repeated measures analysis of variance (RM-ANOVA), pairwise comparisons, principal component analysis (PCA), support vector machine (SVM), binary logistic regression (BLR), relative weight analyses (RWA), and advanced bootstrapping techniques to address the research questions. First, the findings showed that as the experiment progressed, there were significant improvements in the mean automatic performance measures such as the mean primary task duration, mean primary task accuracy, and mean secondary task score over the repeated measurements (p-value < 0.05). These findings were used to demonstrate that automaticity develops during repetitive construction activities. This is because these automatic performance measures provide an index for assessing feature-based changes that are synonymous with automaticity development. Second, this study successfully used supervised machine learning methods including SVM to classify subjects (with an accuracy of 76.8%) based on their eye-tracking data into automatic and nonautomatic states. Also, BLR was used to estimate the probability of exhibiting automaticity based on eye-tracking metrics and ascertain the variables significantly contributing to it. Eye-tracking variables collected towards safety harness and anchor, hammer, and work area AOIs were found to be significant predictors (p < 0.05) of the probability of exhibiting automatic behavior. Third, the results revealed that higher levels of agreeableness significantly impact increased levels of change in attention to productivity-related cues during automatic behavior. Additionally, higher levels of nonreactivity to inner experience significantly reduce the changes in attention to safety-related AOIs while developing automaticity. The findings of this study provide metrics to assess training effectiveness. The findings of this study can be used by practitioners to better understand the positive and negative consequences of developing automaticity, measure workers’ performance more accurately, assess training effectiveness, and personalize learning for workers. In long term, the findings of this study will also aid in improving human-AI teaming since the AI will be better able to understand the cognitive state of its human counterpart and can more precisely adapt to him or her.</p>