Dynamic and diverse ways of knowing in mental health occupational therapy

This study focused on evidence based practice in mental health occupational therapy. It investigated the theoretical, evidentiary and practical knowledge that occupational therapists use to support consumers and understand the relationship between occupation and health.

Taking the sting out of darting : Risks, restraint drugs and procedures for the chemical restraint of Southern Hemisphere otariids

The need to manage otariid populations has necessitated the development of a wide range of capture methods. Chemical restraint by remote drug delivery (i.e., darting) is a highly selective method that can be used to facilitate otariid capture in a range of scenarios, when other methods may be impracticable. However, the risks associated with darting otariids are not widely known and guidelines necessary to promote and refine best practice do not exist. We review the risks associated with darting and in light of our findings, develop darting guidelines to help practitioners assess and minimize risks during capture, anesthesia and recovery. Published studies reveal that mortalities associated with darting predominantly result from complications during anesthetic maintenance (e.g., prolonged respiratory depression, apnea, or hyperthermia), rather than from complications during capture or recovery. In addition to monitoring vital signs and proper intervention, the risk of irreversible complications during anesthesia can be reduced by administering drug doses that are sufficient to enable the capture and masking of animals, after which anesthetic depth can be regulated using gas anesthesia.

Predictors and risks of body fat profiles in young New Zealand European, Māori and Pacific women: study protocol for the women's EXPLORE study.

Body mass index (BMI) (kg/m(2)) is used internationally to assess body mass or adiposity. However, BMI does not discriminate body fat content or distribution and may vary among ethnicities. Many women with normal BMI are considered healthy, but may have an unidentified "hidden fat" profile associated with higher metabolic disease risk. If only BMI is used to indicate healthy body size, it may fail to predict underlying risks of diseases of lifestyle among population subgroups with normal BMI and different adiposity levels or distributions. Higher body fat levels are often attributed to excessive dietary intake and/or inadequate physical activity. These environmental influences regulate genes and proteins that alter energy expenditure/storage. Micro ribonucleic acid (miRNAs) can influence these genes and proteins, are sensitive to diet and exercise and may influence the varied metabolic responses observed between individuals. The study aims are to investigate associations between different body fat profiles and metabolic disease risk; dietary and physical activity patterns as predictors of body fat profiles; and whether these risk factors are associated with the expression of microRNAs related to energy expenditure or fat storage in young New Zealand women. Given the rising prevalence of obesity globally, this research will address a unique gap of knowledge in obesity research.

Principals and legal risks: contrasting perspectives

There has been an increasing awareness across many jurisdictions of potential legal issues that might arise in schools. These issues range from bullying to sexual misconduct, from injury to negligence. In a recent study in Singapore, despite the increased attention to such issues, school principals displayed a range of attitudes toward legal risk and a diverse range of strategies to minimise it. The findings were compared to those from a small scale study of senior educators in Australia in order to ascertain commonalities and differences of view. This paper summarises those views and suggests some basic principles to help those in positions of leadership to avert unwanted legal attention.

Characterization and prediction of issue-related risks in software projects

Identifying risks relevant to a software project and planning measures to deal with them are critical to the success of the project. Current practices in risk assessment mostly rely on high-level, generic guidance or the subjective judgements of experts. In this paper, we propose a novel approach to risk assessment using historical data associated with a software project. Specifically, our approach identifies patterns of past events that caused project delays, and uses this knowledge to identify risks in the current state of the project. A set of risk factors characterizing “risky” software tasks (in the form of issues) were extracted from five open source projects: Apache, Duraspace, JBoss, Moodle, and Spring. In addition, we performed feature selection using a sparse logistic regression model to select risk factors with good discriminative power. Based on these risk factors, we built predictive models to predict if an issue will cause a project delay. Our predictive models are able to predict both the risk impact (i.e. the extend of the delay) and the likelihood of a risk occurring. The evaluation results demonstrate the effectiveness of our predictive models, achieving on average 48%-81% precision, 23%-90% recall, 29%-71% F-measure, and 70%-92% Area Under the ROC Curve. Our predictive models also have low error rates: 0.39-0.75 for Macro-averaged Mean Cost-Error and 0.7-1.2 for Macro-averaged Mean Absolute Error.