Tracy Robinson | PubMed Health

PubMed2006

Exploration of self-identified education needs of alcohol and other drug workers.

OBJECTIVE: It is well established that people who work with clients who are dependent on substances need to be well equipped to deal with challenging and complex situations. Our research explores the experience of alcohol and other drug workers (AODW) in order to identify how successful Australia's teaching institutions are at preparing AODWs to meet this challenge. DESIGN: Interviews were conducted to ascertain the perceived gaps in existing training programs and the perceived training needs of AODW. Using a qualitative research approach in-depth interviews were conducted with more than 20 counsellors working in six AODW rehabilitation centres in regional New South Wales. RESULTS: There was a perception among all workers that their training had been inadequate and that the preparation for employment in AODW settings had not meet the felt needs of grass-roots workers leaving them to learn through experience. CONCLUSION: Considering the recommendations of both national and state policies on AODWs in Australia there is an urgent need to develop courses relevant to the needs of workers as suggested by respondents to this research.

Attitude of Health Personnel↗

PubMedDate not supplied

Predictive toxicology: benchmarking molecular descriptors and statistical methods.

The development of drugs depends on finding compounds that have beneficial effects with a minimum of toxic effects. The measurement of toxic effects is typically time-consuming and expensive, so there is a need to be able to predict toxic effects from the compound structure. Predicting toxic effects is expected to be challenging because there are usually multiple toxic mechanisms involved. In this paper, combinations of different chemical descriptors and popular statistical methods were applied to the problem of predictive toxicology. Four data sets were collected and cleaned, and four different sets of chemical descriptors were calculated for the compounds in each of the four data sets. Three statistical methods (recursive partitioning, neural networks, and partial least squares) were used to attempt to link chemical descriptors to the response. Good predictions were achieved in the two smaller data sets; we found for large data sets that the results were less effective, indicating that new chemical descriptors or statistical methods are needed. All of the methods and descriptors worked to a degree, but our work hints that certain descriptors work better with specific statistical methods so there is a need for better understanding and for continued methods development.

Drug Design↗