Evaluation of a culturally sensitive social and emotional well-being program for Aboriginal and Torres Strait Islanders.

OBJECTIVE: To evaluate Deadly Thinking, a social and emotional well-being promotion program targeted to remote and rural Aboriginal and Torres Strait Islander communities. Deadly Thinking aims to improve emotional health literacy, psychological well-being and attitudes towards associated help-seeking. DESIGN: Participants completed pre/post-test evaluations via a brief self-report survey immediately before and after the Deadly Thinking workshop. SETTING: Aboriginal and Torres Strait Islander communities in rural and regional Australia. PARTICIPANTS: Data were obtained from 413 participants (69.8% female, mean age 41.6 years), of whom 70.4% identified as Aboriginal or Torres Strait Islanders. INTERVENTION: Deadly Thinking workshops involve participant's engaging with a series of videos and facilitated group discussions with other participants related to social and emotional well-being topics relevant to individuals and communities. MAIN OUTCOME MEASURES: Participants completed measures of psychological distress, suicidal ideation, substance use, changes in attitudes towards help-seeking and help-seeking intentions and satisfaction with the workshop. Additionally, participants in a train-the-trainer workshop rated their perceived confidence to deliver the program post-workshop. RESULT: Participants reported positive perceptions of community safety and well-being and low rates of marked distress, with no significant difference between train-the-trainer and community workshop participants. Results indicated significant improvement in help-seeking intentions post-workshop and high rates of satisfaction with workshop components. CONCLUSION: Initial evaluation indicates good acceptability and feasibility of delivering the Deadly Thinking program in rural and remote Indigenous communities; however, more robust evaluation of the program is warranted using controlled conditions to measure effectiveness, particularly for changing in help-seeking behaviour.

Assessing microbial risk through event-based pathogen loading and hydrodynamic modelling.

The aim of this study was to assess the variability of microbial risk associated with drinking water under various contaminant loading conditions in a drinking water source. For this purpose, a probabilistic-deterministic approach was applied to estimate the loadings of Cryptosporidium, Giardia, and Escherichia coli (E. coli) from fecal contamination sources during both dry and wet weather conditions. The relative importance of loads originating from various fecal contamination sources was also determined by a probabilistic approach. This study demonstrates that water resource recovery facilities were the dominant source of Giardia, yet rivers were more important with regards to Cryptosporidium. Estimated loadings were used as input to a three-dimensional hydrodynamic model of Lake Ontario; the fate and transport of microbial organisms were simulated at the influent of a drinking water intake. Discharge-based hydrodynamic modelling results were compared to observed concentrations. Simulated probability distributions of concentrations at the intake were used as an input to a quantitative microbial risk assessment (QMRA) model such that the variability of microbial risk in the context of drinking water could be examined. Depending on wind and currents, higher levels of fecal contamination reached the intake during wet weather loading scenarios. Probability distribution functions of Cryptosporidium, Giardia and E. coli concentrations at the intake were significantly higher during wet weather conditions when compared to dry conditions (p < 0.05). For all contaminants studied, the QMRA model showed a higher risk during wet weather (over 1 order of magnitude) compared to dry weather conditions. When considering sewage by-pass scenarios, risks remained below 2.7 × 10-7 person-1 day-1 for Giardia and E. coli O157:H7. Limited data were available for Cryptosporidium in by-pass effluents and the risk is unknown; hence it is critical to obtain reliable loading data for the riskiest scenarios, such as those associated with water resource recovery facility by-passes.