Improving safety of nursing staff: challenges and solutions regarding safety for nursing staff in acute care psychiatric settings

Workplace violence is defined as an act of abuse, threatening behaviour, intimidation, or assault on a person in his or her place of employment. Unfortunately, such violence is a reality for nurses. These take the form of physical, verbal, and threating behaviours, and harassment. Violence, particularly verbal abuse, is so prevalent that it is often considered “part of the job” and can contribute to many negative professional and personal effects for nurses. Therefore, it is important to understand what influences an individual to become violent in order to suggest and support initiatives to decrease it. A literature review and consultations with key stakeholders were conducted to gather relevant information regarding violence committed by patients and others visiting mental health care settings. Through data analysis, two relevant themes were revealed: reporting and interventions. Reporting incidents of workplace violence is important to track and quantify aggressive episodes, thus emphasizing its seriousness. Nurses may differ in the perception of what constitutes violence, underreport incidents, and feel a sense of futility when reported violence continues. In addition, cumbersome methods of reporting can be a hindrance to the reporting process. Six areas of potential interventions were identified to increase safety for nurses. These are staffing, de-escalation training, environmental considerations, addictions services, organizational support, and consequences. All findings were summarized in a document to be presented to the leadership of the Mental Health and Addictions program within the local health care authority. The goal is to offer recommendations to lead to a decrease in workplace aggression and increased safety for nurses in the acute care psychiatric setting.

Industrial fault detection and diagnosis using Bayesian belief network

Rapid development in industry have contributed to more complex systems that are prone to failure. In applications where the presence of faults may lead to premature failure, fault detection and diagnostics tools are often implemented. The goal of this research is to improve the diagnostic ability of existing FDD methods. Kernel Principal Component Analysis has good fault detection capability, however it can only detect the fault and identify few variables that have contribution on occurrence of fault and thus not precise in diagnosing. Hence, KPCA was used to detect abnormal events and the most contributed variables were taken out for more analysis in diagnosis phase. The diagnosis phase was done in both qualitative and quantitative manner. In qualitative mode, a networked-base causality analysis method was developed to show the causal effect between the most contributing variables in occurrence of the fault. In order to have more quantitative diagnosis, a Bayesian network was constructed to analyze the problem in probabilistic perspective.

Second-order cyclostationarity-based detection and classification of LTE SC-FDMA signals for cognitive radio

Cognitive radio (CR) was developed for utilizing the spectrum bands efficiently. Spectrum sensing and awareness represent main tasks of a CR, providing the possibility of exploiting the unused bands. In this thesis, we investigate the detection and classification of Long Term Evolution (LTE) single carrier-frequency division multiple access (SC-FDMA) signals, which are used in uplink LTE, with applications to cognitive radio. We explore the second-order cyclostationarity of the LTE SC-FDMA signals, and apply results obtained for the cyclic autocorrelation function to signal detection and classification (in other words, to spectrum sensing and awareness). The proposed detection and classification algorithms provide a very good performance under various channel conditions, with a short observation time and at low signal-to-noise ratios, with reduced complexity. The validity of the proposed algorithms is verified using signals generated and acquired by laboratory instrumentation, and the experimental results show a good match with computer simulation results.

Lie detection and children: Impact of the mode of presentation

Although a great deal of research has examined lie-detection among adults, little research has examined the differences between audio and visual mediums for deception among children. In the current study participants were presented (n = 42) with recordings of four children, each describing his/her experience of getting glasses. Two of the accounts were truthful, two were fabricated. Half of the participants were presented with videos, half were presented with audio-recordings. Following the presentation of each recording, participants responded to questions regarding the truthfulness of each child’s account. Results showed that when evaluating truth-tellers, participants’ lie-detection accuracy was significantly greater than chance. Within the video condition, non-parents were shown to report significantly more lie-related cues than parents. Several deception cues were shown to be related to lie-detection accuracy.