The relationship between cannabis outcome expectancies and cannabis refusal self-efficacy in a treatment population

Background and aims Self-efficacy beliefs and outcome expectancies are central to Social Cognitive Theory (SCT). Alcohol studies demonstrate the theoretical and clinical utility of applying both SCT constructs. This study examined the relationship between refusal self-efficacy and outcome expectancies in a sample of cannabis users, and tested formal mediational models. Design Patients referred for cannabis treatment completed a comprehensive clinical assessment, including recently validated cannabis expectancy and refusal self-efficacy scales. Setting A hospital alcohol and drug out-patient clinic. Participants Patients referred for a cannabis treatment [n = 1115, mean age 26.29, standard deviation (SD) 9.39]. Measurements The Cannabis Expectancy Questionnaire (CEQ) and Cannabis Refusal Self-Efficacy Questionnaire (CRSEQ) were completed, along with measures of cannabis severity [Severity of Dependence Scale (SDS)] and cannabis consumption. Findings Positive (β = −0.29, P < 0.001) and negative (β = −0.19, P < 0.001) cannabis outcome expectancies were associated significantly with refusal self-efficacy. Refusal self-efficacy, in turn, fully mediated the association between negative expectancy and weekly consumption [95% confidence interval (CI) = 0.03, 0.17] and partially mediated the effect of positive expectancy on weekly consumption (95% CI = 0.06, 0.17). Conclusions Consistent with Social Cognitive Theory, refusal self-efficacy (a person's belief that he or she can abstain from cannabis use) mediates part of the association between cannabis outcome expectancies (perceived consequences of cannabis use) and cannabis use.

Testing the effectiveness of a self-efficacy based exercise intervention for adults with venous leg ulcers : protocol of a randomised controlled trial

Background: Exercise and adequate self-management capacity may be important strategies in the management of venous leg ulcers. However, it remains unclear if exercise improves the healing rates of venous leg ulcers and if a self-management exercise program based on self-efficacy theory is well adhered to. Method/Design: This is a randomised controlled in adults with venous leg ulcers to determine the effectiveness of a self-efficacy based exercise intervention. Participants with venous leg ulcers are recruited from 3 clinical sites in Australia. After collection of baseline data, participants are randomised to either an intervention group or control group. The control group receive usual care, as recommended by evidence based guidelines. The intervention group receive an individualised program of calf muscle exercises and walking. The twelve week exercise program integrates multiple elements, including up to six telephone delivered behavioural coaching and goal setting sessions, supported by written materials, a pedometer and two follow-up booster calls if required. Participants are encouraged to seek social support among their friends, self-monitor their weekly steps and lower limb exercises. The control group are supported by a generic information sheet that the intervention group also receive encouraging lower limb exercises, a pedometer for self-management and phone calls at the same time points as the intervention group. The primary outcome is the healing rates of venous leg ulcers which are assessed at fortnightly clinic appointments. Secondary outcomes, assessed at baseline and 12 weeks: functional ability (range of ankle motion and Tinetti gait and balance score), quality of life and self-management scores. Discussion: This study seeks to address a significant gap in current wound management practice by providing evidence for the effectiveness of a home-based exercise program for adults with venous leg ulcers. Theory-driven, evidence-based strategies that can improve an individual’s exercise self-efficacy and self-management capacity could have a significant impact in improving the management of people with venous leg ulcers. Information gained from this study will provide much needed information on management of this chronic disease to promote health and independence in this population. Trial registration: Australian New Zealand Clinical Trials Registry ACTRN12612000475842 Trial status: Current follow up

The effect of a urinary incontinence self-management program for older women in South Korea: A pilot study

© 2015 Chinese Nursing Association.Background Although self-management approaches have shown strong evidence of positive outcomes for urinary incontinence prevention and management, few programs have been developed for Korean rural communities. Objectives This pilot study aimed to develop, implement, and evaluate a urinary incontinence self-management program for community-dwelling women aged 55 and older with urinary incontinence in rural South Korea. Methods This study used a one-group pre- post-test design to measure the effects of the intervention using standardized urinary incontinence symptom, knowledge, and attitude measures. Seventeen community-dwelling older women completed weekly 90-min group sessions for 5 weeks. Descriptive statistics and paired t-tests and were used to analyze data. Results The mean of the overall interference on daily life from urine leakage (pre-test: M = 5.76 ± 2.68, post-test: M = 2.29 ± 1.93, t = -4.609, p < 0.001) and the sum of International Consultation on Incontinence Questionnaire scores (pre-test: M = 11.59 ± 3.00, post-test: M = 5.29 ± 3.02, t = -5.881, p < 0.001) indicated significant improvement after the intervention. Improvement was also noted on the mean knowledge (pre-test: M = 19.07 ± 3.34, post-test: M = 23.15 ± 2.60, t = 7.550, p < 0.001) and attitude scores (pre-test: M = 2.64 ± 0.19, post-test: M = 3.08 ± 0.41, t = 5.150, p < 0.001). Weekly assignments were completed 82.4% of the time. Participants showed a high satisfaction level (M = 26.82 ± 1.74, range 22-28) with the group program. Conclusions Implementation of a urinary incontinence self-management program was accompanied by improved outcomes for Korean older women living in rural communities who have scarce resources for urinary incontinence management and treatment. Urinary incontinence self-management education approaches have potential for widespread implementation in nursing practice.

Effects of Web-Based Self-Reporting: College Students’ Self-Efficacy Regarding Fruit and Vegetable Intake

This study evaluated the effect of an online diet-tracking tool on college students’ self-efficacy regarding fruit and vegetable intake. A convenience sample of students completed online self-efficacy surveys before and after a six-week intervention in which they tracked dietary intake with an online tool. Group one (n=22 fall, n=43 spring) accessed a tracking tool without nutrition tips; group two (n=20 fall, n=33 spring) accessed the tool and weekly nutrition tips. The control group (n=36 fall, n=60 spring) had access to neither. Each semester there were significant changes in self-efficacy from pre- to post-test for men and for women when experimental groups were combined (p<0.05 for all); however, these changes were inconsistent. Qualitative data showed that participants responded well to the simplicity of the tool, the immediacy of feedback, and the customized database containing foods available on campus. Future models should improve user engagement by increasing convenience, potentially by automation.

Flexible self-adjustment of task deployment in dynamic environments

This paper presents an investigation into dynamic self-adjustment of task deployment and other aspects of self-management, through the embedding of multiple policies. Non-dedicated loosely-coupled computing environments, such as clusters and grids are increasingly popular platforms for parallel processing. These abundant systems are highly dynamic environments in which many sources of variability affect the run-time efficiency of tasks. The dynamism is exacerbated by the incorporation of mobile devices and wireless communication. This paper proposes an adaptive strategy for the flexible run-time deployment of tasks; to continuously maintain efficiency despite the environmental variability. The strategy centres on policy-based scheduling which is informed by contextual and environmental inputs such as variance in the round-trip communication time between a client and its workers and the effective processing performance of each worker. A self-management framework has been implemented for evaluation purposes. The framework integrates several policy-controlled, adaptive services with the application code, enabling the run-time behaviour to be adapted to contextual and environmental conditions. Using this framework, an exemplar self-managing parallel application is implemented and used to investigate the extent of the benefits of the strategy

A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

Real-time scheduling in multicore : time- and space-partitioned architectures

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2014

Case-based reasoning for self-optimizing behavior

In this paper we present a Self-Optimizing module, inspired on Autonomic Computing, acquiring a scheduling system with the ability to automatically select a Meta-heuristic to use in the optimization process, so as its parameterization. Case-based Reasoning was used so the system may be able of learning from the acquired experience, in the resolution of similar problems. From the obtained results we conclude about the benefit of its use.

Meta-heuristics tunning using CBR for dynamic scheduling

This paper addresses the problem of Biological Inspired Optimization Techniques (BIT) parameterization, considering the importance of this issue in the design of BIT especially when considering real world situations, subject to external perturbations. A learning module with the objective to permit a Multi-Agent Scheduling System to automatically select a Meta-heuristic and its parameterization to use in the optimization process is proposed. For the learning process, Casebased Reasoning was used, allowing the system to learn from experience, in the resolution of similar problems. Analyzing the obtained results we conclude about the advantages of its use.

Self-optimizing through CBR learning

In this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling

The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Evaluating three self-help smoking cessation interventions for post-secondary smokers : a randomized controlled trial on six Ontario campuses

Objective. Smoking prevalence is highest among the young adult cohort. Postsecondary students are no exception. Although many students intend to quit smoking, no research has established what methods best promote reductions in, or complete abstinence from smoking. This randomized controlled trial examined the effectiveness of three self-help smoking cessation interventions. Method. On six post-secondary campuses, 483 smokers who voluntarily accessed Leave The Pack Behind (a tobacco control initiative) were randomly assigned to one of three smoking cessation interventions: One Step At A Time (a 2-booklet, *gold standard' program for adults); Smoke|Quit (a newly-developed 2-booklet program for young adult students); and usual care (a 'Quit Kit' containing a booklet on stress management, information about pharmacological quitting aides and novelty items). All participants also received one proactive telephone support call from a peer counsellor. During the study, 85 participants withdrew. The final sample of 216 students who completed baseline questionnaires and 12-week follow-up telephone interviews was representative of the initial sample in terms of demographic characteristics, and smokingquitting- related variables. Results. Whether participants quit smoking depended upon treatment condition, ^(2, N=2\6) = 6.34, p = .04, with Smoke|Quit producing more successfijl quitters (18.4%) than One Step At A Time (4.5%) or the Quit Kit (1 1.4%). On average, participants had quit 53.46 days, with no significant difference across treatments. Selfefficacy also increased. Use of the intervention or other quitting aides was not associated with treatment condition. Among the 191 participants who did not quit smoking, treatment condition did not influence outcomes. Overall, 46.2% had made a quit attempt. Significant decreases in weekly tobacco consumption and increases in self-efficacy to resist smoking were observed from baseline to follow-up. Conclusion. Post-secondary institutions represent a potentially final opportunity for age-targeted interventions. Self-help resources tailored to students' social and contextual characteristics will have considerable more impact than stage-only tailored interventions. Both reduction and abstinence outcomes should be emphasized to positively support students to stop smoking.

Evaluation of self-esteem before and after preceptorship experience for student nurses

seventy-eight diploma nursing students participated (from a class of 112 students) in completing the Coopersmith Self-Esteem Inventory administered by mailed questionnaire before and at the end of the preceptorship. Also a rating form was completed by 70 preceptors to determine how the observed level of self-confidence compared to self-reported self-esteem at the end of the preceptorship program. As well, four preceptors and five preceptees completed weekly diaries and six preceptors and six preceptees participated in weekly phone interviews with the investigator. Overall, self-esteem went up after the preceptorship. A comparison was made between the pretest and posttest using the t-test (dependent paired samples). Significant difference (p=.05) was demonstrated. Self-confidence ratings by preceptors were inaccurate as they had no relation to the self-reported self-esteem level of students. The diaries and interviews of preceptors and preceptees were a rich source of data as well.