Saint Mary's University

About: Saint Mary's University is a education organization based out in Halifax, Nova Scotia, Canada. It is known for research contribution in the topics: Population & Stars. The organization has 1931 authors who have published 4993 publications receiving 143226 citations.

Community-based monitoring frameworks: increasing the effectiveness of environmental stewardship.

Abstract: This article presents an adaptable community-based monitoring (CBM) framework. The investigators used a well-tested conceptual CBM framework developed by the Canadian Community Monitoring Network (CCMN) as a basis from which to work. With the use of feedback from various types of CBM groups in the Province of Nova Scotia, Canada, obtained through surveys and interviews, the CCMN framework was modified into a document that attempts to address current disparities and inefficiencies within most CBM systems. The need for such a framework was underscored by the lack of stewardship groups’ use of standardized monitoring protocols and inability to effectively provide information to decision makers. From the information collected through the survey, it was concluded that the proposed framework must be a functional, multiparty form of CBM that addresses the key concerns of a standardized monitoring and communication program and must be able to be fed into the environmental-management system.

Multiple Dimensions of Organizational Identification and Commitment as Predictors of Turnover Intentions and Psychological Well-Being.

Abstract: Meme si on reconnait que l'identification et l'engagement sont intimement lies aux aspects de l'attachement psychologique d'un employe a l'organisation, il n'y a pas d'analyse qui fait le lien entre les dimensions multiples de chaque construit. Dans la presente etude, les modeles a trois composants de l'identification et de l'engagement organisationnels ont ete etudies en tant qu'indicateurs previsionnels des intentions de depart (roulement du personnel) et du bien-etre psychologique (estime de soi, satisfaction dans la vie et autoefficacite) parmi les employes (N = 60) d'une petite organisation. Les employes tres en vue avaient tendance a etre ceux qui etaient engages, mais differentes dimensions de chaque construit ont ete liees de facon specifique a divers criteres. Les composants affectifs de l'identification et de l'engagement ont ete associes de facon negative aux intentions de depart et l'affect du groupe interne positif (soit les sentiments derives d'etre un membre de l'organisation) predisait les perceptions d'autoefficacite. L'engagement a la continuation etait distinct des autres indicateurs previsionnels. Les resultats justifient les autres efforts pour integrer les perspectives de la theorie d'identite sociale et de la psychologie organisationnelle.

Getting better and staying better: Assessing civility, incivility, distress, and job attitudes one year after a civility intervention.

Abstract: Health care providers (n = 1,957) in Canada participated in a project to assess an intervention to enhance workplace civility. They completed surveys before the intervention, immediately after the intervention, and one year later. Results highlighted three patterns of change over the three assessments. These data were contrasted with data from control groups, which remained constant over the study period. For workplace civility, experienced supervisor incivility, and distress, the pattern followed an Augmentation Model for the intervention groups, in which improvements continued after the end of the intervention. For work attitudes, the pattern followed a Steady State Model for the intervention group, in that they sustained their gains during intervention but did not continue to improve. For absences, the pattern reflected a Lost Momentum Model in that the gains from preintervention to postintervention were lost, as absences returned to the preintervention level at follow-up. The results are discussed in reference to conceptual and applied issues in workplace civility.

Estimation of missing traffic counts using factor, genetic, neural and regression techniques

Abstract: Analyses from some of the highway agencies show that up to 50% permanent traffic counts (PTCs) have missing values It will be difficult to eliminate such a significant portion of data from traffic analysis Literature review indicates that the limited research uses factor or autoregressive integrated moving average (ARIMA) models for predicting missing values Factor-based models tend to be less accurate ARIMA models only use the historical data In this study, genetically designed neural network and regression models, factor models, and ARIMA models were developed It was found that genetically designed regression models based on data from before and after the failure had the most accurate results Average errors for refined models were lower than 1% and the 95th percentile errors were below 2% for counts with stable patterns Even for counts with relatively unstable patterns, average errors were lower than 3% in most cases