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University of Regina Institutional Repository

The mission of the oURspace digital repository is to share and preserve the scholarly, creative, and cultural work produced at the University of Regina.

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Recent Submissions

Access status: Open Access ,
PeerOnCall: Evaluating Implementation of App-Based Peer Support in Canadian Public Safety Organizations
(MDPI, 2025-08-13) Moll, Sandra, E.; Ricciardelli, Rosemary; Carleton, R. Nicholas; MacDermid, Joy, C.; Czarnuch, Stephen; MacPhee, Renée, S
Public safety personnel (PSP), including correctional workers, firefighters, paramedics, police, and public safety communicators, are at increased risk for posttraumatic stress injury, yet face barriers in receiving timely support. Mobile health (mHealth) applications (apps) offer promising avenues for confidential, on-demand access to relevant information and support. The purpose of this study was to assess implementation of PeerOnCall, a new mHealth platform designed by and for PSP (the platform includes two parallel apps: one for frontline workers and one for peer support providers). A multi-site mixed methods implementation trial was conducted over 3−6 months in 42 public safety organizations across Canada. App usage trends were tracked through software analytics, and facilitators and barriers to app use were explored via interviews with organizational champions. Over 11,300 employees across 42 organizations were invited to use the PeerOnCall app over the trial period, with approximately 1759 PSP (15% of total) downloading the app. Variation within and across sectors was evident in app downloads and feature use. Approaches to communication (mode, timing, and messenger), and organizational culture related to mental health and help outreach affected uptake levels. PeerOnCall is a promising tool to facilitate access to peer support; however, culturally relevant strategies are needed to overcome barriers and integrate this tool into workplace practices
Access status: Open Access ,
Evolution of COVID-19 municipal solid waste disposal behaviors using epidemiology-based periods defined by WHO guidelines
(Elsevier, 2022-09-28) Mahmud, Tanvir S.; Ng, Kelvin Tsun Wai; Karimi, Nima; Adusei, Kenneth K.; Pizzirani, Stefania
This study aims to identify the effects of continued COVID-19 transmission on waste management trends in a Canadian capital city, using pandemic periods defined from epidemiology and the WHO guidelines. Trends are detected using both regression and Mann-Kendall tests. The proposed analytical method is jurisdictionally comparable and does not rely on administrative measures. A reduction of 190.30 tonnes/week in average residential waste collection is observed in the Group II period. COVID-19 virulence negatively correlated with residential waste generation. Data variability in average collection rates during the Group II period increased (SD=228.73 tonnes/week). A slightly lower COVID-19 induced Waste Disposal Variability (CWDW) of 0.63 was observed in the Group II period. Increasing residential waste collection trends during Group II are observed from both regression (b = +1.6) and the MK test (z = +5.0). Both trend analyses reveal a decreasing CWDV trend during the Group I period, indicating higher diversion activities. Decreasing CWDV trends are also observed during the Group II period, probably due to the implementation of new waste programs. The use of pandemic periods derived from epidemiology helps us to better understand the effect of COVID-19 on waste generation and disposal behaviors, allowing us to better compare results in regions with different socio-economic affluences.
Access status: Open Access ,
Impacts of nested forward validation techniques on machine learning and regression waste disposal time series models
(Elsevier BV, 2022-11-05) Vu, Hoang Lan; Ng, Kelvin Tsun Wai; Richter, Amy; Li, Jianbing; Hosseinipooya, Seyed Ashkan
Dataset partitioning and validation techniques are required in all artificial neural network based waste models. However, there is currently no consensual approach on the validation techniques. This study examines the effects of three time series nested forward validation techniques (rolling origin - RO, rolling window - RW, and growing window - GW) on total municipal waste disposal estimates using recurrent neural network (RNN) models, and benchmarks model performance with respect to multiple linear regression (MLR) models. Validation selection techniques appear important to waste disposal time series model construction and evaluation. Sample size is found as an important factor on model accuracy for both RNN and MLR models. Better performance in Trial RW4 is observed, probably due to a more consistent testing set in 2019. Overall, the MAPE of the waste disposal models ranging from 10.4% to 12.7%. Both GW and RO validation techniques appear appropriate for RNN waste models. However, MLR waste models are more sensitive to the dataset characteristics, and RO validation technique appears more suitable to MLR models. It is found that data characteristics are more important than training period duration. It is recommended data set normality and skewness be examined for waste disposal modeling.
Access status: Open Access ,
Modeling of municipal waste disposal behaviors related to meteorological seasons using recurrent neural network LSTM models
(Elsevier BV, 2022-12) Adusei, Kenneth K.; Ng, Kelvin Tsun Wai; Karimi, Nima; Mahmud, Tanvir S.; Doolittle, Edward
The literature suggests that long short-term memory (LSTM) paired with recurrent neural network (RNN) can better express long- and short-term reliance of a data set. The study objectives are to quantify mixed waste disposal (MWD) behaviors at a Canadian landfill from 2013 to 2021, and develop separate RNN-LSTM models to predict MWD rates under four meteorological seasons. Seasonal variations are clearly presented in the historical disposal data, with higher MWD of 417.8 tonnes/month in summer and about 289.7 tonnes/month in winter. The variabilities of MWD are also different among the seasons. Winter experienced the least variation, probably due to similarities in inhabitants’ lifestyles. All seasonal sets are negatively skewed, and the highest skewness is observed in summer. The overall model performance using the entire data range is generally satisfactory, with R2 values between 0.72 ~ 0.86. Meteorological seasons appear to be a significant factor in waste disposal rate modeling. The model performances are less reliable for smaller disposal rates less than 200 tonnes/day, with 0.01 < R2 < 0.59. The results suggest the disposal behaviors on a quiet day can be quite different. The use of distinct time series related to seasons on MWD modeling is original. The proposed analytical approach provides an alternative waste modeling approach accounting for both short term (seasonal) and longer term (annual) effects.
Access status: Open Access ,
An evaluation of the temporal and spatial evolution of waste facilities using a simplified spatial distance analytical framework
(Elsevier BV, 2023-02-15) Ghosh, Abhijeet; Ng, Kelvin Tsun Wai; Karimi, Nima
This study proposed a simplified GIS-based decision support tool to examine the temporal and spatial evolution of waste facilities at a regional level. The key objective is to examine the geospatial distribution of landfills and transfer stations in Saskatchewan, Canada, from 2018 to 2020 based on changes in Euclidean distance computed by both the Central Feature (CF) and median center (MdC) spatial statistical tools. Both the CF and MdC results suggest that transfer stations in 2020 were located significantly closer to communities, and an improved level of landfill regionalization is observed. Smoother Landfill and Transfer Station radial curves are generally observed using the MdC tool. About 47.1% of the divisions are classified as challenging areas using the CF method, whereas only 41.1% of the divisions are classified as challenging areas using the MdC method. Six divisions (35.3%) are considered as appropriately managed by both CF and MdC methods. On the contrary, 23.5% of all divisions are suggested by both methods as challenging areas. Most divisions with an improving placement of waste facilities were located near the Canada-US border. The presences of major cities and total division population appear not key factors affecting the evolution of waste facility siting.