Class Cluster Research Articles

Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle–bicycle crashes

Introduction: In recent years, Australia is seeing an increase in the total number of cyclists. However, the rise of serious injuries and fatalities to cyclists has been a major concern. Understanding the factors affecting the fatalities and injuries of bicyclists in crashes with motor vehicles is important to develop effective policy measures aimed at improving the safety of bicyclists. This study aims to identify the factors affecting motor vehicle-bicycle (MVB) crashes in Victoria, Australia and introducing effective countermeasures for the identified risk factors. Method: A data set of 14,759 MVB crash records from Victoria, Australia between 2006 and 2019 was analyzed using the binary logit model and latent class clustering. Results: It was observed that the factors that increase the risk of fatalities and serious injuries of bicyclists (FSI) in all clusters are: elderly bicyclist, not using a helmet, and darkness condition. Likewise, in areas with no traffic control, clear weather, and dry surface condition (cluster 1), high speed limits increase the risk of FSI, but the occurrence of MVB crashes in cross intersection and T-intersection has been significantly associated with a reduction in the risk of FSI. In areas with traffic control and unfavorable weather conditions (cluster 2), wet road surface increases the risk of FSI, but the areas with give way sign and pedestrian crossing signs reduce the risk of FSI. Practical Applications: Recommendations to reduce the risk of fatalities or serious injury to bicyclists are: improvement of road lighting and more exposure of bicyclists using reflective clothing and reflectors, separation of the bicycle and vehicle path in mid blocks especially in high-speed areas, using a more stable bicycle for the older people, monitoring helmet use, improving autonomous emergency braking, and using bicyclist detection technology for vehicles.

Injury severity analysis of motorcycle crashes: A comparison of latent class clustering and latent segmentation based models with unobserved heterogeneity

The latent class clustering and latent segmentation-based models are employed to account for heterogeneity across different groups. Further, the random parameter variants of these modeling frameworks are employed to consider heterogeneity within the group. Both of these approaches have recently gained significant attention in road safety literature. However, the similarities and differences between these two methods are seldom explained and investigated. To that end, this study proposes to compare the performance of latent class clustering and latent segmentation-based random parameter models in examining crash injury severity outcomes. These models have been developed based on an ordered logit modeling framework to accommodate the ordinal nature of injury severity levels. For examining crash injury severity outcomes, this is the first study to consider the random parameter variant of ordered modeling structure within a latent segmentation modeling scheme. The current study also tests for and incorporates temporal instability of exogenous variables across multiple years of crash data in examining injury severity outcomes. The models have been estimated by using motorcycle crash data of Queensland, Australia, from the year 2012 through 2016. The comparison exercise is also augmented by estimating aggregate level elasticity effects of exogenous variables. The comparison exercise highlights the superiority of the latent segmentation approach in examining injury severity compared to the latent class clustering-based modeling approach. Moreover, the random parameter variants of both frameworks performed better than their fixed-parameter counterparts, which highlights the need to account for both across- and within-group heterogeneity. The temporal stability tests indicate that the effects of exogenous variables on the rider injury severity are different across year-wise models.

Depression and Deliberate Self-Harm Among Rural Adolescents of Sichuan Province in Western China: A 2-Year Longitudinal Study.

Objective: To explore the change in the prevalence and association of depression and deliberate self-harm and their common and independent influencing factors among western Chinese rural adolescents.Methods: A total of 2,744 junior and senior high school students from two rural schools in Sichuan Province, China, participated in the baseline survey and were invited to participate in two follow-up surveys. The Center for Epidemiologic Studies-Depression Scale, a deliberate self-harm item, the Social Support Rating Scale, the Rosenberg Self-esteem Scale and the Connor-Davidson Resilience Scale were administered. A bivariate four-level logistic regression model was used for analysis.Results: The prevalence of depression and deliberate self-harm were 39.6 and 21.2%, respectively. Regular physical exercise, a good relationship with parents, high resilience, and high self-esteem were common protective factors for both depression and deliberate self-harm. Feeling disliked by teachers was a common risk factor for both. Being female, having a mother who emigrated as a migrant worker before the student was 3 years old, feeling disliked by classmates and having a poor family economic status were associated only with an increased risk of depression. Participants with medium social support were less likely to report deliberate self-harm than those with low or high support. Depression and deliberate self-harm were clustered at the class level.Conclusions: The comorbidity of depression and deliberate self-harm in rural adolescents should be given ample attention. Interventions should consider the class clustering of depression and deliberate self-harm and their common and unique influencing factors.

An Empirical Study of Adversarial Examples on Remote Sensing Image Scene Classification

Deep neural networks (DNNs), which learn a hierarchical representation of features, have shown remarkable performance in big data analytics of remote sensing. However, previous research indicates that DNNs are easily spoofed by adversarial examples, which are crafted images with artificial perturbations that fool DNN models toward wrong predictions. To comprehensively evaluate the impact of adversarial examples on the remote sensing image (RSI) scene classification, this study tests eight state-of-the-art classification DNNs on six RSI benchmarks. These data sets include both optical and synthetic-aperture radar (SAR) images of different spectral and spatial resolutions. In the experiment, we create 48 classification scenarios and use four cutting-edge attack algorithms to investigate the influence of the adversarial example on the classification of RSIs. The experimental result shows that the fooling rates of the attacks are all over 98% across the 48 scenarios. We also find that, for the optical data, the seriousness of the adversarial problem has a negative relationship with the richness of the feature information. Besides, adversarial examples generated from SAR images are used easily for fooling the models with an average fooling rate of 76.01%. By analyzing the class distribution of these adversarial examples, we find that the distribution of the misclassifications is not affected by the types of models and attack algorithms-adversarial examples of RSIs of the same class cluster on fixed several classes. The analysis of classes of adversarial examples not only helps us explore the relationships between data set classes but also provides insights for further designing defensive algorithms.

사회계층에 따른 군집유형: 우울, 불안, 정신적 웰빙, 대처 전략 및 심리상담 이용경험과의 관계

본 연구는 객관적·주관적 사회계층에 따른 군집을 분류하고 각 군집에 따라 우울, 불안, 정신적 웰빙, 대처 전략 및 심리상담 이용경험에서 차이가 있는지 살펴보고자 하였다. 또한, 사회계층 군집과 우울, 불안, 정신적 웰빙의 관계를 적극적 대처가 매개하는지 살펴보기 위하여 매개분석을 실시하였다. 총 617명 성인의 설문 응답을 사용하여 군집분석을 실시한 결과, 객관적 사회계층과 주관적 사회계층의 수준에 따라 낙관적 사회계층 인식 군집, 비관적 사회계층 인식 군집, 높은 사회계층 군집, 낮은 사회계층 군집이 도출되었다. 다음으로 다변량분산 분석을 실시하여 군집에 따른 심리적 특성을 확인한 결과, 각 집단에 따라 우울, 불안, 정신적 웰빙, 적극적 대처에서 유의한 차이가 나타났다. 각 군집과 우울, 불안, 정신적 웰빙의 관계에서 적극적 대처의 매개효과를 살펴본 결과, 군집과 정신적 웰빙의 관계를 적극적 대처가 매개하는 것으로 나타났다. 마지막으로 사회계층 군집에 따른 심리상담이용에서는 차이가 나타나지 않았다. 본 연구는 사회계층을 객관적인 측면과 주관적인 측면으로 구분하여 사회계층에 대해 입체적으로 살펴보고, 사회계층이 단순히 인구통계학적 배경이 아니라 내담자의 내적 경험에 영향을 미칠 수 있는 주요한 변인임을 보여주었다는 점에서 그 의의가 있다.

Food choice drivers of potential lab-grown meat consumers in Australia

PurposeTo examine the market potential for lab-grown meat (LGM) in Australia by: (1) determining consumers' willingness to consume LGM; (2) exploring heterogeneity in both consumers' willingness to consume LGM and food choice values; and (3) characterizing unique consumer clusters (segments) using socio-demographic, behavioral and psychosocial factors.Design/methodology/approachLatent class cluster analysis was conducted using online survey data obtained from a nationally representative sample of 1,078 Australian food shoppers.FindingsSix consumer clusters were identified, each distinct in their degree of willingness to consume LGM and in their food choice values. Three clusters (49% of consumers) indicated some willingness to consume LGM. One segment, “Prospective LGM eaters” (12%), appeared “very willing” to consume LGM. These consumers were more likely to be younger (<35 years); university-educated; have greater prior awareness of LGM; stronger beliefs regarding the potential self- and society-related benefits of growing demand for LGM; and higher trust in diverse information sources.Practical implicationsInsights on the characteristics of each cluster provide useful information for the industry on how to tailor product development and marketing strategies to address the needs of consumers with the greatest potential to consume LGM.Originality/valueThis is the first consumer research on the topic of LGM to explore market opportunities for LGM in Australia using a nationally representative consumer sample.

Supratentorial non-RELA, ZFTA-fused ependymomas: a comprehensive phenotype genotype correlation highlighting the number of zinc fingers in ZFTA-NCOA1/2 fusions

The cIMPACT-NOW Update 7 has replaced the WHO nosology of “ependymoma, RELA fusion positive” by “Supratentorial-ependymoma, C11orf95-fusion positive”. This modification reinforces the idea that supratentorial-ependymomas exhibiting fusion that implicates the C11orf95 (now called ZFTA) gene with or without the RELA gene, represent the same histomolecular entity. A hot off the press molecular study has identified distinct clusters of the DNA methylation class of ZFTA fusion-positive tumors. Interestingly, clusters 2 and 4 comprised tumors of different morphologies, with various ZFTA fusions without involvement of RELA. In this paper, we present a detailed series of thirteen cases of non-RELA ZFTA-fused supratentorial tumors with extensive clinical, radiological, histopathological, immunohistochemical, genetic and epigenetic (DNA methylation profiling) characterization. Contrary to the age of onset and MRI aspects similar to RELA fusion-positive EPN, we noted significant histopathological heterogeneity (pleomorphic xanthoastrocytoma-like, astroblastoma-like, ependymoma-like, and even sarcoma-like patterns) in this cohort. Immunophenotypically, these NFκB immunonegative tumors expressed GFAP variably, but EMA constantly and L1CAM frequently. Different gene partners were fused with ZFTA: NCOA1/2, MAML2 and for the first time MN1. These tumors had epigenetic homologies within the DNA methylation class of ependymomas-RELA and were classified as satellite clusters 2 and 4. Cluster 2 (n = 9) corresponded to tumors with classic ependymal histological features (n = 4) but also had astroblastic features (n = 5). Various types of ZFTA fusions were associated with cluster 2, but as in the original report, ZFTA:MAML2 fusion was frequent. Cluster 4 was enriched with sarcoma-like tumors. Moreover, we reported a novel anatomy of three ZFTA:NCOA1/2 fusions with only 1 ZFTA zinc finger domain in the putative fusion protein, whereas all previously reported non-RELA ZFTA fusions have 4 ZFTA zinc fingers. All three cases presented a sarcoma-like morphology. This genotype/phenotype association requires further studies for confirmation. Our series is the first to extensively characterize this new subset of supratentorial ZFTA-fused ependymomas and highlights the usefulness of ZFTA FISH analysis to confirm the existence of a rearrangement without RELA abnormality.

Ability to Write a Reader’s Letter About the School Environment in 03 Lumbir State Junior High School

The issues examined in this research relates to the ability of students in writing letters of readers about the school environment. This research uses descriptive quantitative methods, and sampling techniques used are random class (cluster random sampling). Engineering data collection using the technique of main performance writing letters readers about the school environment. Technique of data analysis using quantitative analysis with the descriptive approach. Based on research results, gained that ability of students in writing letters of readers about the school environment belongs simply because final value shows number 67. The constraints experienced by students in writing letters of readers about the school environment i.e. students still lack understanding about the reader's letters, and the difficulty of applying aspects of mail readers. The most prominent barriers in writing letters of readers about the school environment experienced by students i.e. difficulty applying the spelling language Indonesia with good and true.

Fijian food shopping behaviour: implications for policy makers and smallholder farmers

PurposeThe study aims to identify different consumer groups to better understand changes in urban Fijian food shopping behaviour and the implications for the local food industry.Design/methodology/approachThe authors used a Latent class (LC) cluster analysis of survey data from 1,000 urban Fijian households to identify unique consumer segments based on household food shopping behaviour.FindingsFive distinct urban household clusters were identified based on food shopping behaviour. The cluster with the highest income level spent significantly lower amounts on fresh fruit and vegetables (FFV) at the main traditional market, preferring to buy their FFV from modern supermarket outlets. Considering the vast majority of local smallholder farmers rely on traditional market channels to sell their produce, the growth and dominance of Fijian supermarkets are of some concern.Research limitations/implicationsFuture research should consider repeating these types of detailed consumer surveys to better understand the implications of changes in shopping behaviour over time, and the role that key stakeholders can play in ensuring smallholder farmers is not excluded from the market.Social implicationsSmallholder-driven agriculture accounts for a significant share of Fiji's gross domestic product (GDP), so understanding how the retail food industry is transforming and how this is affecting smallholder farmers is critical to Fiji's social structure.Originality/valueResearch on food retailing and the role of the consumer is rare in small island developing states (SIDS), such as Fiji. Fiji has a somewhat unique set of circumstances. In the absence of significant foreign investment in food retailing, key factors such as urbanisation and rising urban income mean consumer preferences are important drivers of changes in shopping behaviour. The study provides insights into Fiji's changing food industry with implications for other SIDS, while contributing to the global literature in this field.

Activities, time-use and mental health during the first COVID-19 pandemic wave: Insight from Greece

The COVID-19 pandemic shocked the global society and caused significant disruptions on various levels of economic and social activity, apart from the purely humanitarian and health perspective. International community and national goverments introduced a series of restrictions and other measures to minimize the spread of the virus. This paper provides insight from Greece, focusing on activities and time-use, statements towards mental health and overall wellbeing of citizens during the spring lockdown period of 2020. The analysis is based on data from more than 400 individuals collected through an online survey, which included psychometric attitudes and mental health scales, activity participation and time-use, as well as socio-economic variables and reactions to COVID-19 measures and overall situation. We present results and a respondent segmentation, by employing a latent class cluster analysis, which provides useful insight into the mental health, wellbeing of individuals during the restrictions period and information regarding the activities of the various segments of the population before and during the lockdown. Main findings include the identification of three distinct clusters of the respondents, “Relaxed”, “Worried” and “Cautious” which demonstrated some heterogeneity time-use allocation and activity patterns and feelings/mental health during the lockdown. This is one of the first papers to present activity and time-use data for the 2020 lockdown period in Greece by developing a segmentation approach of the participants based on mental health scales and indicators. Such exploratory efforts are useful in identifying different population segments that may react to government restrictions in a heterogenous way and may exhibit varying mental health statuses.

Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

Older drivers are prone to driving errors that can lead to crashes. The risk of older drivers making errors increases in locations with complex roadway features and higher traffic conflicts. Interchanges are freeway locations with more driving challenges than other basic segments. Because of the growing population of older drivers, it is vital to understand driving errors that can lead to crashes on interchanges. This knowledge can assist in developing countermeasures that will ensure safety for all road users when navigating through interchanges. The goal of this study was to determine driver, environmental, roadway, and traffic characteristics that influence older drivers’ errors resulting in crashes along interchanges. The analysis was based on three years (2016–2018) of crash data from Florida. A two-step approach involving a latent class clustering analysis and the penalized logistic regression was used to investigate factors that influence driving errors made by older drivers on interchanges. This approach accounted for heterogeneity that exists in the crash data and enhanced the identification of contributing factors. The results revealed patterns that are not obvious without a two-step approach, including variables that were not significant in all crashes, but were significant in specific clusters. These factors included driver gender and interchange type. Results also showed that all other factors, including distracted driving, lighting condition, area type, speed limit, time of day, and horizontal alignment, were significant in all crashes and few specific clusters.

Stakeholder analysis: Mapping the river networks for integrated flood risk management

In recent decades, a change of paradigm in flood risk management (FRM) has been taking place worldwide. The most current legislation and policies reflect this. This change of paradigm implies a gradual transition towards the adoption of more proactive, integrating strategies which require investment in nature-based solutions (NbS) to mitigate flood risk while ensuring the good status of water bodies and restoring river-floodplain systems. To be effective, this transition should be accompanied by an evolution in governance: from top-down approaches to bottom-up schemes in which empowered and well-endowed stakeholder engagement becomes essential. However, the still limited experiences in implementing this combination present numerous potential difficulties and barriers. We use empirical techniques of social network analysis in combination with latent class cluster analysis to diagnose ex-ante these potential challenges to guide policy makers in designing suitable strategies of communication and involvement of stakeholders in the decision-making process. The analysis is carried out in two stages: (i) identifying and examining the relationships of key stakeholders involved in the management, conservation and exploitation of fluvial systems; and (ii) categorising stakeholders according to their perception of the effectiveness of NbS. Only by creating a cohesive and power-balanced river network is it possible to implement just and legitimate FRM strategy.

Analyzing heterogeneity in passenger satisfaction, loyalty, and complaints with air-rail integrated services

To increase the understanding of passenger choice of air-rail integrated services (ARIS), the present paper a) analyzes the profile of intermodal passengers by classifying them based on their socioeconomic and travel characteristics and (b) uncovers any heterogeneity in passenger satisfaction with ARIS among different passenger groups. Through collecting 1,345 passenger satisfaction questionnaires about ARIS, surveyed in Shijiazhuang, China, we first apply latent class clustering to classify passengers into three groups. Then, the key factors affecting passenger satisfaction and its subsequent effect on loyalty and complaints are identified using a partial least squares structural equation model. Finally, path heterogeneities in different passenger groups are explored using a multi-group analysis. The results show that the three passenger groups attach similar importance to ticket services, reliability, accessibility and comfort, while some noticeable differences in perception are identified in personalized services, information services, and connectivity.

Investigating contributing factors to injury severity levels in crashes involving pedestrians and cyclists using latent class clustering analysis and mixed logit models

Vulnerable road users (VRUs) including pedestrians and cyclists tend to experience more severe injuries when they are involved in crashes compared with motorized vehicle users. Such concern has been expressed as an impediment to the promotion of environment-friendly transportation. To provide insights on the causes of crashes involving VRUs, this study aims to explore the underlying factors that contribute to VRUs injury severity levels and provide constructive recommendations to mitigate injury severity in crashes. In order to minimize heterogeneity existing in the collected data, a latent class clustering method is conducted to categorize collected crash records into different groups. Then the mixed logit models are developed for each cluster as well as the overall crash data. The analysis is conducted based on the crash data retrieved from the Highway Safety Information System (HSIS) from 2012 to 2016 in North Carolina. Distinguished sets of significant factors are identified for clusters with different dominant features. Some factors are found to yield different or even opposite effects in identified clusters, including male gender and non-roadway location. These findings would enhance the understanding of the vulnerable road user (VRU) injury severity mechanism and help policymakers to make reasoned and efficient decisions to improve safety.

Class distribution-aware adaptive margins and cluster embedding for classification of fruit and vegetables at supermarket self-checkouts

The complex task of vision based fruit and vegetables classification at a supermarket self-checkout poses significant challenges. These challenges include the highly variable physical features of fruit and vegetables i.e. colour, texture shape and size which are dependent upon ripeness and storage conditions in a supermarket as well as general product variation. Supermarket environments are also significantly variable with respect to lighting conditions. Attempting to build an exhaustive dataset to capture all these variations, for example a dataset of a fruit consisting of all possible colour variations, is nearly impossible. Moreover, some fruit and vegetable classes have significant similar physical features e.g. the colour and texture of cabbage and lettuce. Current state-of-the-art classification techniques such as those based on Deep Convolutional Neural Networks (DCNNs) are highly prone to errors resulting from the inter-class similarities and intra-class variations of fruit and vegetable images. The deep features of highly variable classes can invade the features of neighbouring similar classes in a learned feature space of the DCNN, resulting in confused classification hyper-planes. To overcome these limitations of current classification techniques we have proposed a class distribution-aware adaptive margins approach with cluster embedding for classification of fruit and vegetables. We have tested the proposed technique for cluster-based feature embedding and classification effectiveness. It is observed that introduction of adaptive classification margins proportional to the class distribution can achieve significant improvements in clustering and classification effectiveness. The proposed technique is tested for both clustering and classification, and promising results have been obtained.

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

Accident analysis and prevention are helpful to ensure the sustainable development of transportation. The aim of this research was to investigate the factors associated with the severity of low-visibility-related rural single-vehicle crashes. Firstly, a latent class clustering model was implemented to partition the whole-dataset into a relatively homogeneous sub-dataset. Then, a spatial random parameters logit model was established for each dataset to capture unobserved heterogeneity and spatial correlation. Analysis was conducted based on the crash data (2014–2019) from 110 two-lane road segments. The results show that the proposed method is a superior crash severity modeling approach to accommodate the unobserved heterogeneity and spatial correlation. Three variables—seatbelt not used, motorcycle, and collision with fixed object—have a stable positive correlation with crash severity. Motorcycle leads to a 12.8%, 23.8%, and 12.6% increase in the risk of serious crashes in the whole-dataset, cluster 3, and cluster 4, respectively. In the whole-dataset, cluster 2, and cluster 3, the risk of serious crashes caused by seatbelt not used increased by 5.5%, 0.1%, and 30.6%, respectively, and caused by collision with fixed object increased by 33.2%, 1.2%, and 13.2%, respectively. The results can provide valuable information for engineers and policy makers to develop targeted measures.

The link between gas and stars in the S254–S258 star-forming region

ABSTRACT The paper aims to study relation between the distributions of the young stellar objects (YSOs) of different ages and the gas-dust constituents of the S254–S258 star formation complex. This is necessary to study the time evolution of the YSO distribution with respect to the gas and dust compounds that are responsible for the birth of the young stars. For this purpose, we use correlation analysis between different gas, dust, and YSO tracers. We compared the large-scale CO, HCO+, near-IR extinction, and far-IR Herschel maps with the density of YSOs of the different evolutionary classes. The direct correlation analysis between these maps was used together with the wavelet-based spatial correlation analysis. This analysis reveals a much tighter correlation of the gas-dust tracers with the distribution of class I YSOs than with that of class II YSOs. We argue that class I YSOs that were initially born in the central bright cluster S255-IR (both N and S parts) during their evolution to class II stage (∼2 Myr) had enough time to travel through the whole S254–S258 star formation region. Given that the region contains several isolated YSO clusters, the evolutionary link between these clusters and the bright central S255-IR (N and S) cluster can be considered. Despite the complexity of the YSO cluster formation in the non-uniform medium, the clusters of class II YSOs in the S254-258 star formation region can contain objects born in the different locations of the complex.

Protocol: A cluster randomized controlled trial of a mobile application to support physical activity maintenance after an exercise oncology program

Objectives/purposeIncreasing physical activity (PA) among adults living with cancer is a public health priority, especially for those in remote/rural locations who have worse health outcomes and lower PA than urban populations. Mobile health technology may overcome barriers to PA participation and change PA behavior. However, few mobile health interventions have focused on sustaining PA long-term in remote/rural adults living with cancer. The present study will examine the effectiveness of a mobile health application to promote PA maintenance in remote/rural adults after completing an exercise oncology program. MethodsThe 24-week prospective 2-arm pilot randomized controlled trial will be embedded within an exercise oncology program in in remote/rural Canada. Participants will be randomized by class cluster 1:1 to the intervention or EXCEL only control group. Both groups will participate in a supervised 12-week group-based exercise oncology program, followed by a 12-week PA maintenance period. The intervention group will use a mobile health application, Zamplo, to complete regular health check-ins and reflections via graphs for 24 weeks. Data will be collected at baseline, 4, 12, 24 weeks using surveys, activity tracking, and semi-structured interviews. Analyses will assess intervention effectiveness to promote PA maintenance and examine participant perspectives and use of Zamplo to support PA. SignificanceThe present study will generate new knowledge on the potential of an mHealth app to promote PA maintenance in remote/rural adults living with cancer. It will inform the integration of mobile health resources within online-delivered exercise oncology programs, leading to sustainable long-term health benefits for this population.

Effectiveness of an Educational School-Based Intervention on Knowledge of Unintentional Injury Prevention and First Aid Among Students in Ujjain, India

To determine the effectiveness of an educational intervention on the knowledge of adolescents on prevention of unintentional injuries and first aid. The study used one group pre-post intervention design, without external controls. A two-stage (at schools and classes) cluster sampling was used to enroll 1944 high school students in Ujjain district. The intervention consisted of educational intervention using lectures presented through Power Point presentations, pictures, and videos. Sixty sessions each wherein a questionnaire to assess knowledge of participants was administered before and after the educational intervention. The outcome was a change in knowledge score. The study included 1944 school students [1105 (57%) boys] with mean (SD) age of 15.9 (1.3) years. The analysis of variance revealed the difference between the sum total of pre- and post-intervention scores was statistically significant (P <0.001), with a large effect size of 3.7. Younger students outperformed older students, boys outperformed girls, students of urban schools outperformed their rural counterparts, students of public schools outperformed those of private schools and students of Hindi medium schools outperformed students of English medium schools. This school-based educational intervention significantly increased the knowledge of students on the prevention of unintentional injuries and first aid.

Exploring the impacts of the COVID-19 pandemic on modality profiles for non-mandatory trips in the Greater Toronto Area

The ongoing COVID-19 pandemic has drastically altered daily life in cities across the world. To slow the spread of COVID-19, many countries have introduced mobility restrictions, ordered the temporary closure of businesses, and encouraged social distancing. These policies have directly and indirectly influenced travel behaviour, particularly modal preferences. The purpose of this paper to explore modality profiles for non-mandatory trips and analyze how they have changed in response to the pandemic and pandemic-related public health policies. The data used for this study were collected from web-based surveys conducted in the Greater Toronto Area. Modality profiles were identified through the application of latent class cluster analysis, with six modality profiles being identified for both the pre-pandemic and pandemic periods. The results indicate that the importance of public transit has declined during the pandemic, while the roles of private vehicles and active modes have become more prominent. However, individuals’ changes in modal preferences vary based on their pre-pandemic modality profile. In particular, it appears that pre-pandemic transit users with access to a private vehicle have substituted public transit for travel by private vehicle, while those without private vehicle access are continuing to use public transit for non-mandatory trips. Consequently, pandemic-related transportation policies should consider those who do not have access to a private vehicle and aim to help those making non-mandatory trips using transit or active modes comply with local public health guidelines while travelling. The results highlight how the changes in modal preferences that occurred due to the pandemic differ among different segments of the population.