Individual Drivers Research Articles

Signs, Sources, Coping Strategies, and Suggested Interventions for Burnout Among Preclerkship Students at a U.S. Medical School: A Qualitative Focus Group Study.

Research suggests that burnout can begin early in medical school, yet burnout among preclerkship students remains underexplored. This study aimed to characterize burnout signs, sources, coping strategies, and potential interventions among preclerkship students at one U.S. medical school. The authors conducted a qualitative study of preclerkship students at Mayo Clinic Alix School of Medicine (MCASOM) in June 2019. Participants completed 2 Maslach Burnout Inventory (MBI) items (measuring frequency of emotional exhaustion and depersonalization) and 2 free-text questions on burnout before participating in 1 of 3 semistructured focus groups. Focus group questions were derived from a literature review on medical student burnout with input from the MCASOM Student Life and Wellness Committee. Group discussions were recorded, transcribed, coded inductively, and analyzed iteratively (along with free-text comments) using a general inductive approach from a constructivist perspective. Eighteen of 111 eligible students (16%) participated, with 5/18 (28%) reporting weekly emotional exhaustion and/or depersonalization on MBI items. Analysis of focus group transcripts showed that most students had experienced burnout symptoms during their first or second year, corresponding with school-related stressors and manifesting in cognitive-emotional, physical, and verbal-behavioral ways. Students identified systemic, institutional, and individual burnout drivers and discussed how these drivers interacted (e.g., high standards of excellence at the system level interacted with anxiety and maladaptive thinking at the individual level, creating pressure to always do more). Students used various coping strategies (e.g., self-care, peer support, reframing, and compartmentalization) but emphasized limitations of these strategies and recommended interventions directed toward systemic and institutional burnout drivers. This study offers insights into burnout signs and sources among preclerkship medical students that can inform future large-scale studies. Results suggest that burnout emerges from dynamic interactions among systemic, institutional, and individual factors and may benefit from multipronged interventions.

From vaccine hesitancy to vaccine motivation: A motivational interviewing based approach to vaccine counselling.

The COVID-19 pandemic highlighted Vaccine Hesitancy (VH) as an accelerating global phenomenon that must be addressed. According to the WHO, thirty to fifty percent of the world's population are VH. Motivational Interviewing (MI) is an evidence-based communication style demonstrated to significantly reduce VH. MI guides people toward change through the expression of empathy and by respecting an individual's autonomy. Healthcare providers (HCPs) are the primary implementors of vaccine policies and the most trusted advisors and influencers of vaccination intention at the individual patient level. Training HCPs in MI is one of the most effective strategies to overcome VH. Many countries are currently implementing HCP training programs and population-based MI interventions to improve vaccine uptake. MI conversations are 'the heart' of vaccine decision-making processes. Understanding individual patient-level drivers of hesitancy allows clinicians to efficiently provide tailored, accurate information that reinforces a person's own motivation and confidence in their own decision. This paper describes a 4-step practical framework designed to support HCPs in their dialogue with vaccine-hesitant patients. (1) Engaging to establish a trustful relationship and safety to freely express opinions, beliefs, and knowledge gaps; (2) Understanding what matters most to the individual; (3) Offering Information to co-build accurate knowledge in order to guide the individual toward vaccine intention (4) Clarifying and Accepting to validate an individual's decision-making autonomy. We believe that our pragmatic approach can contribute to greater acceptability of COVID-19 and other vaccines, and enable rapid deployment of practical MI skills across care systems.

Consumer decision‐making in cosmetic surgery: An interdisciplinary review identifying key challenges and implications for marketing theory

AbstractConsumer interest in cosmetic surgery has grown tremendously in recent years. However, despite the long‐lasting implications of cosmetic surgery decisions on consumer well‐being, existing insights on these decisions remain limited and scattered across various fields. Through an interdisciplinary bibliometric review, we identify that previous research has primarily focused on five aspects shaping consumers' decisions in cosmetic surgery: (1) differing perspectives, (2) social drivers, (3) individual drivers, (4) online media content, and (5) influences relevant to labiaplasty. These investigations stem from the fields of psychology, sociology, communication, and healthcare and aim to understand especially consumer intentions to undergo surgery at the pre‐consumption stage. In this research, we delineate how marketing researchers can advance insights along the entire consumer surgery journey (i.e., pre‐consumption, consumption, and post‐consumption stage) through a better understanding of (i) the phenomenon and its different treatments, (ii) the role of identity, (iii) stakeholder interactions, and (iv) how social media and technology shape cosmetic surgery decisions. Finally, we highlight how studying these topics can contribute to marketing theory through novel insights on the self and identity, the body's meaning in consumption, and self‐enhancement practices.

Critical Individual and Organizational Drivers of Circular Economy Implementation in SMEs in Bangladesh

This study aims to examine the influence of individual and organizational factors on the implementation of circular economy (CE) practices in SMEs in Bangladesh. A non-probability sampling technique is utilized to select a sample of 280 respondents from the textile and leather industries. Data are collected through a survey assessing individual factors such as environmental consciousness and innovation propensity, as well as organizational factors including leadership commitment and training and development programs. A frequency table is used to give the respondents’ details, and skewness and kurtosis are conducted to find the data normality. Reliability and validity analyses are conducted to ensure the robustness of the measurement instruments. A correlation matrix is generated to examine the relationships between variables, followed by a multiple regression analysis to test the hypothesized relationships. The preliminary findings indicate a significant correlation between individual and organizational factors and the implementation of CE practices. The multiple regression analysis reveals that both sets of factors contribute significantly to explaining the variance in CE implementation. The results indicate that environmental consciousness, innovation propensity, leadership commitment, and training and development programs emerge as significant predictors of CE implementation within these industries. This study offers valuable insights and sheds light on the role of individual and organizational factors in shaping sustainable practices. The findings contribute to the explanatory literature on CE implementation, providing empirical evidence to inform policymaking and business strategies aimed at promoting sustainability in the SME sector and beyond.

Explaining Support for Border Closures During the COVID-19 Pandemic: Exposure, Political Attitudes, or Pandemic Policy Feedback?

Abstract The closing of national borders was one of the most far-reaching policy measures adopted to limit the spread of the virus during the coronavirus disease 2019 pandemic. Despite its unprecedented nature and far-reaching impact on individual lives, blocking almost all human movements not only into but also out of states, popular support for this measure was surprisingly high. How can this support be explained? Using an original 4-wave panel dataset across 11 countries together with cross-national policy data, we explore individual and contextual drivers of border closure support throughout 2020. We find that higher support can partly be explained by political attitudes related to cross-border mobility, such as distrust in foreigners or right-wing ideology. Primarily, however, support for closing borders is shaped by respondents’ evaluation of governments’ handling of the pandemic, as well as the intensity and timing of the constraining measures. We also find that support wanes over time, which may indicate a policy fatigue effect. Interestingly, health concerns (exposure to the virus) have almost no influence on support. Even in this exceptional situation, and when confronted with a new policy issue, citizens’ preferences are thus primarily a politically driven response to government measures.

Organizational and Individual Contributing Factors to Safety Climate in Healthcare Industries - Bayesian Network Predictive Modeling Approach.

The current study aims to identify individual and joint drivers that significantly influence the safety climate in healthcare industries by using Bayesian Network simulations for an in-depth analysis. Survey data were collected from 452 employees from two branches of one hospital in China for a study about workplace safety. The original English surveys were translated into Chinese using the back-translation procedure recommended by Brislin. Employees were asked to complete two online surveys with one month in between each administration. The sample was 42% doctors and 58% nurses. A Bayesian Network (BN) model, based on theory, was updated and complemented with expert knowledge. A graphical model based on expert knowledge and data-driven machine learning approaches was used to refine the BN structure, representing interrelationships among all studied variables. The BN model was employed to identify the best key drivers and joint strategies for safety climate improvement. The Bayesian Network model demonstrated a good overall fit. The Euclidean distance metric was used to assess the influence between connected variables, with interpersonal trust and locus of control having the strongest independent effects on safety climate among the five contributing factors. Joint strategies, particularly joint optimization of error disclosure culture and interpersonal trust, as well as error disclosure culture and self-efficacy, were most effective in promoting a safe climate. The findings suggest that hospital safety climate can be improved by providing a psychologically safe error disclosure culture and enhancing interpersonal trust among employees and their self-efficacy.

Unequal Autonomy: An Investigation of Individual and Societal Determinants of Individuals’ Sense of Autonomy in Europe

ABSTRACT This paper explores the social structure of individual autonomy in Europe. Drawing on the capability approach and human empowerment theory, it investigates how individuals’ sense of autonomy is structured by individual means and conversion factors as well as economic, cultural, and institutional context conditions. Using data from the European Social Survey and the European Quality of Life Survey, these relationships are analyzed for 107,070 individuals in 18 European countries and four points in time between 2006 and 2016. Multilevel analyses reveal financial security, health, and social connectedness as the most important drivers of individuals’ sense of autonomy. On the macro level, national wealth, civil liberties, and an emancipative value climate are all found to be positively linked to individuals’ sense of autonomy, albeit weakly. Considering the value individuals attach to leading an autonomous life and the beneficial effects autonomy has been shown to have on people’s well-being, these findings shed first light on the unequal distribution of autonomy and autonomy resources on the individual and macro levels. Concluding, the paper highlights the potential for future research and policy implications.

Privacy-preserving hierarchical federated learning with biosignals to detect drowsiness while driving

AbstractIn response to the global safety concern of drowsiness during driving, the European Union enforces that new vehicles must integrate detection systems compliant with the general data protection regulation. To identify drowsiness patterns while preserving drivers’ data privacy, recent literature has combined Federated Learning (FL) with different biosignals, such as facial expressions, heart rate, electroencephalography (EEG), or electrooculography (EOG). However, existing solutions are unsuitable for drowsiness detection where heterogeneous stakeholders want to collaborate at different levels while guaranteeing data privacy. There is a lack of works evaluating the benefits of using Hierarchical FL (HFL) with EEG and EOG biosignals, and comparing HFL over traditional FL and Machine Learning (ML) approaches to detect drowsiness at the wheel while ensuring data confidentiality. Thus, this work proposes a flexible framework for drowsiness identification by using HFL, FL, and ML over EEG and EOG data. To validate the framework, this work defines a scenario of three transportation companies aiming to share data from their drivers without compromising their confidentiality, defining a two-level hierarchical structure. This study presents three incremental Use Cases (UCs) to assess detection performance: UC1) intra-company FL, yielding a 77.3% accuracy while ensuring the privacy of individual drivers’ data; UC2) inter-company FL, achieving 71.7% accuracy for known drivers and 67.1% for new subjects, ensuring data confidentiality between companies but not intra-organization; and UC3) HFL inter-company, which ensured comprehensive data privacy both within and between companies, with an accuracy of 71.9% for training subjects and 65.5% for new subjects.

FGFR Alterations in Thyroid Carcinoma: A Novel Class of Primary Drivers with Significant Therapeutic Implications and Secondary Molecular Events Potentially Mediating Resistance in Thyroid Malignancy.

Background: Diagnostic classification of thyroid malignancy is primarily accomplished through examination of histomorphological features and may be substantiated and clarified by molecular data. Individual molecular drivers show relatively robust and specific associations with histological subtypes of thyroid malignancy, including BRAF sequence variants and kinase gene fusions in papillary thyroid carcinoma, predominantly RAS variants in follicular-patterned neoplasia, and additional "late" mutations affecting TERT promoter, TP53, and the PI3K/AKT/PTEN pathway in high-grade malignancies. Given the oncogenic role of FGFR, particularly FGFR1-3, the goal of this study was to explore the role of FGFR in thyroid carcinoma biology. Methods: We completed a multicenter retrospective observational study for thyroid carcinomas with pathogenic alterations in the FGFR gene family. We performed this study by querying the molecular data accumulated for thyroid carcinomas from each center. Results: Overall, 5030 sequenced thyroid malignancies were reviewed, yielding 17 tumors with FGFR alterations, including 11 where FGFR was the primary molecular driver and 6 where FGFR was a secondary pathogenic alteration, with a subset for which there was available clinical follow-up data. Of the 11 carcinomas with an FGFR driver, 9 were gene fusions involving FGFR2:VCL (4 tumors), TG::FGFR1 (3 tumors), FGFR2::CIT, and FGFR2::SHTN1, and the remaining 2 were driven by FGFR1 amplification. In the 6 tumors where a canonical driver of thyroid neoplasia was present (5 cases) or no clear primary driver was detected (1 case), sequencing detected secondary FGFR2 p.W290C, p.Y375C, and p.N549K, as well as FGFR1 p.N546K in the respective tyrosine kinase domains, some at subclonal variant allele frequencies. Conclusions: This study presents the first description of a collection of thyroid carcinomas grouped by primary driver alterations in FGFR, as well as a cohort of thyroid tumors with secondary alterations that potentially lead to tumor progression or resistance to targeted therapy. Given the availability of small molecular inhibitors targeting oncogenic FGFR, this study emphasizes the significant implications for patients from identification of FGFR alterations as they are currently under-recognized in the literature and, most importantly, have potential novel treatment options.

Leveraging gamification for tourism marketing activities: Toward a comprehensive conceptual model

Gamification has become one of the most effective ways to generate customer engagement. However, there needs to be more structured research on the drivers of the adoption and sustained use of gamification in travel and tourism research. Drawing upon the uses and gratifications theory and the technology acceptance model, this research examines the individual motivational drivers for adopting gamification in the context of tourism services. We employed a combination of partial least squares—structural equation modeling and fuzzy set qualitative comparative analysis on the data gathered from 680 users of various online travel agencies. Our findings show that individual motivational dimensions of uses and gratifications significantly influence perceived usefulness and ease of use in gamified marketing activities. Our analysis demonstrates five configurations leading to a high level of adoption of gamified marketing activities. This study extends the use of gamification in the tourism industry, offering insights into enhancing customer motivations for adopting and effectively utilizing game mechanics and dynamics in marketing activities. Furthermore, through a novel framework, this study advances the uses and gratification theory at the intersection of gamification research and tourism literature.

What Determines Civil Servants’ Error Response? Evidence From a Conjoint Experiment

To err is human and learning from mistakes is essential for finding viable solutions to grand societal challenges through development and innovation. Yet, public organizations often exhibit a punitive zero-error culture, and public employees are stereotyped as error and risk-averse. Little is known about the underlying behavioral mechanisms that determine civil servants’ likelihood of handling errors positively, namely reporting and correcting them instead of ignoring and hiding them to avoid blame. Based on the transactional theory of stress coping, we argue that individuals’ error-handling strategies relate to both rational and emotional evaluations of error-specific and consequential contextual factors. Using a conjoint survey experiment conducted with N = 276 civil servants in Germany ( Obs. = 1,104), this study disentangles the effects of error-related, individual, and organization-cultural factors as decisive drivers of individuals’ error response. We find that error characteristics (type and harmfulness) determine error-handling behavior, which is revealed to be independent from organizational error culture and individual error orientation, providing important and novel insights for theory and practice.

Pricing weekly motor insurance drivers’ with behavioral and contextual telematics data

Telematics boxes integrated into vehicles are instrumental in capturing driving data encompassing behavioral and contextual information, including speed, distance travelled by road type, and time of day. These data can be amalgamated with drivers' individual attributes and reported accident occurrences to their respective insurance providers. Our study analyzes a substantial sample size of 19,214 individual drivers over a span of 55 weeks, covering a cumulative distance of 181.4 million kilometers driven. Utilizing this dataset, we develop predictive models for weekly accident frequency. As anticipated based on prior research with yearly data, our findings affirm that behavioral traits, such as instances of excessive speed, and contextual data pertaining to road type and time of day significantly aid in ratemaking design. The predictive models enable the creation of driving scores and personalized warnings, presenting a potential to enhance traffic safety by alerting drivers to perilous conditions. Our discussion delves into the construction of multiplicative scores derived from Poisson regression, contrasting them with additive scores resulting from a linear probability model approach, which offer greater communicability. Furthermore, we demonstrate that the inclusion of lagged behavioral and contextual factors not only enhances prediction accuracy but also lays the foundation for a diverse range of usage-based insurance schemes for weekly payments.

Shaping the Habits of Teen Drivers

We show that a targeted law can modify teens’ risky behavior. We examine the effects of an Australian intervention banning first-year drivers from driving late at night with multiple peers, which had accounted for one-fifth of their traffic fatalities. Using data on individual drivers linked to crash outcomes, we find the reform more than halves targeted crashes, casualties and deaths. There are large positive spillovers through lower crashes earlier in the evening and beyond the first year, suggesting broad and persistent declines in high-risk driving. Overall, the targeted intervention delivers gains comparable to harsher restrictions that delay teen driving. (JEL D91, J13, K32, R41)

Using driver monitoring to estimate readiness in automation: a conceptual model based on simulator experimental data

This paper provides a theoretical overview of how the concept of driver readiness can be objectively measured, using controlled experimental data. First, a literature review regarding the concept of driver readiness is provided. Then, it highlights challenges for a standardized readiness estimation model. A conceptual readiness estimation model is presented, and a methodology is proposed for defining readiness thresholds for use by Driver State Monitoring (DSM) systems. The paper then explores how this model can be used to estimate readiness thresholds. A proof of concept for the model application is presented, using previously collected experimental involving SAE Level 2 automation. This paper contributes to the state of the art in DSM-development, by providing a methodology for estimating driver readiness, while considering variabilities across individual drivers. The model also allows readiness thresholds to be defined with data from driving simulator experiments, without relying on subjective assessment of readiness as its ground truth.

Perceived glucose levels matter more than CGM-based data in predicting diabetes distress in type 1 or type 2 diabetes: a precision mental health approach using n-of-1 analyses.

Diabetes distress is one of the most frequent mental health issues identified in people with type 1 and type 2 diabetes. Little is known about the role of glucose control as a potential contributor to diabetes distress and whether the subjective perception of glucose control or the objective glycaemic parameters are more important for the experience. With the emergence of continuous glucose monitoring (CGM), this is a relevant question as glucose values are now visible in real-time. We employed a precision monitoring approach to analyse the independent associations of perceived and measured glucose control with diabetes distress on a daily basis. By using n-of-1 analyses, we aimed to identify individual contributors to diabetes distress per person and analyse the associations of these individual contributors with mental health at a 3 month follow-up. In this prospective, observational study, perceived (hypoglycaemia/hyperglycaemia/glucose variability burden) and measured glucose control (time in hypoglycaemia and hyperglycaemia, CV) were assessed daily for 17 days using an ecological momentary assessment (EMA) approach with a special EMA app and CGM, respectively. Mixed-effect regression analysis was performed, with daily diabetes distress as the dependent variable and daily perceived and CGM-measured metrics of glucose control as random factors. Individual regression coefficients of daily distress with perceived and CGM-measured metrics were correlated with levels of psychosocial well-being at a 3 month follow-up. Data from 379 participants were analysed (50.9% type 1 diabetes; 49.6% female). Perceived glucose variability (t=14.360; p<0.0001) and perceived hyperglycaemia (t=13.637; p<0.0001) were the strongest predictors of daily diabetes distress, while CGM-based glucose variability was not significantly associated (t=1.070; p=0.285). There was great heterogeneity between individuals in the associations of perceived and measured glucose parameters with diabetes distress. Individuals with a stronger association between perceived glucose control and daily distress had more depressive symptoms (β=0.32), diabetes distress (β=0.39) and hypoglycaemia fear (β=0.34) at follow-up (all p<0.001). Individuals with a stronger association between CGM-measured glucose control and daily distress had higher levels of psychosocial well-being at follow-up (depressive symptoms: β=-0.31; diabetes distress: β=-0.33; hypoglycaemia fear: β=-0.27; all p<0.001) but also higher HbA1c (β=0.12; p<0.05). Overall, subjective perceptions of glucose seem to be more influential on diabetes distress than objective CGM parameters of glycaemic control. N-of-1 analyses showed that CGM-measured and perceived glucose control had differential associations with diabetes distress and psychosocial well-being 3 months later. The results highlight the need to understand the individual drivers of diabetes distress to develop personalised interventions within a precision mental health approach.

Geochemical drivers of manganese removal in drinking water reservoirs under hypolimnetic oxygenation

Manganese (Mn) is a naturally occurring contaminant commonly found in drinking water supplies. In lakes and reservoirs, water authorities increasingly use in situ treatment by hypolimnetic oxygenation (HOx) systems to remove metals such as Mn from the water column. HOx systems introduce dissolved oxygen (DO) to the bottom waters (hypolimnion) to promote oxidation and subsequent removal of metals from the water column. Previous laboratory studies have shown the importance of individual geochemical drivers (pH, alkalinity, mineral surfaces) on Mn oxidation, but few studies have examined the influence of these drivers of Mn removal in concert. In this study, we conducted field monitoring and laboratory experiments to examine how pH, alkalinity and the presence of mineral particles influence Mn removal at two drinking water reservoirs in southwest Virginia, both with HOx systems: Falling Creek Reservoir (FCR) and Carvins Cove Reservoir (CCR). Both reservoirs have had historical issues with elevated (>0.05 mg/L) Mn concentrations during seasonal stratification (May–October). Watershed geology contributes to differences in pH and alkalinity between the reservoirs, with FCR having lower historical medians of hypolimnetic pH and alkalinity (6.6 and 18 mg/L CaCO3, respectively) than CCR (7.2 and 62 mg/L CaCO3, respectively).Results of laboratory experiments examining the influence of pH on Mn removal showed substantial Mn loss within 14 days only under high pH (10) conditions. Mn removal did not occur at pH 6 or 8 over the same 14-day period. In experiments with pH 10 and alkalinity >70 mg/L CaCO3, near-total Mn removal occurred within 2 h. Mn removal occurred concurrently with precipitation of microscopic (<5 μm) particles, followed by formation of macroscopic (>100 μm) particles. Particles of both size classes were identified as Mn oxides (MnOx). These observations suggest that increasing pH and alkalinity promotes Mn oxidation and subsequent removal from solution. Results of experiments with pH 10 and alkalinity >70 mg/L CaCO3 suggest that heterogeneous oxidation by MnOx partially drives rapid Mn removal. Thus, initial formation of MnOx creates a positive feedback loop that can enhance additional Mn loss. In experiments using water collected from FCR and CCR, we observed rapid Mn removal in unfiltered water (0.002–0.05 d−1) but no significant removal of Mn in filtered water. These results, in combination with results of analysis of particles collected from reservoir water, suggest that minerals present in the water column likely catalyze MnOx formation. Together, our experimental results suggest that heterogenous oxidation is an important process of Mn removal, while pH and alkalinity variations of the range expected in natural freshwaters contribute less to differential Mn removal. The formation of MnOx particles during in situ oxygenation, as well as the presence of suspended minerals that occur naturally in water columns, play an important role in promoting Mn oxidation and should be accounted for in Mn removal treatment strategies.

Destroying Phantom Jams with Connectivity and Automation: Nonlinear Dynamics and Control of Mixed Traffic

Connected automated vehicles (CAVs) have the potential to improve the efficiency of vehicular traffic. In this paper, we discuss how CAVs can positively impact the dynamic behavior of mixed traffic systems on highways through the lens of nonlinear dynamics theory. First, we show that human-driven traffic exhibits a bistability phenomenon, in which the same drivers can both drive smoothly or cause congestion, depending on perturbations like a braking of an individual driver. As such, bistability can lead to unexpected phantom traffic jams, which are undesired. By analyzing the corresponding nonlinear dynamical model, we explain the mechanism of bistability and identify which human driver parameters may cause it. Second, we study mixed traffic that includes both human drivers and CAVs, and we analyze how CAVs affect the nonlinear dynamic behavior. We show that a large-enough penetration of CAVs in the traffic flow can eliminate bistability, and we identify the controller parameters of CAVs that are able to do so. Ultimately, this helps to achieve stable and smooth mobility on highways. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This work was supported by the University of Michigan’s Center for Connected and Automated Transportation [U.S. DOT Grant 69A3551747105]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0498 .

Unveiling gap acceptance behaviour during lane change with EDIV data: A deep dive into driving behaviour on expressway using a three level mixed effect linear regression approach

Lane change has a potential significance in road safety. Gap acceptance phenomena serves as a primary and critical phase in lane change maneuver. This study aims to investigate the gap acceptance behaviour of drivers during lane changes on expressways, with a focus on understanding how various factors influence drivers' decisions to change lanes. An extensive dataset collected through various sensors tailored for expressway driving, known as the ‘Expressway Drive: Instrumented Vehicle (EDIV) Dataset’ is utilized. Driving data from 59 drivers covering a distance of around 4000 km was used in the current study. Total 2578 lane changing events are identified through computing lateral deviations measured through 3D LiDAR sensor. Substantial differences are observed within the groups in primary analysis which suggest that lane-change direction significantly affect gap acceptance. To effectively manage both intra- and inter-cluster variances, this study employs two separate three levels mixed-effects linear models. These models account for the interdependence of gap acceptance characteristics within individual drivers and for different directions of lane changes by incorporating random effects. Furthermore, these models examine relationships between lead/ lag gap acceptance and the various influencing factors as fixed effects. It was found that factors such as speed of the subject vehicle, gap position, relative speeds, and surrounding vehicle types had influence on gap acceptance during lane changes on expressways. The insights gained from this study could inform the development of advanced driver assistance systems (ADAS) as well as development of autonomous vehicles, contributing to improved road safety and traffic flow management in high-speed environments.

Health service providers' views on barriers and drivers to childhood vaccination of FDMN/Rohingya refugees: a qualitative study in Cox's Bazar, Bangladesh.

Despite established vaccination programs, vaccine-preventable diseases persist among about 900,000 Forcibly Displaced Myanmar Nationals (FDMN)/Rohingya refugees in the world's largest refugee settlement in Bangladesh. Health service providers (HSPs) play a key role in the delivery of childhood vaccination programs. This study explored their views on individual and context barriers and drivers to childhood vaccination in this setting. Informed by the theoretical framework of the Capability-Opportunity-Motivation-Behavior (COM-B) model for behavior change, this qualitative study collected data through eight focus group discussions (FGDs) with community health workers (CHWs) and vaccinators in selected camps with high or low vaccination coverage rates, and through 11 in-depth interviews (IDIs) with key informants working in strategic, management, and administrative roles. Barriers and drivers were evident across all COM factors for HSPs and caregivers. Among HSPs, knowledge around vaccination acted both as a barrier and driver, while communication skills and confidence in vaccination served as drivers. Caregivers' lack of awareness of vaccination, concerns and mistrust were described as main barriers. Context barriers included information system deficiencies, family dynamics, HSPs' working conditions, and vaccination site accessibility. Context drivers included effective communication, mobilization, and incentives. Differences between high and low coverage camps in Cox's Bazar included variations in HSPs' knowledge, communication strategies, incentive use, and stakeholder collaboration. For better vaccination coverage in the camps, context-related changes regarding collaboration, health workforce and the use of incentives seem necessary. Caregivers' mistrust toward vaccination needs to be considered under the social and historical background of the Rohingya community, and further addressed with targeted communication and campaigning.

In-vehicle nudging for increased Adaptive Cruise Control use: a field study

Close following to lead vehicles is associated with increased risk of rear-end crashes in road traffic. One way to reduce instances of close following is through increased use of the Advanced Driver Assistance System (ADAS) Adaptive Cruise Control (ACC), which is designed to adjust vehicle speed to maintain a safe time headway. Since the activation of ACC is driver-initiated, there is a need to influence the propensity of drivers to use the function. This research aimed to explore whether in-vehicle nudging interventions could be effective for this purpose. A field trial was conducted to consecutively assess the effects of two nudges on drivers’ utilization of ACC, compared to baseline usage. Exposing the participants (n = 49) to the first ambient design nudge resulted in a 46% increase in ACC usage on average. Following the introduction of the second nudge (a competitive leaderboard nudge), the average increase among participants (n = 48) during the complete treatment period reached 61%. The changes in ACC utilization varied between individual drivers, highlighting the need to monitor behavioral outcomes of nudges and adapt them when needed. In conclusion, this research shows that utilizing in-vehicle nudging is a promising approach to increase the use of vehicle functions contributing to improved traffic safety.