Response Data Research Articles

Open Data-Driven Decision Making: Observations from COVID-19 Minimal Essential Data, Canton Zürich, Switzerland.

This research delves into the relationship between open data and decision making, drawing insights from the utilization of Minimal Essential Data (MED) during the COVID-19 pandemic in Canton Zürich, Switzerland. It examines observations and lessons learned, emphasizing the pivotal role of open data in response to an emergency. Through a case study approach, this article sheds light on the practical implications and observed strategies in publishing open data from digital public health surveillance software. While it is arguable that open data has the potential to enhance data quality in the context of a public health emergency, our findings indicate that it is firstly essential that a Minimal Essential Dataset (MED) is defined and publicly published, an open data publication platform and procedure are secondly in place (ideally following FAIR data principles), and thirdly, that regular data quality reviews are conducted.

Computational Modeling of Drug Response Identifies Mutant-Specific Constraints for Dosing panRAF and MEK Inhibitors in Melanoma.

This study explores the potential of pre-clinical in vitro cell line response data and computational modeling in identifying the optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. In a drug combination screen of 43 melanoma cell lines, we identified specific dosage landscapes of panRAF and MEK inhibitors for NRAS vs. BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma (mean Bliss score of 0.27 in NRAS vs. 0.1 in BRAF mutants). Computational modeling and follow-up molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated the in vivo translatability of in vitro dose-response maps by predicting tumor growth in xenografts with high accuracy in capturing cytostatic and cytotoxic responses. We analyzed the pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Overall, this work presents a framework to aid dose selection in drug combinations.

Comparison of univariate and bivariate Poisson regression methods in the analysis of determinants of female schooling and fertility in Malawi

Recent research has established existence of a correlation between women’s education and fertility, suggesting that they share similar risk factors. However, in many studies, the two variables were analysed separately, which could bias the conclusions by undermining the apparent correlations of such paired outcomes. In this article, the univariate and bivariate Poisson regression models were applied to nationally representative sample of 24,562 women from the 2015-16 Malawi demographic and health survey to examine the risk factors of women’s education levels and fertility. The R software version 4.1.2 was used for the analyses. The results showed that estimates from the bivariate Poisson model were consistent with those obtained from the separate univariate Poisson models. The sizes of estimates of coefficients, their standard errors, p-values, and directions were comparable in both bivariate and univariate Poisson models. Using either the univariate or bivariate Poisson model, it was found that the age of a woman at first sexual experience, her current age, household wealth index, and contraceptive usage were significantly associated with both the woman’s schooling and fertility. The study further revealed that ethnicity, religion, and region of residence impacted education level only and not fertility. Similarly, marital status and occupation impacted fertility only and not education. The study also found that higher education levels were linked to a lower number of children, with a strong negative correlation of -0.62 between the two variables. The study recommends using bivariate Poisson regression for analysing paired count response data, when there is an apparent covariance between the outcome variables. The results suggest that efforts by policymakers to achieve the desired women’s sexual and reproductive health in sub-Saharan Africa should be intertwined with improving women’s and girls’ education attainment in the region.

Mapping socio-economic status using mixed data: a hierarchical Bayesian approach

Abstract We propose a Bayesian hierarchical model to estimate a socio-economic status (SES) index based on mixed dichotomous and continuous variables. In particular, we extend Quinn’s ([2004]. Bayesian factor analysis for mixed ordinal and continuous responses. Political Analysis, 12(4), 338–353. https://doi.org/10.1093/pan/mph022) and Schliep and Hoeting’s ([2013]. Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data. Journal of Agricultural, Biological, and Environmental Statistics, 18(4), 492–513. https://doi.org/10.1007/s13253-013-0136-z) factor analysis models for mixed dichotomous and continuous variables by allowing a spatial hierarchical structure of key parameters of the model. Unlike most SES assessment models proposed in the literature, the hierarchical nature of this model enables the use of census observations at the household level without needing to aggregate any information a priori. Therefore, it better accommodates the variability of the SES between census tracts and the number of households per area. The proposed model is used in the estimation of a socio-economic index using 10% of the 2010 Ghana census in the Greater Accra Metropolitan area. Out of the 20 observed variables, the number of people per room, access to water piping and flushable toilets differentiated high and low SES areas the best.

ANN-Based Bridge Support Fixity Quantification Using Thermal Response Data from Real-Time Wireless Sensing.

Bridges are critical infrastructures that support our economic activities and daily lives. Aging bridges have been a major issue for decades, prompting researchers to improve resilience and performance through structural health monitoring. While most research focuses on superstructure damage, the majority of bridge failures are associated with support or joint damages, indicating the importance of bridge support. Indeed, bridge support affects the performance of both the substructure and superstructure by maintaining the load path and allowing certain movements to mitigate thermal and other stresses. The support deterioration leads to a change in fixity in the superstructure, compromising the bridge's integrity and safety. Hence, a reliable method to determine support fixity level is essential to detecting bearing health and enhancing the accuracy of the bridge health monitoring system. However, such research is lacking because of its complexity. In this study, we developed a support fixity quantification method based on thermal responses using an Artificial Neural Network (ANN) model. A finite element (FE) model of a representative highway bridge is used to derive thermal displacement data under different bearing stiffnesses, superstructure damage, and thermal loading. The thermal displacement behavior of the bridge under different support fixity conditions is presented, and the model is trained on the simulated response. The performance of the developed FE model and ANN was validated with field monitoring data collected from two in-service bridges in Connecticut using a real-time Wireless Sensor Network (WSN). Finally, the support stiffnesses of both bridges were predicted using the ANN model for validation.

Identification of hanger damage based on deflection influence matrix

Tied-arch bridges, a vital component of modern infrastructure, are susceptible to various forms of damage, particularly hangers. The detection and identification of such damages are crucial for maintaining structural integrity and safety. However, traditional methods face challenges in terms of accuracy and efficiency. This study aims to develop a refined method for hanger damage identification in tied-arch bridges, to address the limitations of existing techniques. By focusing on deflection changes at the anchoring points between the hangers and tie beams, we sought to enhance the precision of damage detection. We propose an innovative approach based on the concept of influence lines, introducing the ‘generalized deflection difference influence line'and the ‘deflection difference influence matrix’. Then proposed a new identification index for identifying the damaged hanger after matrix. An actual tied-arch bridge was used to validate the proposed approach. A detailed three-dimensional finite element model of the bridge was developed and calibrated using dynamic and static response data. Thirty different hanger-damage conditions were simulated to evaluate the effectiveness of the proposed method. Our findings reveal that the deflection difference influence matrix offers more detailed and comprehensive information on bridge distribution points than traditional methods. Our method proved effective in identifying hanger damage, irrespective of its location on the bridge. In additionally, the identification efficiency of the method can be improved by adjusting the magnitude of the applied load, with larger loads amplifying the detectability of damage. This study highlights the potential of the deflection difference influence matrix to revolutionize hanger damage identification for tied-arch bridges. Its adaptability, accuracy, and efficiency are significant advancements over existing methods. This study successfully demonstrates an innovative and reliable method for hanger damage identification.

Detection of ballastless track interlayer gap based on vehicle’s multivariate dynamic response and deep learning

To adapt to the rapid detection of interlayer damage in ballastless track structures of high-speed railways, a cement asphalt mortar (CAM) gap localization and damage degree classification scheme based on multivariate data fusion and deep learning is proposed. Based on vertical axle box acceleration (VABA) and vertical wheel-rail force (VWRF) data, the variation patterns of multi-dynamic response data of vehicles under the edge type and internal type interlayer gaps are analyzed. An improved ensemble local mean decomposition algorithm (IELMD) is designed to achieve data denoising and preliminary enhancement preprocessing of weak damage features for VABA and VWRF under interlayer gaps. The measured and simulated VABA and VWRF constituted multiple dynamic response data sets for interlayer gap detection. An interlayer gap detection model: DTA-Tcnformer is constructed, integrating a dual temporal convolutional network with an attention mechanism, transformer architecture, and gate control mechanism. Multiple dynamic response data are input into the model to locate and classify 24 cases of the two interlayer gap types. The influence of damage cases and vehicle speeds on the location effect is analyzed. The learning ability of the proposed model on the gap features is visualized. The misidentification and omission of the gap cases are calculated, and the comprehensive recognition accuracy is 92.48 %. The recognition performance of the proposed model is compared and verified.

MLC-DKT: A multi-layer context-aware deep knowledge tracing model

As a fundamental research task for intelligent education, knowledge tracing (KT) aims to trace learners’ dynamic knowledge states and predict their future performance based on their historical response data. However, learners’ cognitive processes cannot be studied in isolation from the context; nevertheless, existing knowledge tracing models mainly use knowledge topology as auxiliary information but do not deeply explore the contextual information of exercises. Thus, an in-depth exploration of multi-layer contextual information in learning scenarios is required, focusing on the cognitive differences among different learners. Furthermore, knowledge tracing models exhibit limited interpretability compared with cognition diagnosis methods integrating educational priors. To address these issues, we propose a multi-layer context-aware deep knowledge tracing (MLC-DKT) model. Specifically, we first present the multi-layer contextual representation method involving knowledge concepts and exercises. In the knowledge state tracing module, we incorporate the learners’ knowledge priors into the attention mechanism to capture the evolution of the learners’ knowledge. Then, we develop a calculation method for the similarity effect among the contextual information, which helps estimate the learners’ knowledge states. In the learning performance prediction module, we introduce the guessing and slipping factors from the cognition diagnosis model to optimize the MLC-DKT model, further improving the performance of the KT model. Finally, we conduct extensive experiments on real-world datasets, demonstrating that the MLC-DKT model provides improved predictions of learner performance. Moreover, the MLC-DKT model realizes contextual awareness from knowledge concepts and exercises, making the predictions more interpretable.

Parameter Calibration and Verification of Elastoplastic Wet Sand Based on Attention-Retention Fusion Deep Learning Mechanism

The discrete element method (DEM) is a vital numerical approach for analyzing the mechanical behavior of elastoplastic wet sand. However, parameter uncertainty persists within the mapping between constitutive relationships and inherent model parameters. We propose a Parameter calibration neural network based on Attention, Retention, and improved Transformer for Sequential data (PartsNet), which effectively captures the nonlinear mechanical behavior of wet sand and obtains the optimal parameter combination for the Edinburgh elasto-plastic adhesion constitutive model. Variational autoencoder-based principal component ordering is employed by PartsNet to reduce the high-dimensional dynamic response and extract critical parameters along with their weights. Gated recurrent units are combined with a novel sparse multi-head attention mechanism to process sequential data. The fusion information is delivered by residual multilayer perceptron, achieving the association between sequential response and model parameters. The errors in response data generated by calibrated parameters are quantified by PartsNet based on adaptive differentiation and Taylor expansion. Remarkable calibration capabilities are exhibited by PartsNet across six evaluation indicators, surpassing seven other deep learning approaches in the ablation test. The calibration accuracy of PartsNet reaches 91.29%, and MSE loss converges to 0.000934. The validation experiments and regression analysis confirmed the generalization capability of PartsNet in the calibration of wet sand. The improved sparse attention mechanism optimizes multi-head attention, resulting in a convergence speed of 21.25%. PartsNet contributes to modeling and simulating the precise mechanical properties of complex elastoplastic systems and offers valuable insights for diverse engineering applications.

Empowering Medical Students to Practice High-Value Care

Over the last decade, initiatives like the Choosing Wisely campaign have promoted the practice of evidence-based, cost-conscious care. However, education surrounding value in medicine has been largely restricted to graduate-level medical education or post-clerkship undergraduate medical education. Here, we present a mixed-methods study evaluating pre-clerkship medical student engagement with and response to new value-based curricular initiatives to assess whether high-value care (HVC) can be introduced successfully earlier in training. Initiatives were introduced into the pre-clerkship clinical curriculum without major alterations to the established curricular structure. These comprised an annual event devoted to self-reflecting on HVC observed in prior and current clinical experiences and subsequent small-group facilitated discussion. Engagement with and response to these initiatives were analyzed for two whole-class cohorts using an abductive, progressive focusing approach complementing quantitative survey data. Baseline familiarity with HVC and post-event response to the annual event was higher and more positive, respectively, among responders from the Class of 2025 compared with those from the Class of 2024. Analysis of reflection essays revealed the emergence of five primary themes differentiating the two class years: (1) understanding HVC, (2) addressing barriers to HVC, (3) medical waste and underserved communities, (4) patient–physician relationship and value, and (5) self-reflection. Evaluation of thematic patterns in light of survey response data suggests that early clinical exposure combined with intentional self-reflection could promote engagement with HVC topics in a way that shapes students’ foundational understanding of the practice of medicine.

Distribution of Relaxation Times Analysis of PEMFC through Loewner Framework: A Direct and Regularization Free Approach

The analysis and interpretation of electrochemical impedance spectroscopy (EIS) data are often complex and challenging, despite its popularity within the field of electrochemistry. Hence, recently, we have introduced novel data-driven approach for extracting the distribution of relaxation times (DRT) from impedance data in order to improve further analysis [1, 2]. This approach is based on the Loewner framework, which is a versatile and straightforward data-driven modeling method that computes reduced-order models in a state-space representation directly from frequency response data. Thus, it allows estimation of DRT without need of regularization procedures or iterative optimization algorithms.The effectiveness of this approach is successfully demonstrated by analysing the experimental EIS data of a polymer electrolyte membrane fuel cell (PEMFC). The results show that this method allows clear identification of the individual polarization processes based on their characteristic time constants (see Figure). Furthermore, the peaks observed in the DRT plot are correctly associated with different processes by examining spectra under various operating conditions. This is further validated by employing a physics-based model [3]. In this way, unambiguous fault identification and monitoring of the degradation state of the cell is possible and it can be the foundation for an on-board diagnostic tool.

The Influence of Comic Media in Learning Dudaya Arts for Students at SMA Negeri 1 Soromandi

This research is experimental research, with the aim of designing comic arts and culture media, knowing the influence of comic media in learning arts and culture among students at SMA Negeri 1 Soromandi and students' responses to comic media. The population in this study was class X students of SMA Negeri 1 Soromandi for the 2024/2025 academic year and the research sample was class Samples were taken using purposive sampling technique. Data on students' concept mastery was obtained using a concept mastery test and student response data was collected using a questionnaire. Student concept mastery data was analyzed using the t-test and student response data was analyzed using five scale absolute norms. In this research, 3 series of Arts and Culture comics have been produced. The results of the analysis show that there is a difference in learning arts and culture through comic media for experimental class and control class students at a significance level of 5% with a value of t'= 3.026 and this value is not in the interval - 2.02, <t' <2.02. This means that comic media in arts and culture learning has an influence on improving the learning of class X students at SMA Negeri 1 Soromandi. while the analysis and questionnaire showed that students responded positively to learning arts and culture using comic media with a percentage of 53.84%

Computational modeling of drug response identifies mutant-specific constraints for dosing panRAF and MEK inhibitors in melanoma.

This study explores the potential of preclinical in vitro cell line response data and computational modeling in identifying optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. In a drug combination screen of 43 melanoma cell lines, we identified unique dosage landscapes of panRAF and MEK inhibitors for NRAS vs BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma. Computational modeling and molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated in vivo translatability of in vitro dose-response maps by accurately predicting tumor growth in xenografts. Then, we analyzed pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range.

Blood MALT1 serves as a potential biomarker reflecting the response and survival of immune‑checkpoint‑inhibitor therapy in advanced hepatocellular carcinoma.

Treatment modalities involving an immune-checkpoint-inhibitor (ICI) have emerged as therapeutic options in advanced hepatocellular carcinoma (HCC). Nonetheless, auxiliary biomarkers are required to evaluate their efficacy. The present study aimed to assess the potential of blood mucosa-associated lymphoid tissue 1 (MALT1) in reflecting clinical response and prognosis in patients with advanced HCC who received ICI therapy. Peripheral blood was collected from 51 patients with advanced HCC who were about to receive ICI or ICI-based treatment. Blood MALT1 levels were determined using reverse transcription-quantitative PCR, and the blood MALT1 levels in 50 healthy controls (HCs) were also assessed. Besides, the treatment response and survival data were collected. The Wilcoxon rank-sum test was used for comparison analysis and the Spearman's rank correlation coefficient test was used for correlation analysis. The prognostic value of MALT1 was determined by Kaplan-Meier curve analysis with the log-rank test. Univariate and multivariate Cox regression models were used to identify factors associated with progression-free survival (PFS) and overall survival (OS). The results demonstrated that blood MALT1 levels were significantly increased in patients with advanced HCC compared with that in HCs (P<0.001). Blood MALT1 levels were increased in patients with portal vein invasion (vs. without portal vein invasion; P=0.010), extrahepatic disease (vs. without extrahepatic disease; P=0.026) and α-fetoprotein (AFP) ≥200 ng/ml (vs. AFP <200 ng/ml; P=0.040). After 4 cycles of ICI therapy, the objective response rate (ORR) and disease control rate (DCR) was 29.4 and 68.6%, respectively. Blood MALT1 levels were also significantly and negatively associated with the ORR (P=0.043) and DCR (P=0.004). Furthermore, PFS and OS were shortened in patients with high blood MALT1 levels (cut-off by the median) compared to those with low blood MALT1 levels. After adjusting using multivariate Cox regression models, high blood MALT1 levels were demonstrated to be a significant independent risk factor for shortened PFS [hazard ratio (HR)=2.419; P=0.009] and OS (HR=2.706, P=0.018) in patients with advanced HCC who received ICI therapy. In summary, blood MALT1 levels serve as a potential biomarker to reflect treatment response and survival in patients with advanced HCC who receive ICI therapy.

Response satisficing and data quality in marketing: measurement and effects of satisficing on objective knowledge, experimental results, and replicability of findings

ABSTRACT While crowdsourced data are often used in marketing research, there remain concerns about the validity of data sourced from crowdsourcing platforms such as Amazon’s Mechanical Turk (MTurk). Across two studies using different data sources and respondent screening strategies, this research examines how an explicit measure of participant response satisficing can adversely impact theory-driven research findings and the applied conclusions drawn. Findings demonstrate that respondent satisficing level and the degree of its effects are a significant threat to the integrity and validity of marketing and consumer research. Implications are offered concerning managerial decisions, tests of theory, and consumer research and well-being.

29 Pilot study of an implantable microdevice forin vivo evaluation of drug response in renal cell carcinoma (NCT05700461)

Abstract Background There are multiple therapies for renal cell carcinoma (RCC), yet no personalized biomarkers exist that can guide optimal treatment for individual patients. We developed implantable microdevices (IMD) that can be placed within a target tumor using a standard small-gauge interventional needle under imaging guidance. The IMDs deliver spatially segregated microdoses of up to 20 different drugs and/or combinations over multiple days, and then are retrieved surgically with surrounding tissue. This allows for the assessment of tumor response to multiple drugs in vivo within the native tumor microenvironment, without exposing the patient to systemic drug toxicity. We are conducting a trial to evaluate the safety and feasibility of IMD implantation in RCC tumors and subsequent collection of drug response data on targeted, cytotoxic, and immune-modulatory agents. Methods Eligible patients have suspected or confirmed RCC and are planned for standard surgical resection of either their primary tumor or metastasis. Three days prior to surgery, one or more IMDs are implanted percutaneously into the tumor under CT guidance. Following nephrectomy or excision of a metastasis, the microdevices with surrounding tumor are removed en bloc; subsequently, the tissue is fixed, embedded and sectioned. The tumor/IMD cross-sections are analyzed for drug effect for each of the spatially separate treatment effects using a wide range of techniques, including immunohistochemistry for proliferation and pathway markers, spatial immune profiling using a panel of 25 tumor microenvironment markers, and spatial transcriptomics. Patients receive subsequent standard of care systemic treatment as per their oncologist’s discretion. Five patients have been enrolled and a total of 16 IMDs have been implanted to date. Enrollment is ongoing for a planned total of 20 patients. Success is defined as meeting both safety and feasibility endpoints. Safety is defined as two or fewer safety failures out of 20 enrolled patients. Feasibility is defined as 16 or more patients with successful microdevice implantation and retrieval of interpretable data. IMDs will be analyzed to explore spatially segregated drug effects on the tumor microenvironment including tumor cell death and immune cell activation, as well as evidence of differential tumor drug sensitivity across treatments and patients. Conclusions Implantation of microdevices in RCC tumors may be a useful approach to improve treatment selection and accelerate drug development. Trial enrollment is ongoing.

LDIPRS: A novel longitudinal driving intention prior recognition technique empowered by TENG and deep learning

For autonomous vehicles, real-time and accurate longitudinal driving intention recognition is crucial as it effectively enhances driving safety and improves the driving experience. This study proposes a novel data and model hybrid-driven fine-grained longitudinal driving intention prior recognition system (LDIPRS). Firstly, the system integrates a human-pedal interaction sensor (HPIS) based on triboelectric nanogenerators for fine-grained longitudinal driving maneuver monitoring and the channel attention (CA)-enhanced convolutional neural network (CBRCNet). The HPIS, integrated into the vehicle's acceleration and brake pedals, is capable of monitoring driver foot movement information in the form of electrical signals before the vehicle responds, achieving data level advance. The collected electrical signals are fed into the CBRCNet network, which models and learns the mapping relationship between these signals and fine-grained longitudinal driving intentions, leading to model level advances. The HPIS completes the capture of longitudinal maneuver information 541 ms before the driving simulator starts to respond at the data level. At the model level, CBRCNet can achieve a recognition accuracy of 96.1 % based on partial response data (50 ms after starting response) rather than complete response data of the HPIS. Finally, our proposed LDIPRS realizes the recognition of emergency braking, rapid acceleration, normal braking, and normal acceleration in advance by 732 ms, 1035 ms, 1757 ms, and 2227 ms, respectively. This study introduces self-powered, low-cost, highly sensitive triboelectric sensors into the field of intention recognition, and combines the triboelectric sensors with deep learning algorithms to offer a promising solution to improve the safety of autonomous vehicles and the efficiency of intelligent transportation systems.

Large-Scale Assessments for Learning: A Human-Centred AI Approach to Contextualizing Test Performance

Large-scale assessments play a key role in education: educators and stakeholders need to know what students know and can do, so that they can be prepared for education policies and interventions in teaching and learning. However, a score from the assessment may not be enough—educators need to know why students got low scores, how students engaged with the tasks and the assessment, and how students with different levels of skills worked through the assessment. Process data, combined with response data, reflect students’ test-taking processes and can provide educators such rich information, but manually labelling the complex data is hard to scale for large-scale assessments. From scratch, we leveraged machine learning techniques (including supervised, unsupervised, and active learning) and experimented with a general human-centred AI approach to help subject matter experts efficiently and effectively make sense of big data (including students’ interaction sequences with the digital assessment platform, such as response, timing, and tool use sequences) to provide process profiles, that is, a holistic view of students’ entire test-taking processes on the assessment, so that performance can be viewed in context. Process profiles may help identify different sources for low performance and help generate rich feedback to educators and policy makers. The released National Assessment of Educational Progress (NAEP) Grade 8 mathematics data were used to illustrate our proposed approach.

Evaluating the influence of digital strategy on the interplay between quality certification and sales performance using data science and machine learning algorithms

In the context of international trade with information asymmetries, business certifications can serve as signals of product quality and reliability for customers and stakeholders. Recognizing this, the current study unbraces “signalling theory” to explore the impact of digital transformation strategy (DTS) on firm’s international quality certification status and sales performance in the global economy. To this end, treating quality certification endogenously, the study relies on bivariate copula regression modeling of 121429 firms’ responses data extracted from the World Bank’s Enterprise Surveys. Consistent with the hypothesized theoretical predictions, the findings revealed that DTS, in the form of website ownership, has a significantly positive impact on sales performance due to increasing firm’s reach, visibility, engagement, conversion, and retention of customers across different channels and touchpoints. As theoretically conjectured, quality certification is also found to positively impact sales performance by enhancing the firm’s reputation, trustworthiness, and attractiveness in the global marketplace. Most importantly, the study found empirical evidence that DTS significantly moderates the relationship between quality certification and sales performance, such that quality certification has a stronger effect on sales performance for firms with a well-defined DTS (web=1) than those without (web=0); leading therefore to the conclusion that DTS can amplify the signal of quality certification by making it more visible, transparent, and consistent across different digital channels and touchpoints. Hence, the study provides valuable empirical and theoretical insights that inform current business practices, future research, and potential policy measures to support business growth, and enhanced competitiveness in the global market place.

Implementation of a population mental health and wellness text-message service: a mixed-methods study

BackgroundDespite the growing adoption of digital health tools as a means to support mental health, many individuals remain unaware of the variety of mental health resources available to them through this format. To address this knowledge gap, this study advanced the design, development, and implementation of a text-based service called SaskWell to raise awareness of evidence based mental health resources and create more immediate connections to these tools. The two primary objectives of the study were to assess and evaluate the adoption of SaskWell by focusing on user acceptance, satisfaction, and perceived benefit, and to identify factors which contributed to user engagement with the SaskWell text-based service. Both quantitative and qualitative data contributed to the final study results.ResultsThis study utilized a co-designed text-messaging service to provide residents of Saskatchewan an important connection to digital mental health and wellness resources during the height of the COVID-19 pandemic. Using the RE-AIM framework as an implementation guide, four distinct cycles of SaskWell were delivered with modifications to the service in each subsequent cycle based on user engagement, feedback, and the direction of a community advisory group. Quantitative data was collected through user engagement text message response data, along with enrollment and exit surveys, while semi-structured interviews served as the primary means of qualitative data collection. In addition to the quantitative user data, these user interviews resulted in themes exploring motivation to sign-up for the service, perceptions of texting as a mechanism to deliver mental health resources, the impact of SaskWell on mental health and well-being, and beliefs on the future potential of text-based mental health supports.ConclusionsBoth the user engagement survey and the qualitative data supported the worth of ongoing efforts to refine and extend the use of text messaging as a means to engage citizens around the awareness and use of digital mental health and wellness resources. As the pandemic has receded into the background in many peoples’ daily lives, for healthcare providers, and others who continue to be impacted more heavily by the persistent challenges of this global event, this type of service may continue to be timely.