Secondary Dataset Research Articles

Research methods and the use of visual representation in library and information science research

AbstractThe increasing variety of research strategies and data collection techniques in information science, the access to large secondary data sets, and the ubiquity of information visualization call for expanding the classification of research methods and exploring how research is communicated visually. This study examined the relationship between types of data used in empirical research, visualizations, and research methods applied in information science studies. It analyzed 751 research articles published in the Journal of the Association for Information Science and Technology (JASIST) using content analysis and machine learning techniques. The study finds that most empirical studies adopted a quantitative design with data mining, bibliometrics, experiments, and surveys as dominant strategies. The substantial use of secondary data points to the shift in how data are collected in empirical research. The JASIST articles used a variety of visualizations to present research designs and findings, with quantitative and mixed methods studies employing primarily tables and charts and qualitative studies relying more on tables and diagrams. This study uniquely explores the relationship between research methods and visualization. It contributes to the classification of the methods in information science by expanding the range of strategies within the quantitative, qualitative, and mixed methods designs.

The Relationship Between Three Sources of Social Support and Physical Activity Level in Middle-Aged and Older Adults.

This study examined how different sources of social support from family members (excluding partners), friends, and partners were associated with moderate and vigorous leisure-time physical activity (LTPA) among middle-aged and older adults. This study included married participants aged 45 or older (N = 2,155) from the Midlife in the United States secondary data set. Hierarchical linear regression was performed to investigate the relationship between the three sources of social support and moderate LTPA, and separately, with vigorous LTPA. Partner support (b = 0.19, p < .01), family support (b = -0.19, p < .01), and friend support (b = 0.26, p < .001) were all significantly associated with moderate LTPA. Only social support from friends was associated with vigorous LTPA (b = 0.24, p < .001). Our study emphasizes the significance of social support in influencing LTPA behaviors among middle-aged and older adults. Future programs promoting physical activity should incorporate social support from friends to have the greatest impact.

The role of patient and family advisory boards in promoting greater attention to patient social needs by U.S. acute care hospitals.

There is growing attention to individual-level patient social needs such as unstable housing and food insecurity. Such considerations, however, have historically been the purview of public health and have not been a priority of more traditional health care delivery organizations, such as acute care hospitals. The purpose of this study was to examine whether the presence of patient and family advisory boards (PFABs) among acute care community hospitals was associated with screening for and programs to address patient social needs. We used a secondary data set derived from the American Hospital Association's 2020 and 2021 annual surveys, along with multinomial and negative binomial regression models to assess the relationship between the presence/absence of a PFAB and the likelihood of offering and the number of areas addressed by social needs screening programs. More than half (55.9%) of all responding hospitals reported having a PFAB in 2020 (55.9%) and 2021 (52.7%). The presence of a PFAB among hospitals was significantly associated with patient social needs screening, more areas addressed by programs, and more types of partners to address these needs. Community hospitals with a PFAB have more robust programs and partnerships to address patient social needs. Hospital leaders should consider going beyond simply establishing programs to address social needs but also consider comprehensiveness of these programs to recognize the intersectionality of social needs. One way to do this is to formally establish a PFAB to help hospitals better identify and prioritize the needs in local communities and design social needs programs/solutions that are patient- and family-centric.

VALUING INDIGENOUS RIGHTS OVER FOREST GOVERNANCE IN SIX ASEAN MEMBER COUNTRIES

Indigenous forests and indigenous communities have always shared a deep emotional, spiritual, and practical bond. This paper aims to methodically examine the relationship between the level of rights held by indigenous people concerning indigenous forest governance and the ability of the indigenous community in ensuring the long-term viability of its ecosystems. Concurrently, in examining and discussing these issues it is hoped that it can also contribute to the survival of the indigenous people in the future. The elucidation of indigenous people’s rights in the context of indigenous forest governance is based on the empirical scrutiny of the study hypotheses, utilizing the Sikor et al. type of rights. A quantitative analysis through Somer analysis was also carried out on the comprehensive secondary data sets obtained from six selected ASEAN member countries. The findings show that the six selected ASEAN member countries have not yet been granted the third level of rights for indigenous peoples with regard to indigenous forest governance. The statistical evidence strongly suggests the urgent need in these countries to grant indigenous people rights over indigenous forest governance, both within a legal framework and in practical implementation, particularly at the definitional and allocative level of rights. The absence of such rights at the highest levels raises significant concerns about the sustainability of indigenous forests and the welfare of indigenous people across the observed ASEAN member countries.

Crop Concentration and Diversification in Latur District of Maharashtra: A Case Study

The present study is an attempt to explain the crop concentration and diversification in agriculture of the Latur district of Maharashtra. Here, a detailed study has been done to know the dominant crop as well as the ranking of the crops in the cropping pattern of the study area. The study is based on secondary data sets that have been collected from the district statistical handbook 2020-21. Using Bhatia’s method, the crop concentration indices for all blocks of the district have been calculated for crops like Soybean, Sugarcane, Tur, Gram, Jowar, Mung and Udid. Crop concentration and Crop diversification indices have been calculated by using index for all the tehsil of the district. This study attempted to delineate the tehsils of high and low crop diversification and crop concentration.

Exploring the Association Between Structural Racism and Mental Health: Geospatial and Machine Learning Analysis.

Structural racism produces mental health disparities. While studies have examined the impact of individual factors such as poverty and education, the collective contribution of these elements, as manifestations of structural racism, has been less explored. Milwaukee County, Wisconsin, with its racial and socioeconomic diversity, provides a unique context for this multifactorial investigation. This research aimed to delineate the association between structural racism and mental health disparities in Milwaukee County, using a combination of geospatial and deep learning techniques. We used secondary data sets where all data were aggregated and anonymized before being released by federal agencies. We compiled 217 georeferenced explanatory variables across domains, initially deliberately excluding race-based factors to focus on nonracial determinants. This approach was designed to reveal the underlying patterns of risk factors contributing to poor mental health, subsequently reintegrating race to assess the effects of racism quantitatively. The variable selection combined tree-based methods (random forest) and conventional techniques, supported by variance inflation factor and Pearson correlation analysis for multicollinearity mitigation. The geographically weighted random forest model was used to investigate spatial heterogeneity and dependence. Self-organizing maps, combined with K-means clustering, were used to analyze data from Milwaukee communities, focusing on quantifying the impact of structural racism on the prevalence of poor mental health. While 12 influential factors collectively accounted for 95.11% of the variability in mental health across communities, the top 6 factors-smoking, poverty, insufficient sleep, lack of health insurance, employment, and age-were particularly impactful. Predominantly, African American neighborhoods were disproportionately affected, which is 2.23 times more likely to encounter high-risk clusters for poor mental health. The findings demonstrate that structural racism shapes mental health disparities, with Black community members disproportionately impacted. The multifaceted methodological approach underscores the value of integrating geospatial analysis and deep learning to understand complex social determinants of mental health. These insights highlight the need for targeted interventions, addressing both individual and systemic factors to mitigate mental health disparities rooted in structural racism.

Cold Chain Logistics Projects and Performance of Horticulture Export Companies in Rwanda: A Case of Horticulture Exporters Association of Rwanda (HEAR)

Globally, the top three markets for horticulture imports are Europe, the US, and the UAE. The general objective of this study aims to assess the effects of cold chain logistics projects on performance of horticulture exports companies in Rwanda: a case study of Horticulture Exporters association of Rwanda. The specific objectives of this study aim: to assess the effects associated with costs incurred in cold chain logistics on performance of horticulture exports companies in Rwanda, to evaluate the contributions of technology used in cold chain logistics on performance of horticulture exports companies in Rwanda and to determine the effects of geographic location of cold chain stores on performance of horticulture exports companies in Rwanda. Research focused on system theory and resource-based theory. By combining quantitative and qualitative analytic techniques, the researchers used a correlational approach to look for patterns in the interactions between the study's components. The research was based on responses from 92 out of 120 respondents from Rwanda’s horticulture export company. Statistical sampling and other methods were part of the "mixed method" approach utilized in the study. Stratified and random sampling was used to pick the exporters, whilst convenient and purposive sampling were used to select the participants from the cold chain logistics management group. Using both personal and secondary sources, the research aimed to determine the impact of cold chain logistics on the growth of Rwandan horticultural export businesses. For the main data set, which covered the years 2021–2023, researchers utilized closed-ended questions to survey participants. For the secondary data set, they combed through financial records and relevant documents from cold chain facilities. The data was analyzed using version 24 of the Statistical Package for the Social Sciences, a widely used statistical tool in the field of social science. Descriptive data were presented in tables displaying percentages and frequencies. Additionally, regression analysis was conducted to delve deeper into the relationship between the study variables. The findings suggest that there are significant associations between certain factors and performance. Firstly, the cost associated with cold trucks and cold rooms demonstrated a strong positive relationship with performance (β = 0.427, p &lt;0.05). This implies that higher expenditures in maintaining cold trucks and rooms tend to correspond with better performance among horticulture exports companies. Secondly, technology used in cold chain logistics also exhibited a positive relationship with performance, although the effect size was smaller and the p-value slightly higher (β = 0.395, p&lt;0.05). This indicates that employing advanced technology in managing the cold chain logistics process may contribute positively to the performance of these companies. Finally, the geographic location of cold chain stores displayed a significant positive relationship with performance (β = 0.879, p&lt;0.05). The results show that horticultural export companies may boost their performance and profits by investing in cold chain logistics, refrigeration, and holding facilities. If Rwanda wants to make its horticulture exports more competitive, HEAR should weigh the pros and cons of various cold truck and cold room solutions before making a final decision.

COVID-19 Chest X-Ray Case Detection with Ensemble Deep-Learning

Objectives: The objective of this research is to enhance accuracy on the COVID-19 case identification using X-ray imagery by addressing the drawbacks of utilising a single deep learning model, such as overfitting, high variance, and generalisation errors, by generating predictions with numerous frameworks as opposed to one model. Methods: In this study, secondary data sets from a group of experts from Qatar University in Doha, Qatar, and the University of Dhaka in Bangladesh, together with partners from Pakistan and Malaysia, have produced a dataset of 21,135 CXR pictures for COVID-19 patients, as well as pictures of normal and viral pneumonia. The performance of proposed strategy is, EnDL-COVID-19 is compared with three parameters Accuracy, Sensitivity, PPV Assessment. Findings: ENDL-COVID-19 gives good results for COVID-19, instances identification with a performance of 95%, higher than COVID-Net at 93.3%, are according to research observations ENDL-COVID-19 outperformed by a significant margin in a series of experiments using QU&amp;UD test data consisting of 1592 CXR images. It was able to achieve a sensitivity of 96% and a PPV of 94.1% in determining whether or not COVID-19 occurrences were present. Novelty: The proposed weighted averaging ensemble technique, which is aware of the various sensitivities of deep-learning frameworks on various category types, is used to combine multiple snapshot frameworks of COVIDNet, which made a breakthrough in an open sourced COVID-19 case identification approach using chest X-ray pictures analyzed by deep neural networks. Keywords: COVID-19, Ensembling Learning, Deep-Learning, EnDL-COVID-19, X-Ray Pictures

Oncology social workers' involvement in palliative care: Secondary data analysis from nationwide oncology social workers survey.

Social workers are vital in delivering psychosocial services in palliative care, yet their specific roles in palliative oncology remain undefined. This study aimed to delineate the current practice role of oncology social workers involvement in palliative care in the United States. This study utilized a cross-sectional design and involved secondary analysis of data from a nationwide survey focused on workforce conditions for oncology social workers. The participants were social workers who were directly involved in providing care to cancer patients and delivering palliative care services. They completed an online survey in which they indicated the relevance of 91 tasks related to their practice. The survey also collected individual demographic and work-related characteristics. Exploratory factor analysis was used to achieve the study objective. Responses from a secondary data set of 243 oncology social workers involved in palliative care results in a 6-factor solution comprising 34 tasks. These factors were identified as: Therapeutic Interventions for Individuals, Couples, and Families; Facilitate Patient Care Decision-making; Care Coordination; Assessment and Emotional Support; Organization and Community Service; and Equity and Justice. All 6 factors demonstrated good internal reliability, as indicated by Cronbach's alpha scores above 0.70. The findings can be used to develop job descriptions and education for social workers employed in palliative cancer care. The clear role descriptions also make social work visible to other professionals in palliative oncology. By clarifying the roles of oncology social workers, this study contributes to the improvement of palliative care delivery and enhances interprofessional collaboration within cancer care teams.

Strategic planning, budget monitoring and growth optimism: evidence from Australian SMEs

PurposeGrowth optimism, which describes the expected future growth of a firm, is an important but underexplored construct in strategy. This paper aims to assess the planning antecedents of such growth optimism by using a large Australian sample of small enterprises.Design/methodology/approachThe authors use a secondary data set, gathered among Australian small to medium enterprises (SMEs), by the Australian Bureau of Statistics (ABS). The analysis adopts a regression approach including a mediated and a non-mediated path to explore the direct and indirect effects of strategic planning and budgetary planning and management on expected future revenues.FindingsThis paper assesses the implications of concurrent strategic planning and financial management dynamic capabilities on anticipated future revenue growth, an important predisposition dynamic capability. The authors note that this configuration of actions and predisposition aligns closely with the necessary requirements for growth. The findings suggest that firms that use strategic planning and robust budget planning and monitoring processes exhibit higher optimism about future sales growth and firms that effectively configure these planning activities with market development tend to exhibit higher growth and more growth optimism.Research limitations/implicationsIn terms of theoretical contributions, the paper strongly supports the formality view in the formal/informal debates associated with effectuation strategies. The authors suggest that appropriate strategic and budgetary planning and control systems act as a counterbalance to organisational confusion and managerial capriciousness, leading to improved confidence among managers and their employees regarding future resource commitments and plans.Practical implicationsThe findings of the paper are potentially important for both managers and policy makers. For managers seeking to grow their future sales, planning is shown to be an important antecedent activity. The presence of financial and strategic planning may predispose firms to make important investment decisions that drive future growth. Also, a better understanding of the firm’s current and future strategic and financial position may be evidence of effective firm management, a situation that, in turn, drives growth.Social implicationsIn terms of social and policy implications, the data gathered for the survey by the ABS forms a valuable collection of information in relation to business practices. Australian firms are required by law to regularly report budget plans and outcomes. The research suggests that this data can inform policy initiatives, particularly in relation to programmes that may assist small and young firms to undertake prospective strategic and budgetary planning.Originality/valueTo the best of the authors’ knowledge, this is the first paper to investigate the particular configuration of strategic and financial planning and anticipated sales growth in the SME context.

Multilevel Synergy of Information Technology for Operational Integration: Competition Networks and Operating Performance

Firms’ multilevel access to information plays a significant role in improving operating performance. Increasingly, firms are enhancing their operational integration through information technology (IT) as they grapple with intense competition. Competition networks are an essential but often overlooked source of information that, if leveraged correctly, can provide firms with significant competitive and operational advantages. In this study, we develop a multilevel research model of operating performance (firm level) that simultaneously considers the effects of IT for operational integration (ITOI, firm level) and competitive brokerage (competition network level). We explicate how ITOI and competitive brokerage afford firms’ synergy through complementarities and relatedness of competitive actions, information, and resources to improve their operating performance. We assess the model using a 7-year longitudinal secondary data set that includes firms from multiple industries and find support for our thesis that ITOI and competitive brokerage have a synergistic effect on operating performance. Our exploratory analysis uncovers innovation efficiency as a theoretical mechanism underlying the relationships between ITOI, competitive brokerage, and operating performance. Further exploratory analyses with disaggregated measures of ITOI highlight that synergies arise from IT-enabled coordination and integration across the supply chain and within functional areas of an organization. These findings are robust to concerns of endogeneity and alternative model specifications. We make three critical contributions to our collective understanding of the relationship between IT and operating performance—synergy as a means through which operating performance is realized, a multilevel model of relationships, and a nuanced understanding of the relationships between ITOI, competitive brokerage, and operating performance. Overall, we provide a summative view of the multilevel effects of IT on operating performance.

IJCM_305A: Forecasting Dengue and Chikungunya incidence using Time Series Analysis Modelling

Background: In recent years, the rise in vector-borne diseases, particularly Dengue and Chikungunya, has posed a significant public health challenge globally. Epidemiological data from the Integrated Disease Surveillance Program (IDSP) is a useful resource for forecasting and early outbreak identification by applying effective models like Sessional Autoregressive Integrated Moving Average (SARIMA) models has proven effective in capturing temporal dependencies in various fields. Objective: To forecast the dengue and chikungunya cases in India using Seasonal Autoregressive Integrated Moving Average models. Methodology: we preprocessing the IDSP dataset to extract relevant temporal patterns and trends. Subsequently, SARIMA models are applied to capture the time dependent dynamics of disease occurrences. The model’s performance is evaluated through rigorous validation processes, including cross-validation techniques, to ensure its reliability and generalizability. Results: This study considered a secondary data set of weekly dengue and chikungunya cases over the period of 2019 to 2023. The time plot shows several spikes in the data, indicating periods where the number of dengue cases increased significantly in the most recent years. Forecast of the future dengue and chikungunya cases was done for the next one year (2024). we observed that the number of dengue and chikungunya cases has fluctuated over time but has generally remained within a certain range. The forecast predicts a significant increase in cases as we move towards the latter half of 2024, with a very sharp spike towards the end of the forecast period. Conclusion: In conclusion, utilizing a secondary dataset spanning from 2019 to 2023, this study employed time series analysis to forecast future Dengue and Chikungunya cases. The forecast predicts a significant increase in dengue cases during 2024 which can lead to potential implications for public health practitioners, policymakers, and researchers involved in infectious disease management.

Rural-urban differences in out-of-network treatment initiation and engagement rates for substance use disorders.

To examine rural-urban disparities in substance use disorder treatment access and continuation. We analyzed a 2016-2018 U.S. national secondary dataset of commercial insurance claims. This cross-sectional study examined individuals with a new episode of opioid, alcohol, or other drug use disorders. Treatment initiation and engagement rates, and rates of using out-of-network providers for these services, were compared between rural and urban patients. We included individuals 18-64 years old with continuous employer-sponsored insurance. Patients in rural settings experienced lower treatment initiation rates for alcohol (36.6% vs. 38.0%, p < 0.001), opioid (41.2% vs. 44.2%, p < 0.001), and other drug (37.7% vs. 40.1%, p < 0.001) use disorders, relative to those in urban areas. Similarly, rural patients had lower treatment engagement rates for alcohol (15.1% vs. 17.3%, p < 0.001), opioid (21.0% vs. 22.6%, p < 0.001), and other drug (15.5% vs. 17.5%, p < 0.001) use disorders. Rural patients had higher out-of-network rates for treatment initiation for other drug use disorders (20.4% vs. 17.2%, p < 0.001), and for treatment engagement for alcohol (27.6% vs. 25.2%, p = 0.006) and other drug (36.1% vs. 31.1%, p < 0.001) use disorders. These findings indicate that individuals with substance use disorders in rural areas have lower rates of initial and ongoing treatment, and are more likely to seek care out-of-network.

Enhancing Classification Performance through FeatureBoostThyro: A Comparative Study of Machine Learning Algorithms and Feature Selection

Early-stage prediction of a disease is an important and challenging task. The application of machine learning techniques is playing an important role in this era. Thyroid is one of the chronic endocrine diseases, and approximately 42 million people in India are affected by this disease. This paper presents a comprehensive investigation into the enhancement of classification performance through the novel ‘FeatureBoostThyro’ (FBT) model. The study evaluates various machine learning algorithms, including stochastic gradient descent (SGD), K nearest neighbor (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM), in conjunction with diverse feature selection methods. The research systematically explores the impact of feature selection techniques such as information gain, relief F, chi-square, gini index, forward selection, backward selection, recursive feature elimination, and LASSO on model performance across the chosen algorithms. The analysis reveals notable variations in performance metrics, including accuracy, precision, recall, and F1-score, providing valuable insights into the interplay between algorithm and feature selection. One main contribution of this research is the introduction of the FBT model, which consistently outperforms other models across various feature selection methods, making it a promising tool for addressing complex classification tasks. The findings contribute to a broader understanding of model selection and optimization in machine learning applications. The proposed model undergoes evaluation using two distinct datasets: the primary dataset acquired from Lata Mangeshkar Hospital in Nagpur and the secondary dataset obtained from the UCI dataset.

Decision support for improved construction traffic management and planning

Densifying cities continuously call for new construction, renovation and demolition projects, each generating vast amounts of heavy goods vehicle (HGV) transports. However, how construction transportation affects the urban traffic network remains largely unexplored. This study addresses spatiotemporal network impacts from construction transport, by leveraging traditional traffic and transport simulation. To this end, a framework is presented including (i) a simulation model to compute traffic effects caused by varying off-site construction site transport demands, and (ii) conceptual applications of the simulation model showing construction logistic planning strategies to mitigate congestion disturbances. Simulations are conducted in MATSim using detailed secondary datasets describing site-specific transport arrivals from a case of six projects in Norrköping, Sweden. Subsequently, increasing transport demands were assessed on various time-window arrival scenarios against the baseline schedule, which can be used as decision support in urban planning. Results highlight how rigorous construction transport planning avoiding peak-traffic hours can significantly alleviate traffic congestion. This study also emphasizes the need to combine all simultaneous construction projects’ demands when evaluating disturbances on city-level, alongside the impact on individual links and microenvironments. This study adds knowledge by visualizing the traffic impact during urban transformation.

Retrospective Cohort Studies in Craniofacial Outcomes Research: An Epidemiologist's Approach to Mitigating Bias.

Retrospective cohort studies, defined as a follow-up study in which outcome occurred prior to study onset, are common in craniofacial outcomes research and will continue to be prevalent given the increasing availability of secondary datasets and inherent prospective study limitations. However, if available data are not adequately measured, or necessary variables are absent, retrospective cohort studies can be particularly prone to bias. This brief communication aims to highlight the primary sources of bias, including measurement error, selection bias, and confounding. Each source is clearly defined, examples pertinent to craniofacial outcomes are provided, and mitigation strategies are discussed.

Relationships Between Children’s Literacy Comprehension and Their Achievement in Numeracy and Ethno-Math at Lower Primary Schools in Uganda

The article examined the relationships between children’s literacy comprehension and their competence/achievement in numeracy and Ethno-math at lower primary schools in Uganda using the UWEZO UGANDA (2021) secondary dataset collected by a cross-sectional survey design from a sample of 14,553 pupils. The quantitative data were analyzed using Pearson Product Moment of correlation coefficient and regression analyses to establish the relationship between children’s literacy comprehension and their competencies in numeracy/ethno-math. Findings showed that there was a statistically significant relationship (r = 0.539, p &lt; 0.001) between letter recitation and number recognition, literacy level in English and numeracy level (r = 0.768, p &lt; 0.001), numeracy level and ethno-math (r = −0.612). Although findings show a positive relationship between letter recitation and number recognition, literacy level in English, and numeracy level, there is a negative relationship between numeracy level and ethno-math. The study concluded that children’s ability to recite letters positively influences their ability to recognize numbers. Furthermore, children’s ability in literacy comprehension positively influences their abilities in carrying out numeracy. It was also concluded that children’s ability to work out ethno-math does not necessarily mean their ability to carry out numeracy.

No evidence for relationship between paternal post-partum depressive symptoms and testosterone or cortisol in first-time fathers.

Male life history strategies are regulated by the neuroendocrine system. Testosterone (T) and cortisol regulate male behaviors including parenting and facilitate managing tradeoffs at key transitions in development such as first-time fatherhood. Both hormones demonstrate marked fluctuations in the postnatal period, and this presents an opportunity to investigate the role of T and cortisol in postpartum depressive symptoms-comparably less studied in fathers than in mothers in the evolutionary literature. Prior work on depressive symptoms has yet to integrate insights from the "dual hormone hypothesis (DHH)" which has focused on how T and cortisol interact to jointly regulate traits associated with dominance and status-seeking (i.e., mating effort) but has yet to be included in models of parenting effort. In this research, we use secondary data to investigate the relationship between DHH and traits ostensibly opposed to status seeking (i.e., depressive symptoms). First-time fathers (n = 193) provided morning saliva samples 10 months following parturition and reported on the presence of depressive symptoms (BDI-II). Responses were decomposed into three factors: cognitive, affective, and somatic. Using hybrid latent variable structural equation modeling, we did not find evidence that T predicted variability in cognitive, affective, or somatic depressive symptom factors. We found a null effect for cortisol as well. Finally, we could not find evidence that the DHH variable (T × cortisol interaction) predicted any variability in cognitive, affective, or somatic depressive symptoms. While we did not find evidence to support our hypotheses using a secondary data set, this study contributes to research on the neuroendocrinology of depression in fathers. Discussion focuses on the limitations of sample demographics, timing of saliva and self-report collection, and the lack of extant theory specific to paternal postpartum depression.

Routinely collected burn clinical data in Canada: Determining the knowledge gap

Unlike other developed countries that hold national burn registries to monitor burn injury and care, Canada relies on single-centre secondary datasets and administrative databases as surveillance mechanisms. The objective of this study was to determine the knowledge gap faced in Canada for not having a dedicated burn registry. A comprehensive scoping review was conducted to identify the burn literature that has arisen from secondary datasets in Canada. Literature of all study designs was included with the exception of case reports and cases series. Once data extraction was concluded, a thematic framework was constructed based on the information that arose from nations that hold national burn registries. Eighty-eight studies were included. Twelve studies arose from national datasets, and 18 from provincial databases, most of which were from Ontario and British Columbia. Only seven studies were conducted using a combination of Canadian units’ single-centre datasets. The majority of included studies (58%) resulted from non-collaborative use of single-centre secondary datasets. Research efforts were predominantly conducted by burn units in Ontario, British Columbia, Manitoba and Alberta. A significant number of the included studies were outdated and several provinces/territories had no published burn data whatsoever. Efforts should be made towards the development of systems to surveil burn injury and care in Canada. This study supports the development of a nation-wide burn registry to bridge this knowledge gap.

Digitization as an Adaptation and Resilience Measure for MSMEs amid the COVID-19 Pandemic in Japan: Lessons from the Food Service Industry for Collaborative Future Engagements

Restrictions and other stringent countermeasures due to the COVID-19 pandemic have wreaked havoc on many enterprises over the past few years. The effect on Micro, Small, and Medium Enterprises (MSMEs) is believed to be far-reaching. In most countries, the MSME sector provides the maximum number of jobs, and its resilience is core to the livelihood of that society; Japan is no exception to this. The food service industry was classified as an essential service during the pandemic, and advancements in technology and other digital innovations were seen as revolutionary to many enterprises to diversify their business models to reduce losses. This study reviews how MSMEs in the food service industry utilized technologies and innovations to adapt to the challenges and enhance their resilience. The paper reviews secondary datasets to discuss the perspectives of MSMEs and the impact of the pandemic, as well as utilizing Gotanda Eats as a case example in Japan to understand the method of digital adaption towards resilience. The Gotanda Eats platform was crucial for the survival of the five restaurants involved because it transformed their old business operations into the one that connects them to customers while observing infection prevention protocols. Despite the challenges that are visible in the delivery aspect of the platform, the operation shows that digitization and digital transformation are vital for MSMEs in times of crisis. The World Economic Forum 2021 report has focused on digital power concentration as a new risk, the current study shows that digitization in food-related MSMEs was a critical survival strategy. The study recommends that local authorities take a keen interest in supporting local businesses during hard times.