Inform Decision Makers Research Articles

Evaluation of the feasibility, acceptability, and impact of Group Antenatal Care at the health post level on continuation in antenatal care and facility based delivery in Ethiopia using a cluster randomized stepped-wedge design: Study protocol

Background Adequate antenatal care (ANC) and facility-based delivery are linked to improved maternal and neonatal outcomes. Adequate antenatal care attendance and facility birth rates are increasing in Ethiopia but remain well below national goals and global recommendations. Group ANC (G-ANC), when implemented at higher level facilities, is associated with improved quality and experience of ANC, and increased ANC retention and facility-based delivery. The objectives of the study are to assess the feasibility, acceptability, and impact of G-ANC implemented at lower-level facilities (health posts) on ANC continuation and facility-based delivery. Methods G-ANC will first be piloted in five purposively selected health posts. The study will then use a stepped-wedge design in 36 health posts under six health centers, with randomization of the order of the start of the intervention done at the health center level (clusters). The design will include three time periods: first is a six-month control period with no G-ANC implementation, followed by another six months period where G-ANC will be introduced in half (n=18) of the study health posts, then final six months where G-ANC will be implemented in the remaining 18 health posts. Quantitative and qualitative data collection approaches will be used. The study has “pause and reflect” points designed to iterate on the intervention before rolling out to the next set of sites. Qualitative research will be conducted using in-depth interviews with pregnant women, health care workers, facility managers, and regional health managers. 770 women will be enrolled across all phases. Conclusions The study will inform decision makers locally and globally on whether G-ANC is a feasible service delivery model at the health post level. Effectiveness of G-ANC at increasing ANC retention and facility-based delivery will be reported, as well as its acceptability to pregnant women and Health Extension Workers. Registration NCT05054491, ClinicalTrials.gov (September 23rd 2021).

Clinical and socioeconomic predictors of shunt malfunction in the pediatric emergency department.

The aim of this study was to delineate the clinical and socioeconomic variables associated with shunt revision in pediatric patients presenting to the emergency department (ED) with concerns of ventricular shunt malfunction. A retrospective analysis of pediatric ED consultations for shunt malfunction over a 1-year period was conducted, examining clinical symptoms, radiographic findings, and socioeconomic variables. Sensitivities, specificities, and positive and negative predictive values were calculated for each presenting symptom collected. Logistic regression models were used to estimate the odds ratios for shunt revision based on these variables, and multivariate analyses were used to adjust for potential confounders. Of the 271 ED visits from 137 patients, 19.2% resulted in shunt revision. Increased ventricle size on imaging (OR 11.38, p < 0.001), shunt site swelling (OR 9.04, p = 0.01), bradycardia (OR 7.08, p < 0.001), and lethargy (OR 5.77, p < 0.001) were significantly associated with shunt revision. Seizure-like activity was inversely related to revision needs (OR 0.24, p < 0.001). Patients with private or self-pay insurance were more likely to undergo revision compared with those with public insurance (p = 0.028). Multivariate analysis further confirmed the significant associations of increased ventricle size, lethargy, and bradycardia with shunt revision, while also revealing that seizure-like activity inversely affected the likelihood of revision. Patients with severe cognitive and language disabilities were more likely to be admitted to the hospital from the ED but were not more likely to undergo revision. Clinical signs such as increased ventricle size, shunt site swelling, bradycardia, and lethargy may be strong predictors of the need for shunt revision in pediatric patients presenting to the ED with concerns of shunt malfunction. Socioeconomic factors play a less clear role in predicting shunt revision and admission from the ED; however, the nature of their influence is unclear. These findings can help inform clinical decision-making and optimize resource utilization in the ED.

Problems and Challenges Faced By Franchise Model

Abstract: This research report titled "Problems and Challenges faced by Franchise Model" delves into the intricacies of the franchise business model, it is aiming to know the problems and challenges faced by franchisee, to know how franchisee taker tackled problems and challenges, to know which kind of strategy use franchise taker for marketing and improvement. The study employs a descriptive and research design with a focus on individual franchise holders in Ahmedabad, utilizing primary data collection methods such as Google Forms and personal interviews. Secondary data from previous research papers and magazines supplements the analysis. Statistical tools like Microsoft Excel 2021, SPSS, and Google Forms aid in data analysis. Despite constraints like limited time and geographic scope, the study offers valuable insights for stakeholders in the franchise industry. Findings from this research can enhance operational efficiency, inform decision-making, and contribute to sustainable growth within the franchise model. In conclusion, while operating a franchise presents various challenges, strategic focus on areas such as maintaining consistency in quality, investing in training and skill development, and enhancing online presence can greatly contribute to success. Franchisees who prioritize adherence to brand standards, adaptability to local markets, and foster strong relationships with both franchisors and employees stand a better chance of overcoming barriers and thriving in their businesses

A comprehensive review of strategic management practices in satellite telecommunications, highlighting the role of data analytics in driving operational efficiency and competitive advantage

Satellite telecommunications play a crucial role in modern connectivity, enabling global communication, broadcasting, and internet access. This paper presents a comprehensive review of strategic management practices in the satellite telecommunications industry, with a particular focus on the role of data analytics in enhancing operational efficiency and achieving competitive advantage. The satellite telecommunications sector is characterized by rapid technological advancements, evolving market dynamics, and increasing demand for bandwidth-intensive services. To thrive in this competitive landscape, companies in the industry must adopt strategic management practices that enable them to effectively leverage their resources, capabilities, and market opportunities. One of the key strategic management practices in satellite telecommunications is the integration of data analytics into business operations. Data analytics encompasses the collection, analysis, and interpretation of large volumes of data to extract valuable insights and inform decision-making. By harnessing the power of data analytics, satellite telecommunications companies can optimize their network performance, enhance customer experience, and drive innovation. Data analytics can also help companies in the satellite telecommunications industry to identify new market trends, customer preferences, and competitive threats. By analyzing data from various sources, including satellite sensors, customer feedback, and market research, companies can gain a deeper understanding of their operating environment and make informed strategic decisions. Furthermore, data analytics can enable satellite telecommunications companies to enhance their operational efficiency by optimizing resource allocation, minimizing downtime, and improving network reliability. By using predictive analytics, companies can anticipate equipment failures and proactively address potential issues, thereby reducing maintenance costs and improving service quality. In conclusion, this paper highlights the importance of strategic management practices in the satellite telecommunications industry, with a focus on the role of data analytics in driving operational efficiency and competitive advantage. By adopting data-driven decision-making processes, satellite telecommunications companies can position themselves for long-term success in a rapidly evolving industry landscape.

Implementation of quality control systems based on machine vision and web technologies

The development of Computer Vision technologies has seen widespread adoption in various fields over the last time, including medicine, manufacturing, transportation, logistics, agriculture, and chemical industry. Research analysis indicates that Computer Vision and its practical implementation, known as Machine Vision, are widely used for defect detection in production and equipment. Despite significant research progress in this field in recent years, many unresolved issues remain concerning the quality and dimensions of input data, selection of image recognition models and their training, and the accuracy of results. Integration of machine vision system elements for information exchange and decision-making poses a significant challenge. This paper presents one approach to addressing this issue. The aim of the paper is to explore the implementation approach of quality control systems using web technologies. The task of detecting defects in printed circuit boards (PCBs) is common in many manufacturing processes. PCB inspection involves comparing the current PCB sample with a defect-free reference. Essentially, this task involves comparing two images and identifying differences between them. Defect recognition is based on the Structural Similarity Index (SSIM), which computes the degradation component reflecting the difference between images and the similarity component. The SSIM metric is already implemented in image processing libraries. This work utilizes calculations using OpenCV and scikit-image image libraries. To automate the PCB comparison process and address deployment and integration challenges at the SCADA level, a web application has been developed. This web application allows users to select a reference PCB image and upload the current PCB image for inspection. For the client-side implementation, Vue.js was chosen due to its popularity and efficiency in developing web interfaces, particularly Single Page Applications (SPAs). Vue.js's reactive system ensures automatic updates to reflect changes in data state. For the server-side implementation, Flask – a Python framework, was used, facilitating integration with the PCB comparison algorithm as both utilize Python. According to the available features, operators can upload two images – a reference PCB image and the PCB image to be compared with the reference. After uploading, the images are displayed in the system interface. On the server side, the received data is decoded and compared using the appropriate algorithms. After comparison, the results are encoded in base64 format and sent back to the client-side for display, allowing the operator to assess the similarity or differences between the PCBs. The application can operate in both automatic and manual modes. Therefore, an analysis of the challenges encountered in implementing and deploying quality control systems based on computer vision methods has been developed. The use of web technologies, particularly Python frameworks, for integrating developed models is proposed. This approach is demonstrated using the example of PCB defect recognition tasks.

Taming Misinformation: Fake Review Detection on Social Media platform using Hybrid Ensemble Technique

In today's digital world, we witness exponential growth in the generation of textual content on a daily basis. However, the widespread dissemination of information through social media, online forums, and news websites has given rise to the proliferation of fake views, opinions, and reviews, posing a significant challenge in the battle against misinformation and manipulation. Machine Learning has become increasingly integral to real-world online activities, particularly in the area of Artificial Intelligence. Traditional methods often struggle to keep pace with the relentless creation of internet data. Consequently, short text processing has emerged as a new domain for the application of Machine Learning. This is where sentiment analysis come to the forefront, offering potent tools for discerning the authenticity of online content. Detecting and combating these fabricated sentiments are crucial for preserving the integrity of information and ensuring informed decision-making. This work focus on the previously unexplored area of user comments on review data. By leveraging N-gram technique and hybrid ensemble classification approaches, the research addresses critical issues in fake reviews classification, and sentiment analysis. The aim of this work is to detect fake reviews with limited text using NLP feature extraction, and hybrid ensemble classification algorithms, ultimately contributing to the enhancement of information integrity and decision-making in the digital age.

Adults’ experiences of moral regret

Individuals may need to reflect on their past decisions and this process can often be accompanied by feelings of regret. Recognizing and acknowledging these emotions can facilitate personal growth and inform future decision-making. This study aims to explore adults’ experiences of moral regret. Phenomenological design, one of the qualitative research approaches, was used in the study. The study involved 15 adult individuals selected through snowball sampling. Data were collected through a semi-structured interview form developed by the researcher and analyzed through thematic analysis using Maxqda 2020. As a result of the analysis, the themes of remorse, nostalgia, and grief emerged. The research findings indicate that adults experience spiritual regrets intensely. The research reveals that adults see spiritual regrets as a learning tool. Based on the results of the research, suggestions for researchers and practitioners were made. Keywords: grief; Moral; nostalgia; regret; remorse.

Analyzing Flood Damage and Mapping Flood Hazard Zones Using AHP Model: A Case Study of Pol-e-Alam, Logar Province, Afghanistan

This research aims to evaluate the impact of the most recent floods that occurred on August 20, 2022, in Logar province in southern Afghanistan. For this purpose, changes in land use and land cover (LULC) of the study area were created from the Sentinel-2 image with a spatial resolution of 10 meters. To achieve this, the study utilized Sentinel-2 images to analyze LULC changes before and after the flood event and employed a support vector machine for supervised classification. The study also applied the analytical hierarchy process (AHP) to evaluate the future risks of flooding in the study area, focusing on factors related to hydrological phenomena. Overall, the study demonstrates the effectiveness of geospatial technologies and remote sensing in assessing the impacts of floods and creating flood risk maps. This can significantly reduce the consequences of flooding and inform decision-making for disaster management and mitigation.

Unpacking Smart Campus Assessment: Developing a Framework via Narrative Literature Review

The emergence of the smart campus approach for university campuses addresses the digital transformation needs of higher education institutions, driven by the increasing capability and popularity of digital technologies like artificial intelligence and the internet of things. While existing research has laid a foundation for conceptualizing smart campuses and developing implementation frameworks, a significant gap remains in understanding how to assess a university campus’s ‘smartness’ effectively. The lack of a robust assessment framework makes it challenging to gauge the effectiveness of smart campus initiatives and identify areas for improvement. This study aims to bridge this research gap by identifying key indicators for evaluating the ‘smartness’ of university campuses. Using a narrative literature review method, the study comprehensively reviews the recent literature on smart campuses, organizational management, and societal applications, focusing on identifying pertinent indicators. By incorporating insights from different domains, the study presents a holistic understanding of the indicators necessary for assessing the ‘smartness’ of university campuses through the proposed smart campus assessment framework. The framework and the insights generated inform researchers and decision-makers in assessing and monitoring the effectiveness of smart campuses.

Environmental Management Accounting Information and Environmental Performance, the Mediating Effect of Environmental Decision Quality

In recent times, there has been a heightened focus from both governments and societies towards evaluating Organisations’ Environmental Performance (OEP). Environmental Management Accounting (EMA) is pivotal in elevating OEP as it offers a holistic perspective to elucidate the environmental impacts and associated costs of diverse business activities. This research delves into the dynamic interplay between EMA information and the OEP. The study entails the administration of a questionnaire to Jordanian organisations listed on the Amman Stock Exchange. The research adopts the Resource-Based View Theory. The study establishes statistically significant positive connections between these variables by employing partial least square structural equation modelling (SEM) for data analysis. Central to this investigation is identifying Environmental Decision Quality (EDQ) as a mediating capability. This capability is the mechanism for translating the relationship between EMA information and OEP. This finding underscores the pivotal role of in-formed decision-making in converting such information into tangible improvements in environmental performance. The research makes a notable contribution by expanding the understanding of EMA, highlighting its informational value beyond merely the practice considerations. Moreover, findings provide invaluable guidance for policymakers and regulatory bodies striving to promote sustainable business practices, especially pertinent in the context of developing nations.

Nonspecific Effects of the Bacillus Calmette-Guérin Vaccine in Portuguese Children Under 5 Years of Age: Protocol for a Population-Based Historical Birth Cohort Study.

The Bacillus Calmette-Guérin vaccine (BCG) against tuberculosis (TB) shows beneficial nonspecific effects, which are likely related to innate immune training. Until 2016, a single BCG dose was administered to all newborns in Portugal. In July 2016, a clinical guideline established that only children under 6 years belonging to high-risk groups should receive BCG. This might have prevented nonvaccinated children from developing trained immunological responses as effectively as BCG-vaccinated children. This study aims to investigate if there is variation in TB-related and all-cause mortality, and severe, moderate, or mild morbidity in children under 5 years of age, and whether such variation might be explained by the BCG vaccination policy change in 2016. This population-based historical birth cohort study includes children under 5 years of age born in Portugal between July 1, 2010, and June 30, 2021. Newborns with low birth weight, premature status, or known or suspected HIV infection are excluded. The follow-up period is until the completion of 5 years of age or the end of follow-up (June 30, 2021). The study will use secondary data from the National Health Service user registry, death certificate database, vaccination registry, communicable diseases surveillance system, TB surveillance system, diagnosis-related group information system for hospital admissions and emergency department visits, and primary health care information system. The data will be linked. Primary outcomes include person-time incidence rates of death (all causes and TB), TB diagnosis, and all causes and some specific causes of severe, moderate, or mild morbidity, and the incidence rate ratio of nonvaccinated to BCG-vaccinated children. We will compare the probability of surviving the first and fifth years of life or of not having severe, moderate, or mild morbidity during the follow-up period according to exposure (BCG vaccinated or nonvaccinated, number of doses, and time from birth until the first dose), using the log-rank test for assessing differences in survival rates between exposed and nonexposed children and hazard ratios for quantifying the differences. Moreover, we will perform a proportional hazards regression analysis. Ethics approval has been obtained. In March 2022, database owners were contacted to present the project and discuss the request for data. A unique identifier will be used. In July 2023, a process of redefinition of the variables per database was initiated. Data were received in October and November 2023. In November 2023, further work was conducted. By April 2024, we expect to start analyzing the full data set. The results will contribute to the accumulating body of knowledge and might have relevance to guide global BCG vaccination policy. Data linkage can contribute to a swifter mechanism to use available health data to conduct population-based studies and inform policy decision-making. ClinicalTrials.gov NCT05471167; https://clinicaltrials.gov/study/NCT05471167. DERR1-10.2196/55332.

Investigation of the impact mechanism of humble leadership on employee silence

BACKGROUND: “Silence” is a common phenomenon in the operation of enterprises, where some instances of silence can uphold organizational harmony, while other forms can exacerbate organizational issues, hindering intra-organizational information transfer and decision-making and affecting organizational innovation. OBJECTIVE: This study aims to explore the relationship between humble leadership style and employee silent behavior, verifying the mediating role of psychological contracts in this relationship. METHODS: Based on the social exchange theory, hypotheses are validated using descriptive statistical analysis, correlation analysis and the SEM structural equation modeling testing. RESULTS: The findings indicate a significant negative correlation between humble leadership and employee silence. Humble leadership significantly inversely predicts permissive and defensive dimensions of employee silence, while the correlation with the prosocial dimension is insignificant. Moreover, psychological contracts partially mediate the impact of humble leadership on permissive silence and defensive silence among employees. CONCLUSION: The study helps managers comprehend how humble leadership, characterized by the traditional Chinese “humility” culture, affects employee silence. Especially within Chinese enterprises, where employees are influenced by traditional Chinese thought and exhibit historical dependency on silent behavior, probing whether the humble leadership style can effectively encourage employees to provide constructive suggestions for organizational development is particularly meaningful.

The Impact of Interactive Mass Media in Voters' Education for the 2023 General Election in Nigeria: Overcoming Challenges and Fostering Informed DecisionMaking

This paper explores the significant role of mass media in voters' education, focusing on its potential impact on the upcoming 2023 general election in Nigeria. Drawing on the Agenda Setting Theory and the capabilities of interactive communication, the study examines how mass media can effectively bring issues to the forefront of public attention, fostering engagement and understanding among diverse audiences. The inclusive nature of various mass media outlets, coupled with technological advancements enabling interactive features, presents a promising avenue for overcoming barriers in educating both literate and non-literate individuals. However, challenges such as perception biases, rural disparities, and practical hindrances must be addressed to fully unlock the potential of mass media in voters' education. The paper emphasizes the need for high-quality, carefully crafted messages delivered through collaboration among media organizations and relevant stakeholders to ensure the success of voters' education initiatives. Keywords: Mass media, Voters' education, 2023 general election, Agenda Setting Theory, Interactive communication, Inclusive approach and Perception biases

Supporting Student Competencies in Graph Reading, Interpretation, Construction, and Evaluation.

Graphs are ubiquitous tools in science that allow one to explore data patterns, design studies, communicate findings, and make claims. This essay is a companion to the online, evidence-based interactive guide intended to help inform instructors' decision-making in how to teach graph reading, interpretation, construction, and evaluation within the discipline of biology. We provide a framework with a focus on six instructional practices that instructors can utilize when designing graphing activities: use data to engage students, teach graphing grounded in the discipline, practice explicit instruction, use real world "messy" data, utilize collaborative work, and emphasize reflection. Each component of this guide is supported by summaries of and links to articles that can inform graphing practices. The guide also contains an instructor checklist that summarizes key points with actionable steps that can guide instructors as they work towards refining and incorporating graphing into their classroom practice and emerging questions in which further empirical studies are warranted.

Unlocking the Value: Quantifying the Return on Investment of Hospital Artificial Intelligence

PurposeA comprehensive return on investment (ROI) calculator was developed to evaluate the monetary and nonmonetary benefits of an artificial intelligence (AI)–powered radiology diagnostic imaging platform to inform decision makers interested in adopting AI. MethodsA calculator was constructed to calculate comparative costs, estimated revenues, and quantify the clinical value of using an AI platform compared with no use of AI in radiology workflows of a US hospital over a 5-year time horizon. Parameters were determined on the basis of expert interviews and a literature review. Scenario and deterministic sensitivity analyses were conducted to evaluate calculator drivers. ResultsIn the calculator, the introduction of an AI platform into the hospital radiology workflow resulted in labor time reductions and delivery of an ROI of 451% over a 5-year period. The ROI was increased to 791% when radiologist time savings were considered. Time savings for radiologists included more than 15 8-hour working days of waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time. Using the platform also provided revenue benefits for the hospital in bringing in patients for clinically beneficial follow-up scans, hospitalizations, and treatment procedures. Results were sensitive to the time horizon, health center setting, and number of scans performed. Among those, the most influential outcome was the number of additional necessary treatments performed because of AI identification of patients. ConclusionsThe authors demonstrate a substantial 5-year ROI of implementing an AI platform in a stroke management–accredited hospital. The ROI calculator may be useful for decision makers evaluating AI-powered radiology platforms.

Reviewing big data's role in the digital economy: USA and Africa focus

In the dynamic landscape of the digital economy, the integration and utilization of Big Data have emerged as pivotal factors influencing economic growth and innovation. This paper undertakes a comprehensive review of the contrasting roles that Big Data plays in shaping the digital economies of the United States and Africa. By examining the unique socio-economic contexts and infrastructural landscapes of these regions, the study seeks to shed light on the divergent trajectories of Big Data adoption and its impact on economic development. In the context of the United States, where a mature digital infrastructure prevails, Big Data has become a linchpin for innovation, competitiveness, and economic resilience. The paper delves into specific industries, such as technology, finance, and healthcare, to elucidate how Big Data analytics is leveraged to drive efficiency, inform decision-making, and foster disruptive advancements. The study extends beyond the corporate sector to explore the implications of Big Data on policy formulation and governance. Conversely, the study shifts its focus to Africa, a region characterized by diverse economic landscapes and varying degrees of technological development. The analysis explores the challenges and opportunities posed by Big Data adoption in Africa, highlighting the potential for leapfrogging traditional development stages through strategic data-driven initiatives. Additionally, the paper addresses issues related to data privacy, security, and the need for collaborative efforts to bridge the digital divide. Ultimately, this comparative review aims to contribute to a nuanced understanding of the role of Big Data in shaping the digital economies of the USA and Africa. By delineating the divergent trajectories, challenges, and opportunities, the study provides a foundation for informed policy recommendations, fostering inclusive and sustainable economic development in the digital age.

Preparing healthcare facilities in sub-Saharan Africa for future outbreaks: insights from a multi-country digital self-assessment of COVID-19 preparedness

BackgroundDespite previous experience with epidemics, African healthcare systems were inadequately prepared and substantially impacted by the coronavirus disease 2019 (COVID-19) pandemic. Limited information about the level of COVID-19 preparedness of healthcare facilities in Africa hampers policy decision-making to fight future outbreaks in the region, while maintaining essential healthcare services running.MethodsBetween May–November 2020, we performed a survey study with SafeCare4Covid − a free digital self-assessment application − to evaluate the COVID-19 preparedness of healthcare facilities in Africa following World Health Organization guidelines. The tool assessed (i) COVID-19-related capabilities with 31 questions; and (ii) availability of essential medical supplies with a 23-supplies checklist. Tailored quality improvement plans were provided after assessments. Information about facilities’ location, type, and ownership was also collected.ResultsFour hundred seventy-one facilities in 11 African countries completed the capability assessment; 412 also completed the supplies checklist. The average capability score on a scale of 0–100 (n=471) was 58.0 (interquartile range 40.0–76.0), and the average supplies score (n=412) was 61.6 (39.0–83.0). Both scores were significantly lower in rural (capability score, mean 53.6 [95%CI:50.3–57.0]/supplies score, 59.1 [55.5–62.8]) versus urban facilities (capability score, 65.2 [61.7–68.7]/supplies score, 70.7 [67.2–74.1]) (P<0.0001 for both comparisons). Likewise, lower scores were found for public versus private clinics, and for primary healthcare centres versus hospitals. Guidelines for triage and isolation, clinical management of COVID-19, staff mental support, and contact tracing forms were largely missing. Handwashing stations were partially equipped in 33% of facilities. The most missing medical supply was COVID-19 specimen collection material (71%), while 43% of facilities did not have N95/FFP2 respirators and 19% lacked medical masks.ConclusionsA large proportion of public and private African facilities providing basic healthcare in rural areas, lacked fundamental COVID-19-related capabilities and life-saving personal protective equipment. Decentralization of epidemic preparedness efforts in these settings is warranted to protect healthcare workers and patients alike in future epidemics. Digital tools are of great value to timely measure and improve epidemic preparedness of healthcare facilities, inform decision-making, create a more stakeholder-broad approach and increase health-system resilience for future disease outbreaks.

Patient satisfaction and their determinants in outpatient department of a tertiary public hospital in Nepal: a cross-sectional study

BackgroundPatient satisfaction is a vital metric for assessing healthcare quality and delivering patient-centered care. It can predict service utilization patterns by determining healthcare users’ contentment with their providers. Consequently, evaluating patient satisfaction and its underlying factors is crucial to maintaining the quality of healthcare services. The present study aimed to assess patient satisfaction and its determinants in a tertiary care public hospital in Nepal. In this research, a cross-sectional design was employed to examine patient satisfaction within the Outpatient Department of Mental Hospital Lagankhel, Nepal. The study adopted a systematic random sampling approach for respondent selection, and stringent measures were implemented to uphold the validity and reliability of the collected data. To assess patient satisfaction comprehensively, the Patient Satisfaction Questionnaire-III (PSQ-III), developed by the RAND Corporation, was employed in conjunction with relevant sociodemographic variables. Utilizing mean scores and percentages, we calculated satisfaction levels across various dimensions. Additionally, a multinomial logistic regression analysis was conducted to investigate the relationships between patient satisfaction dimensions and sociodemographic characteristics.ResultsThis study encompassed perspective of 206 participants, with 57.3% representing patient relatives and 51% being male, median age of 32 years (standard deviation: 12.53). Notably, patients reported higher levels of satisfaction, particularly within the interpersonal relationship dimension, while the technical quality domain received comparatively lower satisfaction ratings. Multinomial logistic regression analysis underscored the significance of sociodemographic factors in shaping patient satisfaction, with age (p = 0.008), type of residence (p = 0.001), occupation (p = 0.0019), income status (p = 0.014), time to reach the healthcare facility (p = 0.013), and insurance enrollment status (p = 0.017) all demonstrating significant associations. These findings illuminate the intricate qualities of patient satisfaction within our healthcare context, offering actionable insights for enhancement and guiding the trajectory of future research endeavors.ConclusionsOverall patient expressed satisfaction with service provided by tertiary care hospital, however continuous improvement remains essential. Conducting large-scale, nationwide studies across hospital tiers is vital. This data-driven approach empowers policymakers to allocate resources effectively, inform decision-making, and enact policies that exceed patient expectations, fostering a healthcare system of unparalleled excellence.

Intersecting Inequities in COVID-19 Vaccination: A Discourse Analysis of Information Use and Decision-Making Among Ethnically Diverse Parents in Canada.

Little is known about how intersecting social privilege and disadvantage contribute to inequities in COVID-19 information use and vaccine access. This study explored how social inequities intersect to shape access to and use of COVID-19 information and vaccines among parents in Canada. We conducted semi-structured interviews on COVID-19 vaccination information use with ethnically diverse parents of children ages 11 to 18years from April to August 2022. We purposefully invited parents from respondents to a national online survey to ensure representation across diverse intersecting social identities. Five researchers coded transcripts in NVivo using a discourse analysis approach informed by intersectionality. Our analysis focused on use of vaccine information and intersecting privileges and oppressions, including identifying with equity-denied group(s). Interview participants (N = 48) identified as ethnically diverse non-Indigenous (n = 40) and Indigenous (n = 8) Peoples from seven Canadian provinces. Racialized minority or Indigenous participants reflected on historical and contemporary events of racism from government and medical institutions as barriers to trust and access to COVID-19 information, vaccines, and the Canadian healthcare system. Participants with privileged social locations showed greater comfort in resisting public health measures. Despite the urgency to receive COVID-19 vaccines, information gaps and transportation barriers delayed vaccination among some participants living with chronic medical conditions. Historicization of colonialism and ongoing events of racism are a major barrier to trusting public health information. Fostering partnerships with trusted leaders and/or healthcare workers from racialized communities may help rebuild trust. Healthcare systems need to continuously implement strategies to restore trust with Indigenous and racialized populations.

Rewiring care delivery through Digital Therapeutics (DTx): a machine learning-enhanced assessment and development (M-LEAD) framework

BackgroundDigital transformation has sparked profound change in the healthcare sector through the development of innovative digital technologies. Digital Therapeutics offer an innovative approach to disease management and treatment. Care delivery is increasingly patient-centered, data-driven, and based on real-time information. These technological innovations can lead to better patient outcomes and support for healthcare professionals, also considering resource scarcity. As these digital technologies continue to evolve, the healthcare field must be ready to integrate them into processes to take advantage of their benefits. This study aims to develop a framework for the development and assessment of Digital Therapeutics.MethodsThe study was conducted relying on a mixed methodology. 338 studies about Digital Therapeutics resulting from a systematic literature review were analyzed using descriptive statistics through RStudio. Machine learning algorithms were applied to analyze variables and find patterns in the data. The results of these analytical analyses were summarized in a framework qualitatively tested and validated through expert opinion elicitation.ResultsThe research provides M-LEAD, a Machine Learning-Enhanced Assessment and Development framework that recommends best practices for developing and assessing Digital Therapeutics. The framework takes as input Digital Therapeutics characteristics, regulatory aspects, study purpose, and assessment domains. The framework produces as outputs recommendations to design the Digital Therapeutics study characteristics.ConclusionsThe framework constitutes the first step toward standardized guidelines for the development and assessment of Digital Therapeutics. The results may support manufacturers and inform decision-makers of the relevant results of the Digital Therapeutics assessment.