Theoretical Models of Consciousness: A Scoping Review.
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ABSTRACT:
SUBMITTER: Sattin D
PROVIDER: S-EPMC8146510 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundSince the 2014-2016 West Africa Ebola epidemic, the concept of measuring health security capacity has become increasingly important within the broader context of health systems-strengthening, enhancing responses to public health emergencies, and reducing global catastrophic biological risks. Efforts to regularly and sustainably track the evolution of health security capabilities and capacities over time - while also accounting for political, social, and environmental risks - could help countries progress toward eliminating sources of health insecurity. We sought to aggregate evidence-based principles that capture a country's baseline public health and healthcare capabilities, its health security system performance before and during infectious disease crises, and its broader social, political, security, and ecological risk environments.MethodsWe conducted a scoping review of English-language scholarly and gray literature to identify evidence- and practice-based indicators and proxies for measuring health security at the country level over time. We then used a qualitative coding framework to identify recurrent themes in the literature and synthesize foundational principles for measuring global health security. Documents reviewed included English-language literature published after 2001 until the end of the research period-September 2017-to ensure relevance to the current global health security landscape; literature examining acute infectious disease threats with potential for transnational spread; and literature addressing global health security efforts at the country level.ResultsWe synthesized four foundational principles for measuring global health security: measurement requires assessment of existing capacities, as well as efforts to build core public health, healthcare, and biosecurity capabilities; assessments of national programs and efforts to mitigate a critical subset of priority threats could inform efforts to generate useful metrics for global health security; there are measurable enabling factors facilitating health security-strengthening efforts; and finally, measurement requires consideration of social, political, and ecological risk environments.ConclusionThe themes identified in this review could inform efforts to systematically assess the impacts and effectiveness of activities undertaken to strengthen global health security.
| S-EPMC6637489 | biostudies-literature
Project description:BACKGROUND:Microbeam Radiation Therapy (MRT) is an innovative approach in radiation oncology where a collimator subdivides the homogeneous radiation field into an array of co-planar, high-dose beams which are tens of micrometres wide and separated by a few hundred micrometres. OBJECTIVE:This scoping review was conducted to map the available evidence and provide a comprehensive overview of the similarities, differences, and outcomes of all experiments that have employed animal models in MRT. METHODS:We considered articles that employed animal models for the purpose of studying the effects of MRT. We searched in seven databases for published and unpublished literature. Two independent reviewers screened citations for inclusion. Data extraction was done by three reviewers. RESULTS:After screening 5688 citations and 159 full-text papers, 95 articles were included, of which 72 were experimental articles. Here we present the animal models and pre-clinical radiation parameters employed in the existing MRT literature according to their use in cancer treatment, non-neoplastic diseases, or normal tissue studies. CONCLUSIONS:The study of MRT is concentrated in brain-related diseases performed mostly in rat models. An appropriate comparison between MRT and conventional radiotherapy (instead of synchrotron broad beam) is needed. Recommendations are provided for future studies involving MRT.
| S-EPMC7139755 | biostudies-literature
Project description:BackgroundFitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior.ObjectiveThis review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions.MethodsA scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization.ResultsThe literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized.ConclusionsThis review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users' social profile. Finally, the long-term effects of such interventions as well as the technology medium used for the interventions need to be evaluated rigorously.
| S-EPMC6352015 | biostudies-literature
Project description:BackgroundReduction or elimination of inappropriate, ineffective, or potentially harmful healthcare services and public health programs can help to ensure limited resources are used effectively. Frameworks and models (FM) are valuable tools in conceptualizing and guiding the study of de-implementation. This scoping review sought to identify and characterize FM that can be used to study de-implementation as a phenomenon and identify gaps in the literature to inform future model development and application for research.MethodsWe searched nine databases and eleven journals from a broad array of disciplines (e.g., healthcare, public health, public policy) for de-implementation studies published between 1990 and June 2020. Two raters independently screened titles and abstracts, and then a pair of raters screened all full text records. We extracted information related to setting, discipline, study design, methodology, and FM characteristics from included studies.ResultsThe final search yielded 1860 records, from which we screened 126 full text records. We extracted data from 27 articles containing 27 unique FM. Most FM (n = 21) were applicable to two or more levels of the Socio-Ecological Framework, and most commonly assessed constructs were at the organization level (n = 18). Most FM (n = 18) depicted a linear relationship between constructs, few depicted a more complex structure, such as a nested or cyclical relationship. Thirteen studies applied FM in empirical investigations of de-implementation, while 14 articles were commentary or review papers that included FM.ConclusionDe-implementation is a process studied in a broad array of disciplines, yet implementation science has thus far been limited in the integration of learnings from other fields. This review offers an overview of visual representations of FM that implementation researchers and practitioners can use to inform their work. Additional work is needed to test and refine existing FM and to determine the extent to which FM developed in one setting or for a particular topic can be applied to other contexts. Given the extensive availability of FM in implementation science, we suggest researchers build from existing FM rather than recreating novel FM.RegistrationNot registered.
| S-EPMC8611904 | biostudies-literature
Project description:OBJECTIVES:Nursing home (NH) residents experience a high burden of chronic disease. Chronic disease management (CDM) can be a challenge, as the context of care provision and the way care is provided impact care delivery. This scoping review aimed to identify types of chronic diseases studied in intervention studies in NHs, influential contextual factors addressed by interventions and future CDM research considerations. DESIGN:The scoping review followed guidelines by Arksey and O'Malley (2005) and Levac, Colquhoun and O'Brien (2010). Six reviewers screened citations for inclusion. Data extraction was performed by one reviewer and verified by a second reviewer. DATA SOURCES:We searched four databases: CINAHL, EMBASE, PubMed and Scopus, in March 2018. ELIGIBILITY CRITERIA:Studies were included if (1) aim of intervention was to improve CDM, (2) intervention incorporated the chronic care model (CCM), (3) included NH residents, (4) analysed the efficacy of the intervention and (5) sample included adults over age 65 years. Studies were limited to English or French language and to those published after 1996, when the CCM was first conceptualised. DATA EXTRACTION AND SYNTHESIS:Extracted information included the type of chronic disease, the type and number of CCM model components used in the intervention, the method of delivery of the intervention, and outcomes. RESULTS:On completion of the review of 11?917 citations, 13 studies were included. Most interventions targeted residents living with dementia. There was significant heterogeneity noted among designs, outcomes, and type and complexity of intervention components. There was little evaluation of the sustainability of interventions, including feasibility. CONCLUSIONS:Research was heavily focused on management of dementia. The most commonly included CCM components were multidisciplinary care, evidence-based care, coordinated care and clinical information systems. Future research should include subjective and objective outcomes, which are meaningful for NH residents, for common chronic diseases.
| S-EPMC7044889 | biostudies-literature
Project description:BACKGROUND:Diabetes is a highly prevalent chronic disease that places a large burden on individuals and health care systems. Models predicting the risk (also called predictive models) of other conditions often compare people with and without diabetes, which is of little to no relevance for people already living with diabetes (called patients). This review aims to identify and synthesize findings from existing predictive models of physical and mental health diabetes-related conditions. METHODS:We will use the scoping review frameworks developed by the Joanna Briggs Institute and Levac and colleagues. We will perform a comprehensive search for studies from Ovid MEDLINE and Embase databases. Studies involving patients with prediabetes and all types of diabetes will be considered, regardless of age and gender. We will limit the search to studies published between 2000 and 2018. There will be no restriction of studies based on country or publication language. Abstracts, full-text screening, and data extraction will be done independently by two individuals. Data abstraction will be conducted using a standard methodology. We will undertake a narrative synthesis of findings while considering the quality of the selected models according to validated and well-recognized tools and reporting standards. DISCUSSION:Predictive models are increasingly being recommended for risk assessment in treatment decision-making and clinical guidelines. This scoping review will provide an overview of existing predictive models of diabetes complications and how to apply them. By presenting people at higher risk of specific complications, this overview may help to enhance shared decision-making and preventive strategies concerning diabetes complications. Our anticipated limitation is potentially missing models because we will not search grey literature.
| S-EPMC7282106 | biostudies-literature
Project description:IntroductionFalls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess patients' fall risk have been implemented in hospitals internationally, ranging from brief screening questions to multifactorial risk assessments and complex prediction models, despite a lack of clear evidence of effect in reducing falls in acute hospital environments. The increasing digitisation of hospital systems provides new opportunities to understand and predict falls using routinely recorded data, with potential to integrate fall prediction models into real-time or near-real-time computerised decision support for clinical teams seeking to mitigate fall risk. However, the use of non-traditional approaches to fall risk prediction, including machine learning using integrated electronic medical records, has not yet been reviewed relative to more traditional fall prediction models. This scoping review will summarise methodologies used to develop existing hospital fall prediction models, including reporting quality assessment.Methods and analysisThis scoping review will follow the Arksey and O'Malley framework and its recent advances, and will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews recommendations. Four electronic databases (CINAHL via EBSCOhost, PubMed, IEEE Xplore and Embase) will be initially searched for studies up to 12 November 2020, and searches may be updated prior to final reporting. Additional studies will be identified by reference list review and citation analysis of included studies. No restriction will be placed on the date or language of identified studies. Screening of search results and extraction of data will be performed by two independent reviewers. Reporting quality will be assessed by the adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis.Ethics and disseminationEthical approval is not required for this study. Findings will be disseminated through peer-reviewed publication and scientific conferences.
| S-EPMC8438847 | biostudies-literature
Project description:BackgroundFor health care services to address the health care needs of populations and respond to changes in needs over time, workforces must be planned. This requires quantitative models to estimate future workforce requirements that take account of population size, oral health needs, evidence-based approaches to addressing needs, and methods of service provision that maximize productivity. The aim of this scoping review was to assess whether and how these 4 elements contribute to existing models of oral health workforce planning.MethodsA scoping review was conducted. MEDLINE, Embase, HMIC, and EconLit were searched, all via OVID. Additionally, gray literature databases were searched and key bodies and policy makers contacted. Workforce planning models were included if they projected workforce numbers and were specific to oral health. No limits were placed on country. A single reviewer completed initial screening of abstracts; 2 independent reviewers completed secondary screening and data extraction. A narrative synthesis was conducted.ResultsA total of 4,009 records were screened, resulting in 42 included articles detailing 47 models. The workforce planning models varied significantly in their use of data on oral health needs, evidence-based services, and provider productivity, with most models relying on observed levels of service utilization and demand.ConclusionsThis review has identified quantitative workforce planning models that aim to estimate future workforce requirements. Approaches to planning the oral health workforce are not always based on deriving workforce requirements from population oral health needs. In many cases, requirements are not linked to population needs, while in models where needs are included, they are constrained by the existence and availability of the required data. It is critical that information systems be developed to effectively capture data necessary to plan future oral health care workforces in ways that relate directly to the needs of the populations being served.Knowledge transfer statementPolicy makers can use the results of this study when making decisions about the planning of oral health care workforces and about the data to routinely collect within health services. Collection of suitable data will allow for the continual improvement of workforce planning, leading to a responsive health service and likely future cost savings.
| S-EPMC8674798 | biostudies-literature
Project description:BackgroundComplementary and Alternative Medicine (CAM) is widely used throughout the UK and the Western world. CAM is commonly used for children and the decision-making process to use CAM is affected by numerous factors. Most research on CAM use lacks a theoretical framework and is largely based on bivariate statistics. The aim of this review was to identify a conceptual model which could be used to explain the decision-making process in parental choice of CAM.MethodsA systematic search of the literature was carried out. A two-stage selection process with predetermined inclusion/exclusion criteria identified studies using a theoretical framework depicting the interaction of psychological factors involved in the CAM decision process. Papers were critically appraised and findings summarised.ResultsTwenty two studies using a theoretical model to predict CAM use were included in the final review; only one examined child use. Seven different models were identified. The most commonly used and successful model was Andersen's Sociobehavioural Model (SBM). Two papers proposed modifications to the SBM for CAM use. Six qualitative studies developed their own model.ConclusionThe SBM modified for CAM use, which incorporates both psychological and pragmatic determinants, was identified as the best conceptual model of CAM use. This model provides a valuable framework for future research, and could be used to explain child CAM use. An understanding of the decision making process is crucial in promoting shared decision making between healthcare practitioners and parents and could inform service delivery, guidance and policy.
| S-EPMC2680396 | biostudies-literature
Project description:Greater integration of medication-assisted treatment (MAT) for opioid use disorder (OUD) in U.S. primary care settings would expand access to treatment for this condition. Models for integrating MAT into primary care vary in structure. This article summarizes findings of a technical report for the Agency for Healthcare Research and Quality describing MAT models of care for OUD, based on a literature review and interviews with key informants in the field. The report describes 12 representative models of care for integrating MAT into primary care settings that could be considered for adaptation across diverse health care settings. Common components of existing care models include pharmacotherapy with buprenorphine or naltrexone, provider and community education, coordination and integration of OUD treatment with other medical and psychological needs, and psychosocial services and interventions. Models vary in how each component is implemented. Decisions about adopting MAT models of care should be individualized to address the unique milieu of each implementation setting.
| S-EPMC5504692 | biostudies-other