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The British Journal of Educational... Sep 2022There is a plethora of reviews that summarize much of the evidence base in Social and Emotional Learning (SEL). However, there are criticisms around variability of... (Review)
Review
BACKGROUND
There is a plethora of reviews that summarize much of the evidence base in Social and Emotional Learning (SEL). However, there are criticisms around variability of quality and focus of those reviews, meaning there is little strategic overview of the current state of the field. Further, there are rising concerns as to systemic gaps in the evidence base itself. An overview of reviews provides an opportunity for a comprehensive classification and corresponding critique of evidence.
AIMS
The study sought to examine a-priori concerns regarding (1) variation in the rigour and quality of the meta-analytic and systematic evidence base, (2) comparatively less conclusive evidence for whole school approaches when compared to class-based curricula, and (3) an assumed universality of effect (i.e., lack of examination of any differential gains for sub-groups).
METHOD AND RESULTS
A systematic search of the systematic and meta-analytic literature identified a total of 33 reviews examining SEL interventions. Papers were subject to a quality assessment in order to examine methodological rigour and were collated in line with the study's objectives.
CONCLUSIONS
We maintain the prevailing consensus that SEL programmes have an important role in education. However, variation in evidence quality remains high and there appear ambiguities regarding what constitutes whole school approaches. The review also highlights a novel and concerning lack of data for differentiating any subgroup effects. The review concludes with recommended novel directions for future research, including adoption of more complex trial architecture in evaluation alongside a move towards a wider plurality in methodological approach.
Topics: Cognition; Emotions; Humans; Schools
PubMed: 34921555
DOI: 10.1111/bjep.12480 -
British Journal of Anaesthesia Jun 2023Chronic pain and depression represent two global health problems with considerable economic consequences. Although existing literature reports on the relation between... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Chronic pain and depression represent two global health problems with considerable economic consequences. Although existing literature reports on the relation between depression and pain conditions, meta-analytic evidence backing the mediating role of sleep disturbance as one of the main symptoms of depression is scarce. To examine the extent to which sleep disturbance mediates the depression-chronic pain association, we conducted a systematic review and meta-analysis of the associations of chronic pain, depression, and sleep quality.
METHODS
We systematically searched for literature in MEDLINE and other relevant databases and identified cohort and case-control studies on depression, sleep disturbance, and chronic pain. Forty-nine studies were eligible, with a total population of 120 489 individuals. We obtained direct and indirect path coefficients via two-stage meta-analytic structural equation modelling, examined heterogeneity via subgroup analyses, and evaluated primary studies quality.
RESULTS
We found a significant, partial mediation effect of sleep disturbance on the relation between depression and chronic pain. The pooled path coefficient (coef.) of the indirect effect was 0.03 (95% confidence interval [CI]: 0.01-0.05) and accounted for 12.5% of the total effect of depression on chronic pain. This indirect effect also existed for cohort studies (coef. 0.02; 95% CI: 0.002-0.04), European studies (coef. 0.03; 95% CI: 0.004-0.05), and studies that adjusted for confounders (coef. 0.04; 95% CI: 0.01-0.09).
CONCLUSIONS
Sleep disturbance partially mediates the association between depression and pain. Although plausible mechanisms could explain this mediation effect, other explanations, including reverse causation, must be further explored.
SYSTEMATIC REVIEW PROTOCOL
PROSPERO CRD42022338201.
Topics: Humans; Chronic Pain; Depression; Sleep Quality; Sleep; Sleep Wake Disorders
PubMed: 37059623
DOI: 10.1016/j.bja.2023.02.036 -
Iranian Journal of Public Health Jun 2019This systematic review was conducted to highlights key challenges, and outlines important next steps to maximize the potential to contribute to the broader malaria... (Review)
Review
BACKGROUND
This systematic review was conducted to highlights key challenges, and outlines important next steps to maximize the potential to contribute to the broader malaria elimination interventions.
METHODS
This systematic review on malaria elimination intervention and challenges was undertaken searching six databases, between 1995 and 2018. Inclusion and exclusion criteria were set. The references were collated and categorized according to type of study, intervention, population, and health outcome. Articles selection based on title and abstract, retrieval of full text and additions of articles from reference lists and recommendations from experts. Disagreement in data extraction was solved by consultation of third reviewer.
RESULTS
Overall, 4039 records were examined related to malaria elimination that initially identified by our designated electronic databases search. Overall, 35 studies contained 14 experimental studies (40%) and 21 analytic observational studies (60%) met the inclusion criteria for this review. Studies used a wide variety of malaria elimination interventions. Types of interventions either elimination-focused interventions or general interventions on educational, prevention and treatment of malaria are included. This review pointed out the variety of challenges for eliminate malaria among low and high endemic countries.
CONCLUSION
Malaria elimination is facilitated by strong health systems, determined leadership, appropriate incentivization, an effective surveillance system, and regional collaborations. We have identified areas for elimination-specific interventions deserve more attention in the conduct and reporting.
PubMed: 31341841
DOI: No ID Found -
German Medical Science : GMS E-journal 2022The goal of this review was to identify decision-analytic modeling studies in early health technology assessments (HTA) of high-risk medical devices, published over the... (Review)
Review
OBJECTIVE
The goal of this review was to identify decision-analytic modeling studies in early health technology assessments (HTA) of high-risk medical devices, published over the last three years, and to provide a systematic overview of model purposes and characteristics. Additionally, the aim was to describe recent developments in modeling techniques.
METHODS
For this scoping review, we performed a systematic literature search in PubMed and Embase including studies published in English or German. The search code consisted of terms describing early health technology assessment and terms for decision-analytic models. In abstract and full-text screening, studies were excluded that were not modeling studies for a high-risk medical device or an in-vitro diagnostic test. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram was used to report on the search and exclusion of studies. For all included studies, study purpose, framework and model characteristics were extracted and reported in systematic evidence tables and a narrative summary.
RESULTS
Out of 206 identified studies, 19 studies were included in the review. Studies were either conducted for hypothetical devices or for existing devices after they were already available on the market. No study extrapolated technical data from early development stages to estimate potential value of devices in development. All studies except one included cost as an outcome. Two studies were budget impact analyses. Most studies aimed at adoption and reimbursement decisions. The majority of studies were on in-vitro diagnostic tests for personalized and targeted medicine. A timed automata model, to our knowledge a model type new to HTA, was tested by one study. It describes the agents in a clinical pathway in separate models and, by allowing for interaction between the models, can reflect complex individual clinical pathways and dynamic system interactions. Not all sources of uncertainty for in-vitro tests were explicitly modeled. Elicitation of expert knowledge and judgement was used for substitution of missing empirical data. Analysis of uncertainty was the most valuable strength of decision-analytic models in early HTA, but no model applied sensitivity analysis to optimize the test positivity cutoff with regard to the benefit-harm balance or cost-effectiveness. Value-of-information analysis was rarely performed. No information was found on the use of causal inference methods for estimation of effect parameters from observational data.
CONCLUSION
Our review provides an overview of the purposes and model characteristics of nineteen recent early evaluation studies on medical devices. The review shows the growing importance of personalized interventions and confirms previously published recommendations for careful modeling of uncertainties surrounding diagnostic devices and for increased use of value-of-information analysis. Timed automata may be a model type worth exploring further in HTA. In addition, we recommend to extend the application of sensitivity analysis to optimize positivity criteria for in-vitro tests with regard to benefit-harm or cost-effectiveness. We emphasize the importance of causal inference methods when estimating effect parameters from observational data.
Topics: Humans; Technology Assessment, Biomedical; Equipment and Supplies
PubMed: 36742459
DOI: 10.3205/000313 -
International Journal of Molecular... Mar 2023The concentration of biomolecules in living systems shows numerous systematic and random variations. Systematic variations can be classified based on the frequency of... (Review)
Review
The concentration of biomolecules in living systems shows numerous systematic and random variations. Systematic variations can be classified based on the frequency of variations as ultradian (<24 h), circadian (approximately 24 h), and infradian (>24 h), which are partly predictable. Random biological variations are known as between-subject biological variations that are the variations among the set points of an analyte from different individuals and within-subject biological variation, which is the variation of the analyte around individuals' set points. The random biological variation cannot be predicted but can be estimated using appropriate measurement and statistical procedures. Physiological rhythms and random biological variation of the analytes could be considered the essential elements of predictive, preventive, and particularly personalized laboratory medicine. This systematic review aims to summarize research that have been done about the types of physiological rhythms, biological variations, and their effects on laboratory tests. We have searched the PubMed and Web of Science databases for biological variation and physiological rhythm articles in English without time restrictions with the terms "Biological variation, Within-subject biological variation, Between-subject biological variation, Physiological rhythms, Ultradian rhythms, Circadian rhythm, Infradian rhythms". It was concluded that, for effective management of predicting, preventing, and personalizing medicine, which is based on the safe and valid interpretation of patients' laboratory test results, both physiological rhythms and biological variation of the measurands should be considered simultaneously.
Topics: Humans; Circadian Rhythm; Ultradian Rhythm
PubMed: 37047252
DOI: 10.3390/ijms24076275 -
Anesthesia and Analgesia Feb 2023Several frailty screening tools have been shown to predict mortality and complications after surgery. However, these tools were developed for in-person evaluation and... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Several frailty screening tools have been shown to predict mortality and complications after surgery. However, these tools were developed for in-person evaluation and cannot be used during virtual assessments before surgery. The FRAIL (fatigue, resistance, ambulation, illness, and loss of weight) scale is a brief assessment that can potentially be conducted virtually or self-administered, but its association with postoperative outcomes in older surgical patients is unknown. The objective of this systematic review and meta-analysis (SRMA) was to determine whether the FRAIL scale is associated with mortality and postoperative outcomes in older surgical patients.
METHODS
Systematic searches were conducted of multiple literature databases from January 1, 2008, to December 17, 2022, to identify English language studies using the FRAIL scale in surgical patients and reporting mortality and postoperative outcomes, including postoperative complications, postoperative delirium, length of stay, and functional recovery. These databases included Medline, Medline ePubs/In-process citations, Embase, APA (American Psychological Association) PsycInfo, Ovid Emcare Nursing, (all via the Ovid platform), Cumulative Index to Nursing and Allied Health Literature (CINAHL) EbscoHost, the Web of Science (Clarivate Analytics), and Scopus (Elsevier). The risk of bias was assessed using the quality in prognosis studies tool.
RESULTS
A total of 18 studies with 4479 patients were included. Eleven studies reported mortality at varying time points. Eight studies were included in the meta-analysis of mortality. The pooled odds ratio (OR) of 30-day, 6-month, and 1-year mortality for frail patients was 6.62 (95% confidence interval [CI], 2.80-15.61; P < .01), 2.97 (95% CI, 1.54-5.72; P < .01), and 1.54 (95% CI, 0.91-2.58; P = .11), respectively. Frailty was associated with postoperative complications and postoperative delirium, with an OR of 3.11 (95% CI, 2.06-4.68; P < .01) and 2.65 (95% CI, 1.85-3.80; P < .01), respectively. The risk of bias was low in 16 of 18 studies.
CONCLUSIONS
As measured by the FRAIL scale, frailty was associated with 30-day mortality, 6-month mortality, postoperative complications, and postoperative delirium.
Topics: Humans; Aged; Frailty; Frail Elderly; Emergence Delirium; Geriatric Assessment; Postoperative Complications
PubMed: 36638509
DOI: 10.1213/ANE.0000000000006272 -
PloS One 2023This systematic review aimed to evaluate the association between smartphone addiction and sleep in medical students. The secondary outcomes included the prevalence of... (Meta-Analysis)
Meta-Analysis
OBJECTIVES
This systematic review aimed to evaluate the association between smartphone addiction and sleep in medical students. The secondary outcomes included the prevalence of smartphone addiction, duration and purpose of its use, prevalence of poor sleep, duration and quality of sleep.
METHODS
The authors searched PubMed, Cochrane Library, Embase, PsycINFO and CINAHL databases, from inception of each database to October 2022. Quantitative studies in the English language on smartphone addiction and sleep in students studying Western Medicine were included. The Rayyan application was used for title-abstract screening, and Joanna Briggs Institute (JBI) critical appraisal checklist to assess the risk of bias. Heterogeneity tests and meta-synthesis of data were performed using the meta-package in R software. Data on the activities used on the smartphone was synthesized qualitatively.
RESULTS
A total of 298 abstracts were initially assessed for inclusion eligibility: 16 of them were eventually appraised, covering 9466 medical students comprising 3781 (39.9%) males and 5161 (54.5%) females. Meta-correlation between the Smartphone Addiction Scale Short Version (SAS-SV) and Pittsburgh Sleep Quality Index (PSQI) was 0.30 (95%CI = 0.24-0.36), and 0.27 (95% CI = 0.18-0.36) for SAS-SV and sleep duration. The meta-analytic estimation of smartphone addiction prevalence was 39% (95%CI = 0.30-0.50), and score using SAS-SV was 31.11 (95%CI = 29.50-32.72). The mean duration of smartphone daily used was 4.90 hours (95%CI = 3.72-6.08). The meta-analytic estimation on prevalence of poor sleep was 57% (95%CI = 0.48-0.66), and the meta-mean of PSQI and duration of sleep was 5.95 (95%CI = 4.90-7.00) and 5.62h (95%CI = 4.87-6.36) respectively. Medical students used their smartphones mostly for text messaging, followed by photo-sharing or social networking. Its usage for medical education remains unclear.
CONCLUSION
The prevalence of poor sleep and smartphone addiction in medical students was 57% and 39% respectively, with a correlation index of 0.30. Medical students commonly used the smartphone for text-messaging, photo-sharing or social networking, averaging 4.9 hours daily.
Topics: Female; Male; Humans; Students, Medical; Internet Addiction Disorder; Sleep; Sleep Duration; Academies and Institutes
PubMed: 37713408
DOI: 10.1371/journal.pone.0290724 -
BMC Public Health Nov 2022Global public health action to address noncommunicable diseases (NCDs) requires new approaches. NCDs are primarily prevented and managed in the community where there is... (Review)
Review
BACKGROUND
Global public health action to address noncommunicable diseases (NCDs) requires new approaches. NCDs are primarily prevented and managed in the community where there is little investment in digital health systems and analytics; this has created a data chasm and relatively silent burden of disease. The nascent but rapidly emerging area of precision public health offers exciting new opportunities to transform our approach to NCD prevention. Precision public health uses routinely collected real-world data on determinants of health (social, environmental, behavioural, biomedical and commercial) to inform precision decision-making, interventions and policy based on social position, equity and disease risk, and continuously monitors outcomes - the right intervention for the right population at the right time. This scoping review aims to identify global exemplars of precision public health and the data sources and methods of their aggregation/application to NCD prevention.
METHODS
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Six databases were systematically searched for articles published until February 2021. Articles were included if they described digital aggregation of real-world data and 'traditional' data for applied community, population or public health management of NCDs. Real-world data was defined as routinely collected (1) Clinical, Medication and Family History (2) Claims/Billing (3) Mobile Health (4) Environmental (5) Social media (6) Molecular profiling (7) Patient-centred (e.g., personal health record). Results were analysed descriptively and mapped according to the three horizons framework for digital health transformation.
RESULTS
Six studies were included. Studies developed population health surveillance methods and tools using diverse real-world data (e.g., electronic health records and health insurance providers) and traditional data (e.g., Census and administrative databases) for precision surveillance of 28 NCDs. Population health analytics were applied consistently with descriptive, geospatial and temporal functions. Evidence of using surveillance tools to create precision public health models of care or improve policy and practice decisions was unclear.
CONCLUSIONS
Applications of real-world data and designed data to address NCDs are emerging with greater precision. Digital transformation of the public health sector must be accelerated to create an efficient and sustainable predict-prevent healthcare system.
Topics: Humans; Noncommunicable Diseases; Public Health; Delivery of Health Care; Telemedicine; Social Media
PubMed: 36434553
DOI: 10.1186/s12889-022-14452-7 -
JMIR Medical Education Jun 2020The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and... (Review)
Review
BACKGROUND
The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice.
OBJECTIVE
The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience.
METHODS
A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments.
RESULTS
This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula.
CONCLUSIONS
To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.
PubMed: 32602844
DOI: 10.2196/19285 -
Molecules (Basel, Switzerland) Aug 2021The biosynthesis of silver nanoparticles and the antibacterial activities has provided enormous data on populations, geographical areas, and experiments with bio silver...
The biosynthesis of silver nanoparticles and the antibacterial activities has provided enormous data on populations, geographical areas, and experiments with bio silver nanoparticles' antibacterial operation. Several peer-reviewed publications have discussed various aspects of this subject field over the last generation. However, there is an absence of a detailed and structured framework that can represent the research domain on this topic. This paper attempts to evaluate current articles mainly on the biosynthesis of nanoparticles or antibacterial activities utilizing the scientific methodology of big data analytics. A comprehensive study was done using multiple databases-Medline, Scopus, and Web of Sciences through PRISMA (i.e., Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The keywords used included 'biosynthesis silver nano particles' OR 'silver nanoparticles' OR 'biosynthesis' AND 'antibacterial behavior' OR 'anti-microbial opposition' AND 'systematic analysis,' by using MeSH (Medical Subject Headings) terms, Boolean operator's parenthesis, or truncations as required. Since their effectiveness is dependent on particle size or initial concentration, it necessitates more research. Understanding the field of silver nanoparticle biosynthesis and antibacterial activity in Gulf areas and most Asian countries also necessitates its use of human-generated data. Furthermore, the need for this work has been highlighted by the lack of predictive modeling in this field and a need to combine specific domain expertise. Studies eligible for such a review were determined by certain inclusion and exclusion criteria. This study contributes to the existence of theoretical and analytical studies in this domain. After testing as per inclusion criteria, seven in vitro studies were selected out of 28 studies. Findings reveal that silver nanoparticles have different degrees of antimicrobial activity based on numerous factors. Limitations of the study include studies with low to moderate risks of bias and antimicrobial effects of silver nanoparticles. The study also reveals the possible use of silver nanoparticles as antibacterial irrigants using various methods, including a qualitative evaluation of knowledge and a comprehensive collection and interpretation of scientific studies.
Topics: Animals; Anti-Bacterial Agents; Humans; Metal Nanoparticles; Particle Size; Silver
PubMed: 34443644
DOI: 10.3390/molecules26165057