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Neuropsychology Review Jun 2024Mathematics incorporates a broad range of skills, which includes basic early numeracy skills, such as subitizing and basic counting to more advanced secondary skills... (Meta-Analysis)
Meta-Analysis Review
Mathematics incorporates a broad range of skills, which includes basic early numeracy skills, such as subitizing and basic counting to more advanced secondary skills including mathematics calculation and reasoning. The aim of this review was to undertake a detailed investigation of the severity and pattern of early numeracy and secondary mathematics skills in people with epilepsy. Searches were guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Twenty adult studies and 67 child studies were included in this review. Overall, meta-analyses revealed significant moderate impairments across all mathematics outcomes in both adults (g= -0.676), and children (g= -0.593) with epilepsy. Deficits were also observed for specific mathematics outcomes. For adults, impairments were found for mathematics reasoning (g= -0.736). However, two studies found that mathematics calculation was not significantly impaired, and an insufficient number of studies examined early numeracy skills in adults. In children with epilepsy, significant impairments were observed for each mathematics outcome: early numeracy (g= -0.383), calculation (g= -0.762), and reasoning (g= -0.572). The gravity of impairments also differed according to the site of seizure focus for children and adults, suggesting that mathematics outcomes were differentially vulnerable to the location of seizure focus.
Topics: Humans; Epilepsy; Mathematics; Child; Adult
PubMed: 37490196
DOI: 10.1007/s11065-023-09600-8 -
Frontiers in Public Health 2023The coronavirus disease 2019 pandemic has prompted the exploration of new response strategies for such health contingencies in the near future. Over the last 15 years,...
BACKGROUND
The coronavirus disease 2019 pandemic has prompted the exploration of new response strategies for such health contingencies in the near future. Over the last 15 years, several pharmacy-based immunization (PBI) strategies have emerged seeking to exploit the potential of pharmacies as immunization, medication sale, and rapid test centers. However, the participation of pharmacies during the last pandemic was very uneven from one country to another, suggesting a lack of consensus on the definition of their roles and gaps between the literature and practice.
PURPOSE
This study aimed to consolidate the current state of the literature on PBI, document its progress over time, and identify the gaps not yet addressed. Moreover, this study seeks to (i) provide new researchers with an overview of the studies on PBI and (ii) to inform both public health and private organization managers on the range of possible immunization models and strategies.
METHODOLOGY
A systematic review of scientific qualitative and quantitative studies on the most important scientific databases was conducted. The Preferred Reporting Items for Systematic Reviews and Meta-analyzes guidelines were followed. Finally, this study discusses the trends, challenges, and limitations on the existing literature on PBI.
FINDINGS
Must studies concluded that PBI is a beneficial strategy for the population, particularly in terms of accessibility and territorial equity. However, the effectiveness of PBI is affected by the economic, political, and/or social context of the region. The collaboration between the public (government and health departments) and private (various pharmacy chains) sectors contributes to PBI's success.
ORIGINALITY
Unlike previous literature reviews on PBI that compiled qualitative and statistical studies, this study reviewed studies proposing mathematical optimization methods to approach PBI.
Topics: Humans; Pharmacies; COVID-19; Immunization; Vaccination; Pharmacy
PubMed: 37124782
DOI: 10.3389/fpubh.2023.1152556 -
Psychiatry Research Aug 2023We developed and tested a Bayesian network(BN) model to predict ECT remission for depression, with non-response as a secondary outcome.
INTRODUCTION
We developed and tested a Bayesian network(BN) model to predict ECT remission for depression, with non-response as a secondary outcome.
METHODS
We performed a systematic literature search on clinically available predictors. We combined these predictors with variables from a dataset of clinical ECT trajectories (performed in the University Medical Center Utrecht) to create priors and train the BN. Temporal validation was performed in an independent sample.
RESULTS
The systematic literature search yielded three meta-analyses, which provided prior knowledge on outcome predictors. The clinical dataset consisted of 248 treatment trajectories in the training set and 44 trajectories in the test set at the same medical center. The AUC for the primary outcome remission estimated on an independent validation set was 0.686 (95%CI 0.513-0.859) (AUC values of 0.505 - 0.763 observed in 5-fold cross validation of the model within the train set). Accuracy 0.73 (balanced accuracy 0.67), sensitivity 0.55, specificity 0.79, after temporal validation in the independent sample. Prior literature information marginally reduced CI width.
DISCUSSION
A BN model comprised of prior knowledge and clinical data can predict remission of depression after ECT with reasonable performance. This approach can be used to make outcome predictions in psychiatry, and offers a methodological framework to weigh additional information, such as patient characteristics, symptoms and biomarkers. In time, it may be used to improve shared decision-making in clinical practice.
Topics: Humans; Electroconvulsive Therapy; Depression; Bayes Theorem; Prognosis; Biomarkers; Treatment Outcome
PubMed: 37429173
DOI: 10.1016/j.psychres.2023.115328 -
BMC Medical Research Methodology Sep 2023Healthcare, as with other sectors, has undergone progressive digitalization, generating an ever-increasing wealth of data that enables research and the analysis of... (Review)
Review
BACKGROUND
Healthcare, as with other sectors, has undergone progressive digitalization, generating an ever-increasing wealth of data that enables research and the analysis of patient movement. This can help to evaluate treatment processes and outcomes, and in turn improve the quality of care. This scoping review provides an overview of the algorithms and methods that have been used to identify care pathways from healthcare utilization data.
METHOD
This review was conducted according to the methodology of the Joanna Briggs Institute and the Preferred Reporting Items for Systematic Reviews Extension for Scoping Reviews (PRISMA-ScR) Checklist. The PubMed, Web of Science, Scopus, and EconLit databases were searched and studies published in English between 2000 and 2021 considered. The search strategy used keywords divided into three categories: the method of data analysis, the requirement profile for the data, and the intended presentation of results. Criteria for inclusion were that health data were analyzed, the methodology used was described and that the chronology of care events was considered. In a two-stage review process, records were reviewed by two researchers independently for inclusion. Results were synthesized narratively.
RESULTS
The literature search yielded 2,865 entries; 51 studies met the inclusion criteria. Health data from different countries ([Formula: see text]) and of different types of disease ([Formula: see text]) were analyzed with respect to different care events. Applied methods can be divided into those identifying subsequences of care and those describing full care trajectories. Variants of pattern mining or Markov models were mostly used to extract subsequences, with clustering often applied to find care trajectories. Statistical algorithms such as rule mining, probability-based machine learning algorithms or a combination of methods were also applied. Clustering methods were sometimes used for data preparation or result compression. Further characteristics of the included studies are presented.
CONCLUSION
Various data mining methods are already being applied to gain insight from health data. The great heterogeneity of the methods used shows the need for a scoping review. We performed a narrative review and found that clustering methods currently dominate the literature for identifying complete care trajectories, while variants of pattern mining dominate for identifying subsequences of limited length.
Topics: Humans; Algorithms; Checklist; Cluster Analysis; Data Analysis; Data Mining
PubMed: 37759162
DOI: 10.1186/s12874-023-02019-y -
The Journals of Gerontology. Series B,... May 2016To review the evidence on the association between age and limited health literacy, overall and by health literacy test, and to investigate the mediating role of... (Review)
Review
OBJECTIVES
To review the evidence on the association between age and limited health literacy, overall and by health literacy test, and to investigate the mediating role of cognitive function.
METHOD
The Embase, MEDLINE, and PsycINFO databases were searched. Eligible studies were conducted in any country or language, included participants aged ≥50 years, presented a measure of association between age and health literacy, and were published through September 2013.
RESULTS
Seventy analyses in 60 studies were included in the systematic review; 29 of these were included in the meta-analysis. Older age was strongly associated with limited health literacy in analyses that measured health literacy as reading comprehension, reasoning, and numeracy skills (random-effects odds ratio [OR] = 4.20; 95% confidence interval [CI]: 3.13-5.64). By contrast, older age was weakly associated with limited health literacy in studies that measured health literacy as medical vocabulary (random-effects OR = 1.19; 95% CI: 1.03-1.37). Evidence on the mediating role of cognitive function was limited.
DISCUSSION
Health literacy tests that utilize a range of fluid cognitive abilities and mirror everyday health tasks frequently observe skill limitations among older adults. Vocabulary-based health literacy skills appear more stable with age. Researchers should select measurement tests wisely when assessing health literacy of older adults.
Topics: Aged; Aging; Cognition; Comprehension; Female; Health Literacy; Humans; Male; Middle Aged; Statistics as Topic
PubMed: 25504637
DOI: 10.1093/geronb/gbu161 -
Frontiers in Psychology 2022Self-efficacy is an integral part of personal factors that contributes substantially to students' success in mathematics. This review draws on previous intervention...
Self-efficacy is an integral part of personal factors that contributes substantially to students' success in mathematics. This review draws on previous intervention studies to identify, describe, and expose underlying mechanisms of interventions that foster mathematics self-efficacy. The findings show that effective mathematics self-efficacy interventions can be categorized into three categories using their underlying mechanisms: those that directly manipulate sources of self-efficacy to foster the construct, and those that either embed self-efficacy features in teaching methods or in learning strategies. Specific examples of interventions that fall in each of these three categories are described including their features and the underlying mechanisms that improve students' mathematics self-efficacy. I argue for the two "most effective" interventions that foster mathematics self-efficacy and their relevance to either pre-university or university students with implications for teaching and learning of mathematics.
PubMed: 36225698
DOI: 10.3389/fpsyg.2022.986622 -
Frontiers in Psychology 2022The call for evidence-based practice in education emphasizes the need for research to provide evidence for particular fields of educational practice. With this...
The call for evidence-based practice in education emphasizes the need for research to provide evidence for particular fields of educational practice. With this systematic literature review we summarize and analyze aggregated effectiveness information from 41 meta-analyses published between 2004 and 2019 to inform evidence-based practice in a particular field. In line with target specifications in education that are provided for a certain school subject educational level, we developed and adopted a selection heuristic for filtering aggregated effect sizes specific to both science and mathematics education the secondary student population. The results include 78 context-specific aggregated effect sizes based on data from over one million students. The findings encompass a multitude of different teaching strategies, most of which offer a measurable advantage to alternatives. Findings demonstrate that context-specific effect size information may often differ from more general effect size information on teaching effectiveness and adherence to quality standards varies in sampled meta-analyses. Thus, although meta-analytic research has strongly developed over the last few years, providing context-specific and high-quality evidence still needs to be a focus in the field of secondary mathematics and science teaching and beyond.
PubMed: 35548498
DOI: 10.3389/fpsyg.2022.873995 -
JMIR Mental Health Jun 2024Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of... (Review)
Review
BACKGROUND
Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention.
OBJECTIVE
This systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention.
METHODS
A systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar.
RESULTS
Overall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being.
CONCLUSIONS
Our findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention.
Topics: Humans; Machine Learning; Suicide Prevention; Mental Health; Social Media; Data Analysis
PubMed: 38935419
DOI: 10.2196/55747 -
Frontiers in Psychology 2023Game-based learning (GBL) is one of the modern trends in education in the 21st century. Numerous research studies have been carried out to investigate the influence of...
INTRODUCTION
Game-based learning (GBL) is one of the modern trends in education in the 21st century. Numerous research studies have been carried out to investigate the influence of teaching on the students' academic attainment. It is crucial to integrate the cognitive and affective domains into teaching and learning strategies. This study aims to review journal articles from 2018 to 2022 concerning the influence of GBL in mathematics T&L on the students' cognitive and affective domains.
METHODS
A research methodology based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was used for the survey on the basis of the Scopus and Web of Science (WOS) databases wherein 773 articles relating to game-based learning (GBL) in mathematics were discovered. Based on the study topic, study design, study technique, and analysis, only 28 open-access articles were chosen for further evaluation. Two types of cognitive domain and five types of affective domain were identified as related to the implications of GBL on the students' T&L of mathematics.
RESULTS
The study results show that GBL has positively impacted students when they are learning mathematics. It is comprised of two types of cognitive domain (knowledge and mathematical skills) and five types of affective domain (achievement, attitude, motivation, interest, and engagement). The findings of this study are anticipated to encourage educators in the classrooms more effectively.
DISCUSSION
GBL in education is now one of the major learning trends of the 21st century. Since 2019, the number of studies relating to game-based learning has increased. There is an influence on the cognitive and affective domains due to T&L Mathematics utilizing a game-based learning (GBL) approach.
PubMed: 37057144
DOI: 10.3389/fpsyg.2023.1105806 -
Biotechnology Advances Oct 2023Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves... (Review)
Review
Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.
Topics: Temperature; Metabolic Networks and Pathways; Phenotype; Models, Biological
PubMed: 37348662
DOI: 10.1016/j.biotechadv.2023.108203