From Full Day Learning to 30 Minutes a Day: A Descriptive Study of Early Learning During the First COVID-19 Pandemic School Shutdown in Ontario.
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ABSTRACT: Supplementary information
The online version contains supplementary material available at 10.1007/s10643-021-01304-z.
SUBMITTER: Spadafora N
PROVIDER: S-EPMC8761377 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The global pandemic caused by the novel Coronavirus (COVID-19) in spring 2020
| S-EPMC7723736 | biostudies-literature
Project description:PurposeSchools and organized sports both offer great chances to promote physical activity among children. Full-day schools particularly allow for extensive participation in extra-curricular physical activities. However, due to time reasons, full-day schools may also prevent children from engagement in organized sports outside school. There is only little national and international research addressing the possible competition of full-day schools and providers of organized sports outside school and the potential effects on children's physical activity behavior. In Germany's educational system, a transformation towards more full-day schools is currently taking place. The existence of both, half-day and full-day schools, gave occasion to the following research question: Do students attending half-day and full-day school differ with respect to a) sports club membership rate and b) weekly amount of sports club training?MethodsData were collected in eleven German primary schools. Selected schools offered both half-day and full-day (minimum three days/week with at least seven hours) care. 372 students' data (grades 1-4; N = 153 half-day, N = 219 full-day; 47.4% male, 8.8±1.2y) were eligible for analyses. We assessed sports club membership and weekly training duration via questionnaire. Statistical analyses included Chi-square and Mann-Whitney-U-Tests.Results83% of half-day school students and 67% of full-day school students were sports club members (?2(1) = 12.31, p<.001). Weekly duration of training in sports clubs among sports club members (N = 266) also differed between the groups (mdn = 150 min in half-day, mdn = 120 min in full-day school students; z = -2.37, p = .018). Additional analyses stratified for age and gender showed similar results.ConclusionPrimary school students attending full-day schools engage less in organized sports outside school than half-day school students, regardless of age and gender. Future studies should examine if the detected lower engagement in sports club physical activity is compensated by physical activities in other settings such as school or non-organized leisure time.
| S-EPMC6874300 | biostudies-literature
Project description:BACKGROUND:Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. METHODS:We constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data. RESULTS:Of 95 907 patients, 941 (1%) died within 30 days. The multipath convolutional neural network predicted postoperative 30-day mortality with an area under the receiver operating characteristic curve of 0.867 (95% confidence interval [CI]: 0.835-0.899). This was higher than that for the deep neural network (0.825; 95% CI: 0.790-0.860), random forest (0.848; 95% CI: 0.815-0.882), support vector machine (0.836; 95% CI: 0.802-870), and logistic regression (0.837; 95% CI: 0.803-0.871). CONCLUSIONS:A deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient's risk for postoperative mortality.
| S-EPMC6993109 | biostudies-literature
Project description:Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions. Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting-XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables. Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64-0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69). Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.
| S-EPMC8044392 | biostudies-literature
Project description:ImportanceEarly childhood interventions have demonstrated positive effects on well-being. Whether full-day vs part-day attendance improves outcomes is unknown.ObjectiveTo evaluate the association between a full- vs part-day early childhood program and school readiness, attendance, and parent involvement.Design, setting, and participantsEnd-of-preschool follow-up of a nonrandomized, matched-group cohort of predominantly low-income, ethnic minority children enrolled in the Child-Parent Centers (CPC) for the full day (7 hours; n = 409) or part day (3 hours on average; n = 573) in the 2012-2013 school year in 11 schools in Chicago, Illinois.InterventionThe Midwest CPC Education Program provides comprehensive instruction, family-support, and health services from preschool to third grade.Main outcomes and measuresSchool readiness skills at the end of preschool, attendance and chronic absences, and parental involvement. The readiness domains in the Teaching Strategies GOLD Assessment System include a total of 49 items with a score range of 105-418. The specific domains are socioemotional with 9 items (score range, 20-81), language with 6 items (score range, 15-54), literacy with 12 items (score range, 9-104), math with 7 items (score, 8-60), physical health with 5 items (score range, 14-45), and cognitive development with 10 items (score range, 18-90).ResultsFull-day preschool participants had higher scores than part-day peers on socioemotional development (58.6 vs 54.5; difference, 4.1; 95% CI, 0.5-7.6; P = .03), language (39.9 vs 37.3; difference, 2.6; 95% CI, 0.6-4.6; P = .01), math (40.0 vs 36.4; difference, 3.6; 95% CI, 0.5-6.7; P = .02), physical health (35.5 vs 33.6; difference, 1.9; 95% CI, 0.5-3.2; P = .006), and the total score (298.1 vs 278.2; difference, 19.9; 95% CI, 1.2-38.4; P = .04). Literacy (64.5 vs 58.6; difference, 5.9; 95% CI, -0.07 to 12.4; P = .08) and cognitive development (59.7 vs 57.7; difference, 2.0; 95% CI, -2.4 to 6.3; P = .38) were not significant. Full-day preschool graduates also had higher rates of attendance (85.9% vs 80.4%; difference, 5.5; 95% CI, 2.6-8.4; P = .001) and lower rates of chronic absences (≥10% days missed; 53.0% vs 71.6%; difference, -18.6; 95% CI, -28.5 to -8.7; P = .001; ≥20% days missed; 21.2% vs 38.8%; difference -17.6%; 95% CI, -25.6 to -9.7; P < .001) but no differences in parental involvement.Conclusions and relevanceIn an expansion of the CPCs in Chicago, a full-day preschool intervention was associated with increased school readiness skills in 4 of 6 domains, attendance, and reduced chronic absences compared with a part-day program. These findings should be replicated in other programs and contexts.
| S-EPMC4505551 | biostudies-literature
Project description:Previous scoring models, such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, do not adequately predict the mortality of patients receiving mechanical ventilation in the intensive care unit. Therefore, this study aimed to apply machine learning algorithms to improve the prediction accuracy for 30-day mortality of mechanically ventilated patients. The data of 16,940 mechanically ventilated patients were divided into the training-validation (83%, n = 13,988) and test (17%, n = 2952) sets. Machine learning algorithms including balanced random forest, light gradient boosting machine, extreme gradient boost, multilayer perceptron, and logistic regression were used. We compared the area under the receiver operating characteristic curves (AUCs) of machine learning algorithms with those of the APACHE II and ProVent score results. The extreme gradient boost model showed the highest AUC (0.79 (0.77-0.80)) for the 30-day mortality prediction, followed by the balanced random forest model (0.78 (0.76-0.80)). The AUCs of these machine learning models as achieved by APACHE II and ProVent scores were higher than 0.67 (0.65-0.69), and 0.69 (0.67-0.71)), respectively. The most important variables in developing each machine learning model were APACHE II score, Charlson comorbidity index, and norepinephrine. The machine learning models have a higher AUC than conventional scoring systems, and can thus better predict the 30-day mortality of mechanically ventilated patients.
| S-EPMC8157228 | biostudies-literature
Project description:BackgroundCoronary artery bypass grafting (CABG) and surgical aortic valve replacement (AVR) are the 2 most common cardiac surgery procedures in North America. We derived and externally validated clinical models to estimate the likelihood of death within 30 days of CABG, AVR or combined CABG + AVR.MethodsWe obtained data from the CorHealth Ontario Cardiac Registry and several linked population health administrative databases from Ontario, Canada. We derived multiple logistic regression models from all adult patients who underwent CABG, AVR or combined CABG + AVR from April 2017 to March 2019, and validated them in 2 temporally distinct cohorts (April 2015 to March 2017 and April 2019 to March 2020).ResultsThe derivation cohorts included 13 435 patients who underwent CABG (30-d mortality 1.73%), 1970 patients who underwent AVR (30-d mortality 1.68%) and 1510 patients who underwent combined CABG + AVR (30-d mortality 3.05%). The final models for predicting 30-day mortality included 15 variables for patients undergoing CABG, 5 variables for patients undergoing AVR and 5 variables for patients undergoing combined CABG + AVR. Model discrimination was excellent for the CABG (c-statistic 0.888, optimism-corrected 0.866) AVR (c-statistic 0.850, optimism-corrected 0.762) and CABG + AVR (c-statistic 0.844, optimism-corrected 0.776) models, with similar results in the validation cohorts.InterpretationOur models, leveraging readily available, multidimensional data sources, computed accurate risk-adjusted 30-day mortality rates for CABG, AVR and combined CABG + AVR, with discrimination comparable to more complex American and European models. The ability to accurately predict perioperative mortality rates for these procedures will be valuable for quality improvement initiatives across institutions.
| S-EPMC8608458 | biostudies-literature
Project description:BackgroundBetween 2001 and 2006, the Ontario government introduced a menu of new primary care models, with elements such as patient enrolment and minimum group sizes, and various combinations of fee-for-service, capitation, pay-for-performance and salary. From the statistical perspective of physicians, as opposed to patients, we looked at the distribution of physician characteristics, group size and patient visit patterns across models to describe primary care practice in Ontario.MethodsUsing administrative data for fiscal year 2010/11 containing information on physician characteristics, patient rostering status, patient visits and other practice information, we described similarities and differences across primary care models.ResultsOur sample included 11?626 family physicians. Compared with physicians in the new primary care models, physicians in fee-for-service models are much more likely to work part-time and many, particularly younger and female physicians, do not work in full-year full-scope practices. Among the new primary care models, physicians in capitated models are slightly younger, are less likely to be an international medical graduate, work in smaller physician teams and do not practice in urban areas. On average, physicians saw and rostered 1888 patients. Although there is still substantial variation within each model, fee-for-service physicians saw the fewest patients; physicians in capitated models saw somewhat more, and those in the noncapitated models saw the most patients.InterpretationPractice and physician characteristics vary systematically across models. A high percentage of rostered patients see physicians outside the group with which they are rostered. Group-based primary care models may not have a large impact on group integration and continuity in the provision of primary care services.
| S-EPMC5173461 | biostudies-literature
Project description:BACKGROUND:Healthcare interventions on weekends have been associated with increased mortality and adverse clinical outcomes, but these findings are inconsistent. We hypothesized that patients admitted to hospital on weekends who have surgery have an increased risk of death compared with patients who are admitted and have surgery on weekdays. METHODS AND FINDINGS:This matched cohort study included 318,202 adult patients from Ontario health administrative and demographic databases, admitted to acute care hospitals from 1 January 2005 to 31 December 2015. A total of 159,101 patients who were admitted on weekends and underwent noncardiac surgery were classified by day of surgery (weekend versus weekday) and matched 1:1 to patients who both were admitted and had surgery on a weekday (Tuesday to Thursday); matching was based on age (in years), anesthesia basic unit value for the surgical procedure, median neighborhood household income quintile, resource utilization band (a ranking system of overall morbidity), rurality of home location, year of admission, and urgency of admission. Of weekend admissions, 16.2% (25,872) were elective and 53.9% (85,744) had surgery on the weekend of admission. The primary outcome was all-cause mortality within 30 days of the date of hospital admission. The 30-day all-cause mortality for patients admitted on weekends who had noncardiac surgery was 2.6% (4,211/159,101) versus 2.5% (3,901/159,101) for those who were admitted and had surgery on weekdays (adjusted odds ratio [OR] 1.05; 95% CI 1.00 to 1.11; P = 0.03). However, there was significant heterogeneity in the increased odds of death according to the urgency of admission and when surgery was performed (weekend versus weekday). For urgent admissions on weekends (n = 133,229), there was no significant increase in odds of mortality when surgery was performed on the weekend (adjusted OR 1.02; 95% CI 0.95 to 1.09; P = 0.7) or on a subsequent weekday (adjusted OR 1.05; 95% CI 0.98 to 1.12; P = 0.2) compared to urgent admissions on weekdays. Elective admissions on weekends (n = 25,782) had increased risk of death both when surgery was performed on the weekend (adjusted OR 3.30; 95% CI 1.98 to 5.49; P < 0.001) and when surgery was performed on a subsequent weekday (adjusted OR 2.70; 95% CI 1.81 to 4.03; P < 0.001). The main limitations of this study were the lack of data regarding reason for admission and cause of increased time interval from admission to surgery for some cases, the small number of deaths in some subgroups (i.e., elective surgery), and the possibility of residual unmeasured confounding from increased illness severity for weekend admissions. CONCLUSIONS:When patients have surgery during their hospitalization, admission on weekends in Ontario, Canada, was associated with a small but significant proportional increase in 30-day all-cause mortality, but there was significant heterogeneity in outcomes depending on the urgency of admission and when surgery was performed. An increased risk of death was found only for elective admissions on weekends; whether this is a function of patient-level factors or represents a true weekend effect needs to be further elucidated. These findings have potential implications for resource allocation in hospitals and the redistribution of elective surgery to weekends.
| S-EPMC6350956 | biostudies-literature
Project description:Predicting unplanned rehospitalizations has traditionally employed logistic regression models. Machine learning (ML) methods have been introduced in health service research and may improve the prediction of health outcomes. The objective of this work was to develop a ML model to predict 30-day all-cause rehospitalizations based on the French hospital medico-administrative database.This was a retrospective cohort study of all discharges in the year 2015 from acute-care inpatient hospitalizations in a tertiary-care university center comprising 4 French hospitals. The study endpoint was unplanned 30-day all-cause rehospitalization. Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB), and neural networks (NN) were applied to the collected data. The predictive performance of the models was evaluated using the H-measure and the area under the ROC curve (AUC).Our analysis included 118,650 hospitalizations, of which 4127 (3.5%) led to rehospitalizations via emergency departments. The RF model was the most performant model according to the H-measure (0.29) and the AUC (0.79). The performances of the RF, GB and NN models (H-measures ranged from 0.18 to 0. 29, AUC ranged from 0.74 to 0.79) were better than those of the LR model (H-measure = 0.18, AUC = 0.74); all P values <.001. In contrast, LR was superior to CART (H-measure = 0.16, AUC = 0.70), P < .0001.The use of ML may be an alternative to regression models to predict health outcomes. The integration of ML, particularly the RF algorithm, in the prediction of unplanned rehospitalization may help health service providers target patients at high risk of rehospitalizations and propose effective interventions at the hospital level.
| S-EPMC7717815 | biostudies-literature