Explore the vast range of titles available.

The COVID-19 crisis has highlighted the role of remote learning in higher education. Increased investments and understanding of the transition to remote learning today will yield access to high-quality learning. Using data from an institution with a diverse student population, the purpose of this study was to explore students’ transition to remote learning during the COVID-19 pandemic by examining aspects within the course that contributed to perceptions of a high-quality remote learning experience. Five elements of the remote learning experience were statistically significant predictors of course quality ratings: ratings of the quality of student supports (Academic Advising, Tutoring, Internships, Financial Aid, etc.), ratings of connectedness to instructors in at least one class, the use of many learning activities by instructors, clear course organization by instructors, and clear communication used by instructors. Analysis of qualitative student interviews reinforced these findings and provided further support for the importance of reliable technology during remote learning.

The purpose of this study was to examine student's perceptions of the iPad applications-assisted instruction as part of their learning. In this study, multiple features and applications were evaluated as an instructional tool. Fifteen participants responded to the survey indicating their perceptions of the iPad and applications used as part of their course delivery. Overall, participants favored the use of this technology as an instructional tool in their learning and indicated several aspects that could be improved. J Allied Health 2016; 45(3):e49-e57.

We report baseline results of a community-based, targeted, low-dose CT (LDCT) lung cancer screening pilot in deprived areas of Manchester. Ever smokers, aged 55–74 years, were invited to ‘lung health checks’ (LHCs) next to local shopping centres, with immediate access to LDCT for those at high risk (6-year risk ≥1.51%, PLCOM2012 calculator). 75% of attendees (n=1893/2541) were ranked in the lowest deprivation quintile; 56% were high risk and of 1384 individuals screened, 3% (95% CI 2.3% to 4.1%) had lung cancer (80% early stage) of whom 65% had surgical resection. Taking lung cancer screening into communities, with an LHC approach, is effective and engages populations in deprived areas.

Background The Ultra-Low Emission Zone (ULEZ), introduced in Central London in April 2019, aims to enhance air quality and improve public health. The Children's Health in London and Luton (CHILL) study evaluates the impact of the ULEZ on children's health. This analysis focuses on the one-year impacts on the shift towards active travel to school. Methods CHILL is a prospective parallel cohort study of ethnically diverse children, aged 6–9 years attending 84 primary schools within or with catchment areas encompassing London’s ULEZ (intervention) and Luton (non-intervention area). Baseline (2018/19) and one-year follow-up (2019/20) data were collected at school visits from 1992 (58%) children who reported their mode of travel to school ‘today’ (day of assessment). Multilevel logistic regressions were performed to analyse associations between the introduction of the ULEZ and the likelihood of switching from inactive to active travel modes, and vice-versa. Interactions between intervention group status and pre-specified effect modifiers were also explored. Results Among children who took inactive modes at baseline, 42% of children in London and 20% of children in Luton switched to active modes. For children taking active modes at baseline, 5% of children in London and 21% of children in Luton switched to inactive modes. Relative to the children in Luton, children in London were more likely to have switched from inactive to active modes (OR 3.51, 95% CI 1.68–7.31). Children in the intervention group were also less likely to switch from active to inactive modes (OR 0.22, 0.12–0.41). Moderator analyses showed that children living further from school were more likely to switch from inactive to active modes (OR 5.20; 1.67–15.21) compared to those living closer (OR 1.54, 0.33-7.21). Conclusions Implementation of clean air zones can increase uptake of active travel to school and was particularly associated with more sustainable and active travel in children living further from school.

We report results from the second annual screening round (T1) of Manchester’s ‘Lung Health Check’ pilot of community-based lung cancer screening in deprived areas (undertaken June to August 2017). Screening adherence was 90% (n=1194/1323): 92% of CT scans were classified negative, 6% indeterminate and 2.5% positive; there were no interval cancers. Lung cancer incidence was 1.6% (n=19), 79% stage I, treatments included surgery (42%, n=9), stereotactic ablative radiotherapy (26%, n=5) and radical radiotherapy (5%, n=1). False-positive rate was 34.5% (n=10/29), representing 0.8% of T1 participants (n=10/1194). Targeted community-based lung cancer screening promotes high screening adherence and detects high rates of early stage lung cancer.

IntroductionLow-dose CT (LDCT) screening of high-risk smokers reduces lung cancer (LC) specific mortality. Determining screening eligibility using individualised risk may improve screening effectiveness and reduce harm. Here, we compare the performance of two risk prediction models (PLCOM2012 and Liverpool Lung Project model (LLPv2)) and National Lung Screening Trial (NLST) eligibility criteria in a community-based screening programme.MethodsEver-smokers aged 55–74, from deprived areas of Manchester, were invited to a Lung Health Check (LHC). Individuals at higher risk (PLCOM2012 score ≥1.51%) were offered annual LDCT screening over two rounds. LLPv2 score was calculated but not used for screening selection; ≥2.5% and ≥5% thresholds were used for analysis.ResultsPLCOM2012 ≥1.51% selected 56% (n=1429) of LHC attendees for screening. LLPv2 ≥2.5% also selected 56% (n=1430) whereas NLST (47%, n=1188) and LLPv2 ≥5% (33%, n=826) selected fewer. Over two screening rounds 62 individuals were diagnosed with LC; representing 87% (n=62/71) of 6-year incidence predicted by mean PLCOM2012 score (5.0%). 26% (n=16/62) of individuals with LC were not eligible for screening using LLPv2 ≥5%, 18% (n=11/62) with NLST criteria and 7% (n=5/62) with LLPv2 ≥2.5%. NLST eligible Manchester attendees had 2.5 times the LC detection rate than NLST participants after two annual screens (≈4.3% (n=51/1188) vs 1.7% (n=438/26 309); p<0.0001). Adverse measures of health, including airflow obstruction, respiratory symptoms and cardiovascular disease, were positively correlated with LC risk. Coronary artery calcification was predictive of LC (adjOR 2.50, 95% CI 1.11 to 5.64; p=0.028).ConclusionProspective comparisons of risk prediction tools are required to optimise screening selection in different settings. The PLCOM2012 model may underestimate risk in deprived UK populations; further research focused on model calibration is required.