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In this study, 676 middle school students in grades 6, 7 and 8 were asked to complete a survey online, during class time, which asked them their opinions on using iPads in school. Responses to the survey questions were generally positive however comments written at the end were very critical of the initiative. Significant differences were found when comparing the responses of 6th, 7th and 8th grade students. Seventh grade students, who had been using the iPad since 6th grade, were significantly more positive than the 6th or 8th grade students. Also, the younger students in grade 6 were significantly more positive about using iPads than students in 8th grade. Gender differences were also found, with boys being more positive in their opinions than girls. Distraction and technical problems were among the problems students commented on, as well as eye strain from using the iPad for long periods of time. Increased engagement was evident from the high percentage of students who either agreed or strongly agreed with the statement that “the iPad makes learning more fun and interesting”.

In education, new technologies are used to improve the process of teaching and learning. This study examined middle school teachers' perceptions regarding the use of iPads for instruction. The participants, 53 middle school teachers in Western New York, responded to an online survey, asking them questions about how they felt about the 1:1 iPad initiative at their school. Data analysis included open and axial coding for identification of themes and patterns, as well as quantitative statistical analysis. The results showed mixed findings, as some teachers believed that iPads had a positive impact on the teaching-learning process by improving student engagement and communication, while some responded with concerns that iPads caused student distraction and allowed off-task behaviors in the classroom. The findings also suggest that teachers need targeted professional development on pedagogical and practical use of this technology to be able to successfully integrate it into their practice.

Background The COVID‐19 pandemic, caused by the SARS‐CoV‐2 virus, has resulted in illness, deaths and societal disruption on a global scale. Societies have implemented various control measures to reduce transmission of the virus and mitigate its impact. Individual behavioural changes are crucial to the successful implementation of these measures. One commonly recommended measure to limit risk of infection is distancing. It is important to identify those factors that can predict the uptake and maintenance of distancing. Objectives We aimed to identify and synthesise the evidence on malleable psychological and psychosocial factors that determine uptake and adherence to distancing aimed at reducing the risk of infection or transmission of COVID‐19. Search Methods We searched various literature sources including electronic databases (Medline ALL, Child Development & Adolescent Studies, ERIC, PsycInfo, CINAHL & Web of Science), web searches, conference proceedings, government reports, other repositories of literature and grey literature. The search strategy was built around three concepts of interest including (1) context (terms relating to COVID‐19), (2) behaviour of interest and (3) terms related to psychological and psychosocial determinants of COVID‐19 Health‐Related Behaviours and adherence or compliance with distancing, to capture malleable determines. Searches capture studies up until October 2021. Selection Criteria Eligibility criteria included observational studies (both retrospective and prospective) and experimental studies that measure and report malleable psychological and psychosocial determinants and distancing (social and/or physical) at an individual level, amongst the general public. We defined physical distancing as, maintaining the recommended distance from others when physically present. And social distancing being defined as, minimising social contact with those outside of your own household. Screening was supported by the Cochrane Crowd. Studies' titles and s were screened against the eligibility criteria by three independent screeners. Following this, all potentially relevant studies were screened at full‐text level by the research team. All conflicts between screeners were resolved by discussion between the core research team. Data Collection and Analysis All data extraction was managed in EPPI‐Reviewer software. All eligible studies, identified through full‐text screening were extracted by one author. We extracted data on study information, population, determinant, behaviour and effects. A second author checked data extraction on 20% of all included papers. All conflicts were discussed by the two authors until consensus was reached. We assessed methodological quality of all included studies using an adapted version of the Joanna Briggs Institute Quality appraisal tool. Main Results A total of 91 studies were suitable for inclusion in the review, representing 199,604 participants. The vast majority of studies had samples from the general public, with 15 of the studies focusing on specific samples. The majority of studies included participants over 18 years old, with 5 reporting on specific ages (adolescents and adults over 65). The quality of 29 of the studies was rated as unclear, 48 were rated as low, and 14 rated high risk of bias, predominately due to lack of reporting of recruitment, sample characteristics and methodology. Overall the majority of these relationships were weak. Stronger relationships were observed between attitudes, social norms, perceived behavioural control and both social and physical distancing. And between worry, response effectiveness, self‐efficacy and social distancing. However, there is a high level of heterogeneity in the findings. This heterogeneity might be, partly, due to the differences in measurement of the determinants and distancing across studies. Authors' Conclusions The findings from this review indicate that social distancing behaviours are more likely to be undertaken by people who are worried about COVID‐19 and who believe that social distancing is an effective way of avoiding COVID‐19. Physical distancing behaviours are more likely to be undertaken by those who believe that they can control physical distancing from others, who believe that physical distancing is the social norm and who have a positive attitude to engaging in this behaviour. It is important to understand how to strengthen these behavioural determinants to develop effective interventions to promote distancing behaviours in any potential future waves of COVID‐19, and other respiratory infections.

Background The COVID‐19 pandemic, caused by the SARS‐CoV‐2 virus, has resulted in illness, deaths and societal disruption on a global scale. Societies have implemented various control measures to reduce transmission of the virus and mitigate its impact. Individual behavioural changes are crucial to the successful implementation of these measures. Common recommended measures to limit risk of infection include frequent handwashing, reducing the frequency of social interactions and the use of face coverings. It is important to identify those factors that can predict the uptake and maintenance of these protective behaviours. Objectives We aimed to identify and map the existing evidence (published and unpublished) on psychological and psychosocial factors that determine uptake and adherence to behaviours aimed at reducing the risk of infection or transmission of COVID‐19. Search Methods Our extensive search included electronic databases (n = 12), web searches, conference proceedings, government reports, other repositories including both published peer reviewed, pre‐prints and grey literature. The search strategy was built around three concepts of interest including (1) context (terms relating to COVID‐19), (2) behaviours of interest and (3) terms related to psychological and psychosocial determinants of COVID Health‐Related Behaviours and adherence or compliance with recommended behaviours, to capture both malleable and non‐malleable determinants (i.e. determinants that could be changed and those that could not). Selection Criteria This Evidence and Gap Map (EGM) includes all types of studies examining determinants of common recommended behaviours aimed at mitigating human‐to‐human spread of COVID‐19. All potential malleable and non‐malleable determinants of one or more behaviours are included in the map. As part of the mapping process, categories are used to group determinants. The mapping categories were based on a previous rapid review by Hanratty 2021. These include: ‘behaviour’, ‘cognition’, ‘demographics’, ‘disease’, ‘emotions’, ‘health status’, ‘information’, ‘intervention’, and ‘knowledge’. Those not suitable for categorisation in any of these groups are included in the map as ‘other’ determinants. Data Collection and Analysis Results were imported to a bibliographic reference manager where duplications of identical studies gathered from multiple sources were removed. Data extraction procedures were managed in EPPI‐Reviewer software. Information on study type, population, behaviours measured and determinants measured were extracted. We appraised the methodological quality of systematic reviews with AMSTAR‐2. We did not appraise the quality of primary studies in this map. Main Results As of 1 June 2022 the EGM includes 1034 records reporting on 860 cross‐sectional, 68 longitudinal, 78 qualitative, 25 reviews, 62 interventional, and 39 other studies (e.g., mixed‐methods approaches). The map includes studies that measured social distancing (n = 487), masks and face coverings (n = 382), handwashing (n = 308), physical distancing (n = 177), isolation/quarantine (n = 157), respiratory hygiene/etiquette (n = 75), cleaning surfaces (n = 59), and avoiding touching the T‐zone (n = 48). There were 333 studies that assessed composite measures of two or more behaviours. The largest cluster of determinants was ‘demographics’ (n = 730 studies), followed by ‘cognition’ (n = 496 studies) and determinants categorised as ‘other’ (n = 447). These included factors such as ‘beliefs’, ‘culture’ and ‘access to resources’. Less evidence is available for some determinants such as ‘interventions’ (n = 99 studies), ‘information’ (n = 101 studies), and ‘behaviour’ (149 studies). Authors' Conclusions This EGM provides a valuable resource for researchers, policy‐makers and the public to access the available evidence on the determinants of various COVID‐19 health‐related behaviours. The map can also be used to help guide research commissioning, by evidence synthesis teams and evidence intermediaries to inform policy during the ongoing pandemic and potential future outbreaks of COVID‐19 or other respiratory infections. Evidence included in the map will be explored further through a series of systematic reviews examining the strength of the associations between malleable determinants and the uptake and maintenance of individual protective behaviours.

Background Telephone quit lines are accessible to many smokers and are used to engage motivated smokers to make quit attempts. Smoking cessation counselling provided via telephone can either be reactive (i.e. primarily involving the provision of evidence-based information), or proactive (i.e. primarily involving repeated, sequenced calls from and interaction with trained cessation counsellors). Some studies have found proactive telephone counselling more effective and this trial will investigate whether or not proactive telephone support for smoking cessation, delivered through the National Health Service (NHS) Smoking Helpline is more effective or cost-effective than reactive support. It will also investigate whether or not providing nicotine replacement therapy (NRT), in addition to telephone counselling, has an adjunctive impact on smoking cessation rates and whether or not this is cost effective. Methods This will be a parallel group, factorial design RCT, conducted through the English national NHS Smoking Helpline which is run from headquarters in Glasgow. Participants will be smokers who call the helpline from any location in England and who wish to stop smoking. If 644 participants are recruited to four equally-sized trial groups (total sample size = 2576), the trial will have 90% power for detecting a treatment effect (Odds Ratio) of 1.5 for each of the two interventions: i) proactive versus reactive support and ii) the offer of NRT versus no offer. The primary outcome measure for the study is self-reported, prolonged abstinence from smoking for at least six months following an agreed quit date. A concurrent health economic evaluation will investigate the cost effectiveness of the two interventions when delivered via a telephone helpline. Discussion The PORTSSS trial will provide high quality evidence to determine the most appropriate kind of counselling which should be provided via the NHS Smoking Helpline and also whether or not an additional offer of cost-free NRT is effective and cost effective for smoking cessation. Trial Registration (clinicaltrials.gov): NCT00775944

Examines the impact of intensive management of type 1 diabetes (T1D) from diagnosis on HbA1c 12 months from diagnosis. Compares HbA1c measured 12 months after diagnosis for 70 consecutively newly diagnosed children with type 1 diabetes (T1D) following implementation of an intensive management protocol with 70 children consecutively diagnosed immediately pre-implementation. Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.

This research presents findings from a two-part study. In the first part, graduate students taking online courses were given a course evaluation form. Student responses from online abbreviated summer sessions were compared to student responses from online full-semester courses. Both the intensive and full-semester courses were taught by the same professor and both had identical requirements in terms of assignments and exams. The independent variable was the length of time taken to complete the requirements, with the dependent variables being satisfaction with the course, perceived learning, and academic performance. A statistical analysis of the data found significant differences in a number of areas.

aims: To examine the impact of intensive management of type 1 diabetes (T1D) from diagnosis on HbA1c 12 months from diagnosis. methods: HbA1c measured 12 months after diagnosis for 70 consecutively newly diagnosed children with T1D following implementation of an intensive management protocol was compared with 70 children consecutively diagnosed immediately pre-implementation. Intensive management involved carbohydrate counting and flexible insulin dosing from first meal with subcutaneous insulin, targeted blood glucose levels from 4–8mmol/L irrespective of time of day, avoidance of twice daily insulin regimen and promotion of continuous glucose monitoring (CGM). HbA1c, diabetes technology use and insulin regimen at 12 months post-diagnosis were compared. results: The post-intensive management implementation cohort had an improved mean HbA1c of 58.2±15.3mmol/mol vs 63.7±10.7mmol/mol at 12 months (p=0.014). The proportion of young people with diabetes meeting a target HbA1c of <53mmol/mol at 12 months improved from 11% to 40% (p=<0.001). There was a reduction of twice daily insulin regimen from 66% to 11% (p=<0.001), and increased CGM use from 57% to 76% (p=0.02). conclusion: Intensive management when implemented with consistent messaging from the multi-disciplinary team resulted in clinic-wide improvements in HbA1c and the proportion meeting HbA1c targets.

Objective To describe the epidemiology of pediatric type 1 diabetes over 50 years in Canterbury, New Zealand. Further, to explore variation in case presentation according to age, gender, ethnicity, urban/rural character, socio‐economic deprivation and immunogenetic features. Research Design and Methods Prospective ascertainment of cases commenced in 1982, and incident cases presenting 1970–1982 were ascertained retrospectively from clinical records. Eligibility criteria included diagnosis of type 1 diabetes by a physician and commencement of insulin therapy at diagnosis and age less than 15 years. Data collection included name, hospital number, date of birth, date of diagnosis, and date of initiation of insulin treatment. Full address at diagnosis was assigned an urban–rural classification, and a deprivation score. HLA‐DQ susceptibility alleles and diabetes associated autoantibodies were determined. Results The incidence of type 1 diabetes increased more than 5‐fold (3.9% per annum) over 50 years for the entire cohort. The mean for 5‐year periods, starting from 1970, increased from 5.3 to 29.0 cases per 100,000 person years. Incidence was greatest in the 10–14 year age group. The cohort is predominantly European (89.4%), but there has been an increase in cases identifying as New Zealand Māori in the last three decades. Weak evidence was found for reduced incidence of type 1 diabetes in rural regions (adjusted IRR = 0.70, 95%CI 0.52 to 0.91, p = 0.011). Conclusions The incidence of type 1 diabetes in children aged less than 15 years continues to increase with time. Incidence was significantly affected by age, ethnicity, and urban/rural characterization of address at diagnosis.

The authors of this research paper have alerted us to some errors in table 2, which presents a comparison of baseline characteristics by treatment group (BMJ 2012;344:e1696, doi: 10.1136/bmj.e1696 ). The total (n=2591) values for the rows that list previous smoking support are incorrect and should have read: for nicotine replacement therapy without prescription 516 (19.9), for nicotine replacement therapy from health professional 123 (4.7), for bupropion 27 (1.0), for varenicline 60 (2.3), for NHS Stop Smoking Service 21 (0.8), for other quitline 2 (<0.1), and for other 30 (1.1).