A Case Study of the Benefits of the Science Learning Partnerships in Early Years and Primary Education in England

This paper charts the recent history of the STEM Learning UK contracts with local Science Learning Partnerships (SLPs) and identifies what leadership has been made available to support the Early Years and Primary school sector. A case study approach is taken using ‘Super SLP’ hubs in England. Curriculum Hubs exist in core subject areas such as maths, English, science and computing. They have recently been expanded to include Behaviour Hubs. This forms the current DfE strategy of Teaching School Hubs (TSHs), i.e., to offer system support and a full career-length support for all stages of teacher-career and leadership development. This paper charts the changes to the Early Years (EY) and Primary teacher support networks, in science particularly, and examines what they provide and how this can be improved, and discusses, through session evaluation and feedback, what teachers have appreciated the most.</jats:p

Smoking cessation in severe mental ill health : what works? an updated systematic review and meta-analysis

BACKGROUND: People with severe mental ill health are more likely to smoke than those in the general population. It is therefore important that effective smoking cessation strategies are used to help people with severe mental ill health to stop smoking. This study aims to assess the effectiveness and cost -effectiveness of smoking cessation and reduction strategies in adults with severe mental ill health in both inpatient and outpatient settings. METHODS: This is an update of a previous systematic review. Electronic databases were searched during September 2016 for randomised controlled trials comparing smoking cessation interventions to each other, usual care, or placebo. Data was extracted on biochemically-verified, self-reported smoking cessation (primary outcome), as well as on smoking reduction, body weight, psychiatric symptom, and adverse events (secondary outcomes). RESULTS: We included 26 trials of pharmacological and/or behavioural interventions. Eight trials comparing bupropion to placebo were pooled showing that bupropion improved quit rates significantly in the medium and long term but not the short term (short term RR = 6.42 95% CI 0.82-50.07; medium term RR = 2.93 95% CI 1.61-5.34; long term RR = 3.04 95% CI 1.10-8.42). Five trials comparing varenicline to placebo showed that that the addition of varenicline improved quit rates significantly in the medium term (RR = 4.13 95% CI 1.36-12.53). The results from five trials of specialised smoking cessation programmes were pooled and showed no evidence of benefit in the medium (RR = 1.32 95% CI 0.85-2.06) or long term (RR = 1.33 95% CI 0.85-2.08). There was insufficient data to allowing pooling for all time points for varenicline and trials of specialist smoking cessation programmes. Trials suggest few adverse events although safety data were not always reported. Only one pilot study reported cost effectiveness data. CONCLUSIONS: Bupropion and varenicline, which have been shown to be effective in the general population, also work for people with severe mental ill health and their use in patients with stable psychiatric conditions. Despite good evidence for the effectiveness of smoking cessation interventions for people with severe mental ill health, the percentage of people with severe mental ill health who smoke remains higher than that for the general population

A randomised controlled trial linking mental health inpatients to community smoking cessation supports: A study protocol

<p>Abstract</p> <p>Background</p> <p>Mental health inpatients smoke at higher rates than the general population and are disproportionately affected by tobacco dependence. Despite the advent of smoke free policies within mental health hospitals, limited systems are in place to support a cessation attempt post hospitalisation, and international evidence suggests that most smokers return to pre-admission smoking levels following discharge. This protocol describes a randomised controlled trial that will test the feasibility, acceptability and efficacy of linking inpatient smoking care with ongoing community cessation support for smokers with a mental illness.</p> <p>Methods/Design</p> <p>This study will be conducted as a randomised controlled trial. 200 smokers with an acute mental illness will be recruited from a large inpatient mental health facility. Participants will complete a baseline survey and will be randomised to either a multimodal smoking cessation intervention or provided with hospital smoking care only. Randomisation will be stratified by diagnosis (psychotic, non-psychotic). Intervention participants will be provided with a brief motivational interview in the inpatient setting and options of ongoing smoking cessation support post discharge: nicotine replacement therapy (NRT); referral to Quitline; smoking cessation groups; and fortnightly telephone support. Outcome data, including cigarettes smoked per day, quit attempts, and self-reported 7-day point prevalence abstinence (validated by exhaled carbon monoxide), will be collected via blind interview at one week, two months, four months and six months post discharge. Process information will also be collected, including the use of cessation supports and cost of the intervention.</p> <p>Discussion</p> <p>This study will provide comprehensive data on the potential of an integrated, multimodal smoking cessation intervention for persons with an acute mental illness, linking inpatient with community cessation support.</p> <p>Trial Registration</p> <p>Australian and New Zealand Clinical Trials Registry ANZTCN: <a href="http://www.anzctr.org.au/ACTRN12609000465257.aspx">ACTRN12609000465257</a></p

Demographic, health and socioeconomic characteristics related to lung cancer diagnosis: a population analysis in New South Wales, Australia

Abstract Background Lung cancer is a major cause of health loss internationally, and in Australia. Most of that loss is inequitably concentrated among vulnerable or disadvantaged people and amenable to prevention and earlier detection. In response, best practice lung cancer care considers peoples’ background, circumstances and care needs. Comprehensive, person level descriptions of demographic, health and discrete socio-economic disadvantage related factors are therefore required to inform best practice. We examine population wide correlations of demographic, health and socioeconomic characteristics with lung cancer diagnosis for use in cancer control programs, including screening. Methods A study of 5,504,777 (89.9%) adults living in New South Wales and participating in Australia’s Census in August 2016 with subsequent follow-up to the end of 2018. The Australian Bureau of Statistics’ (ABS) person-level integrated data asset linked census records with the NSW population cancer registry which includes primary site. Our study compared census participants who did not experience cancer in the follow-up period with those diagnosed with lung cancer, (n = 6160 and ICD10 C33-34). Outcomes are expressed as the adjusted relative odds (aOR) of incident lung cancer among adults in the community and measured using multi-variable logistic regression models. Validated ABS methods informed categorisation of social and economic variables. Results Multivariable comparison of those with lung cancer and those without a first cancer diagnosis (3276 lung cancers among 2,484,145 males; 2884 lung cancers among 2,944,148 females) showed associations with increasing age, varying ancestry, living alone (aOR = 1.30 95% CI 1.19–1.42 males; 1.24 95% CI 1.14–1.35 females), number of health conditions medicated, less than Year 12 education (aOR = 1.40 95% CI 1.30–1.51 males; 1.37 95% CI 1.27–1.48 females) and housing authority rental (aOR = 1.69 95% CI 1.48–1.94 males; 1.85 95% CI 1.63–2.11 females). Additional associations occurred among males with low income, disabilities before age 70, those unemployed and labouring occupations. As numbers of characteristics increased, so did the likelihood of lung cancer. Conclusion We provided a population wide description of characteristics relevant to lung cancer diagnosis. Deeper knowledge of these characteristics inform continuing development of lung cancer programs in prevention (e.g. tobacco control) and detection (e.g. lung cancer screening), then help prioritise targeted delivery of those programs