Background People with schizophrenia from families that express high levels of criticism, hostility, or over involvement, have more frequent relapses than people with similar problems from families that tend to be less expressive of emotions. Forms of psychosocial intervention, designed to reduce these levels of expressed emotions within families, are now widely used. Objectives To estimate the effects of family psychosocial interventions in community settings for people with schizophrenia or schizophrenia-like conditions compared with standard care. Search strategy We updated previous searches by searching the Cochrane Schizophrenia Group Trials Register (September 2008). Selection criteria We selected randomised or quasi-randomised studies focusing primarily on families of people with schizophrenia or schizoaffective disorder that compared community-orientated family-based psychosocial intervention with standard care. Data collection and analysis We independently extracted data and calculated fixed-effect relative risk (RR), the 95% confidence intervals (CI) for binary data, and, where appropriate, the number needed to treat (NNT) on an intention-to-treat basis. For continuous data, we calculated mean differences (MD). Main results This 2009-10 update adds 21 additional studies, with a total of 53 randomised controlled trials included. Family intervention may decrease the frequency of relapse (n = 2981, 32 RCTs, RR 0.55 CI 0.5 to 0.6, NNT 7 CI 6 to 8), although some small but negative studies might not have been identified by the search. Family intervention may also reduce hospital admission (n = 481, 8 RCTs, RR 0.78 CI 0.6 to 1.0, NNT 8 CI 6 to 13) and encourage compliance with medication (n = 695, 10 RCTs, RR 0.60 CI 0.5 to 0.7, NNT 6 CI 5 to 9) but it does not obviously affect the tendency of individuals/families to leave care (n = 733, 10 RCTs, RR 0.74 CI 0.5 to 1.0). Family intervention also seems to improve general social impairment and the levels of expressed emotion within the family. We did not find data to suggest that family intervention either prevents or promotes suicide. Authors’ conclusions Family intervention may reduce the number of relapse events and hospitalisations and would therefore be of interest to people with schizophrenia, clinicians and policy makers. However, the treatment effects of these trials may be overestimated due to the poor methodological quality. Further data from trials that describe the methods of randomisation, test the blindness of the study evaluators, and implement the CONSORT guidelines would enable greater confidence in these findings.

No abstract

No abstract

The public have an incomplete understanding of antibiotic resistance and misperceptions about it and its causes and do not believe they contribute to its development. These data can be used to inform interventions to change the public's beliefs about how they can contribute to tackling this global issue.

BackgroundCitation screening is time consuming and inefficient. We sought to evaluate the performance of Abstrackr, a semi-automated online tool for predictive title and abstract screening.MethodsFour systematic reviews (aHUS, dietary fibre, ECHO, rituximab) were used to evaluate Abstrackr. Citations from electronic searches of biomedical databases were imported into Abstrackr, and titles and abstracts were screened and included or excluded according to the entry criteria. This process was continued until Abstrackr predicted and classified the remaining unscreened citations as relevant or irrelevant. These classification predictions were checked for accuracy against the original review decisions. Sensitivity analyses were performed to assess the effects of including case reports in the aHUS dataset whilst screening and the effects of using larger imbalanced datasets with the ECHO dataset. The performance of Abstrackr was calculated according to the number of relevant studies missed, the workload saving, the false negative rate, and the precision of the algorithm to correctly predict relevant studies for inclusion, i.e. further full text inspection.ResultsOf the unscreened citations, Abstrackr’s prediction algorithm correctly identified all relevant citations for the rituximab and dietary fibre reviews. However, one relevant citation in both the aHUS and ECHO reviews was incorrectly predicted as not relevant. The workload saving achieved with Abstrackr varied depending on the complexity and size of the reviews (9 % rituximab, 40 % dietary fibre, 67 % aHUS, and 57 % ECHO). The proportion of citations predicted as relevant, and therefore, warranting further full text inspection (i.e. the precision of the prediction) ranged from 16 % (aHUS) to 45 % (rituximab) and was affected by the complexity of the reviews. The false negative rate ranged from 2.4 to 21.7 %. Sensitivity analysis performed on the aHUS dataset increased the precision from 16 to 25 % and increased the workload saving by 10 % but increased the number of relevant studies missed. Sensitivity analysis performed with the larger ECHO dataset increased the workload saving (80 %) but reduced the precision (6.8 %) and increased the number of missed citations.ConclusionsSemi-automated title and abstract screening with Abstrackr has the potential to save time and reduce research waste.