ObjectiveHospital at home (HAH) for chronic obstructive pulmonary disease exacerbation selected by low-risk Dyspnoea, Eosinopenia, Consolidation, Acidaemia and atrial Fibrillation (DECAF) score is clinical and cost-effective; DECAF is a prognostic score indicating risk of mortality. Up to 50% of admitted patients are suitable, a much larger proportion than earlier services. Introduction of new models of care is challenging, but may be facilitated by informed engagement with stakeholders. This qualitative study sought to identify facilitators and barriers to implementation of HAH.DesignSemistructured interviews, data were analysed using thematic-construct analysis.SettingInterviews were conducted within patients’ homes and hospitals in North East England.Participants89 participants were interviewees; 44 patients, 15 carers, 15 physicians, 11 specialist nurses and 4 managers.ResultsFacilitators include the following: (1) availability of home comforts and maintaining independence (with positive influences on perceived rate of recovery, sleep quality and convenience for friends, family and carers) and (2) confidence in the continuity of HAH care. Barriers include the following: (1) fear of being alone at home; (2) privacy issues and not wanting visitors and (3) resistance to change. Clinician concerns occasionally delayed return home, principally during the early phase of the trial. Nurses cited higher workload and greater responsibility, but with additional resource and training; overall, they viewed HAH positively. Operational concerns included keeping medical records in a patient’s home and inability to capture activity within current payment systems.ConclusionHAH selected by DECAF was preferred to inpatient care by most patients and their families. Implementation in other hospitals will require education, training and service planning, tailored to overcome the identified barriers.Trial registration numberISRCTN29082260.
Background A core outcome set (COS) is a standardised collection of outcomes to be collected and reported in all trials within a research area. A COS can reduce reporting bias and facilitate evidence synthesis. This is currently unavailable for use in community-based bipolar trials. This research aimed to develop such a COS, with input from a full range of stakeholders. Methods A co-production approach was used throughout. A longlist of outcomes was derived from focus groups with people with a bipolar diagnosis and carers, interviews with healthcare professionals and a rapid review of outcomes listed in bipolar trials on the Cochrane database. An expert panel with personal and/or professional experience of bipolar participated in a modified Delphi process and the COS was finalised at a consensus meeting. Results Fifty participants rated the importance of each outcome. Sixty-six outcomes were included in Round 1 of the questionnaire; 13 outcomes were added by Round 1 participants and were rated in Round 2. Seventy-six percent of participants (n = 38) returned to Round 2 and 60 outcomes, including 4 outcomes added by participants in Round 1, received a rating of 7–9 by >70% and 1–3 by <25% of the sample. Fourteen participants finalised a COS containing 11 outcomes at the consensus meeting: personal recovery; connectedness; clinical recovery of bipolar symptoms; mental health and wellbeing; physical health; self-monitoring and management; medication effects; quality of life; service outcomes; experience of care; and use of coercion. Conclusions This COS is recommended for use in community-based bipolar trials to ensure stakeholder-relevant outcomes, facilitate data synthesis, and transparent reporting. The COS includes guidance notes for each outcome to allow the identification of suitable measurement instruments. Further validation is recommended for use with a wide range of communities and to achieve standardised measurement.
In this article, we present an exemplar of the initial theory-building phase of theory-driven evaluation for the PARTNERS2 project, a collaborative care intervention for people with experience of psychosis in England. Initial theory-building involved analysis of the literature, interviews with key leaders and focus groups with service users. The initial programme theory was developed from these sources in an iterative process between researchers and stakeholders (service users, practitioners, commissioners) involving four activities: articulation of 442 explanatory statements systematically developed using realist methods; debate and consensus; communication; and interrogation. We refute two criticisms of theory-driven evaluation of complex interventions. We demonstrate how the process of initial theory-building made a meaningful contribution to our complex intervention in five ways. Although time-consuming, it allowed us to develop an internally coherent and well-documented intervention. This study and the lessons learnt provide a detailed resource for other researchers wishing to build theory for theory-driven evaluation.
Identifying which parts of a Web-page contain target content (e.g., the portion of an online news page that contains the actual article) is a significant problem that must be addressed for many Webbased applications. Most approaches to this problem involve crafting hand-tailored rules or scripts to extract the content, customized separately for particular Web sites. Besides requiring considerable time and effort to implement, hand-built extraction routines are brittle: they fail to properly extract content in some cases and break when the structure of a site's Web-pages changes. In this work we treat the problem of identifying content as a sequence labeling problem, a common problem structure in machine learning and natural language processing. Using a Conditional Random Field sequence labeling model, we correctly identify the content portion of web-pages anywhere from 80-97% of the time depending on experimental factors such as ensuring the absence of duplicate documents and application of the model against unseen sources. Categories and Subject Descriptors General TermsAlgorithms, Experimentation. KeywordsConditional random fields, content identification, maximum entropy markov models, sequence labeling. INTRODUCTIONWeb pages containing news stories also include many other pieces of extraneous information such as navigation bars, JavaScript, images and advertisements. There are a number of tasks that necessitate the extraction of just the news article from these pages. This might be done to provide input into a database or into an application such as a Natural Language tool, index for a search engine or duplicate detection tool. Another cause to extract just the news story is to re-display it on a small screen such as a cell phone or PDA. An example of identifying the embedded news article can be seen in Figure 1.Typically, content extraction is done via a hand-crafted tool targeted to handle a single web page format. This approach is brittle in that when the page format changes, the extractor is likely to break. Additionally, it is labor intensive since a new extractor must be written to handle each unique page format. In our experience, web page formats change fairly quickly and custom extractors often become obsolete a short time after they are written. Further, some websites use multiple formats concurrently and identifying each one and handling them properly makes this a complex task. As part of a larger project, we initially developed such site-specific content extractors and found them to be unworkable as a long-term solution due to the aforementioned problems.The approach described in this paper is meant to overcome these issues. The data set for this work consisted of web pages from 27 different news sites. The sites are visually similar in that they contain a news article surrounded by other information. However, the underlying HTML for creating this layout varies amongst the sites. For example, while some sites separate sections of the article content with paragraph tags, others segment the...
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