IntroductionHeart failure affects over 26 million people worldwide with prevalence expected to grow due to an ageing global population. Palliative care can address the holistic needs of patients with heart failure, and integrated palliative care in heart failure management has been indicated to improve outcomes for patients. Despite known benefits for integrated palliative care in heart failure management, implementation is poor across the majority of global health services. Recent systematic reviews have identified the benefits of integrating palliative care into heart failure management and highlighted barriers to implementation. However, there was heterogeneity in terms of countries, healthcare settings, delivery by differing staff across multidisciplinary teams, modes of delivery and different intervention components.Methods and analysisThe aim of this study is to identify how integrated palliative care and heart failure interventions produce desired outcomes, in which contexts, and for which patients. We will undertake a realist synthesis to identify this, using Pawson’s five iterative steps. We will recruit an international stakeholder group comprised of healthcare providers and patients with heart failure to advise and provide feedback throughout the review. Our initial realist programme theory sets out the necessary steps needed to accomplish the final intended outcome(s) from the implementation of integrated palliative care and heart failure. This initial programme theory will be shaped through an iterative process of testing and refinement.Ethics and disseminationEthical approval is not required for this study. With our stakeholder group, we will coproduce a user guide that outlines practical advice to optimise, tailor and implement interventions designed to integrate palliative care and heart failure, taking into consideration local context, alongside user-friendly summaries of the synthesis findings using short animations to convey complex findings. We will draw on the expertise within the stakeholder group to identify key stakeholders for disseminating to relevant audiences, ensuring outputs are tailored for their respective needs.PROSPERO registration numberCRD42021240185.

Aim: The at-risk mental state (ARMS) allows clinicians to identify individuals who have an increased risk of developing psychosis. At present, most screening for psychosis-risk is carried out within help-seeking populations; however, screening within educational settings may allow clinicians to identify individuals at-risk earlier and to increase the rate of detection. This review aimed to examine screening for the ARMS in educational settings, with the key questions: what screening tools have been used in educational settings, can screening in educational settings detect individuals with ARMS, what threshold scores in screening tools indicate a positive screen in educational settings, are there comorbid mental health conditions associated with the ARMS in educational settings? Methods: Searches were carried out in PsycINFO, MEDLINE, EMBASE, Scopus and Web of Science and reference lists of included articles searched. Results were summarized using narrative synthesis. Results: Nine papers were included for narrative synthesis. A variety of screening tools have been used when screening for the ARMS in educational settings. The majority of studies have been conducted in schools. The prevalence of the ARMS reported in ranges from 1% to 8%. Conclusions: The ARMS indicates the presence of distressing symptoms for which intervention may be beneficial. Screening programmes within educational settings may allow outreach for prodromal symptoms at an earlier stage than clinical settings currently provided for.

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Aims To examine the structure of the Prodromal Questionnaire (PQ‐16) in a non‐help‐seeking population through exploratory factor analysis and confirmatory factor analysis. Previous studies have not looked at the structure of this self‐report measure outside clinical settings. Methods Participants (n = 1045) were recruited through Amazon's Mechanical Turk (MTurk), and then completed the PQ‐16. The data set was split randomly in two, one being used for exploratory factor analysis (EFA) and the other for confirmatory factor analysis (CFA). A polychoric correlation matrix was created and EFA was used to explore the factor structure of the PQ‐16. Four models were tested through CFA to determine best fit: one, two, three and four‐factor models were all analysed. Results EFA indicated a two‐factor structure in the PQ‐16 in a non‐help‐seeking population (with a mean age = 29.7 years). Factor 1 represented perceptual abnormalities/hallucinations and factor 2 general symptoms associated with psychosis‐risk. CFA indicated that all the proposed models were suitable fits for the dataset. Fit indices for the three‐factor model (factor 1 representing perceptual abnormalities/hallucinations, factor 2 unusual thought content, and factor 3 negative symptom) indicated that it appeared to be a better fit for the data than the one, two, and four factor models. Conclusions This study suggests that a three‐factor model of the PQ‐16 is a better fit than other proposed models in a non‐help‐seeking population. Future research of the structure of the PQ‐16 in this population may benefit from recruiting subjects with a lower mean age than the current study.

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