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Evidence suggests that complementary hot drinks and biscuits benefit an overworked and highly stressed healthcare workforce. But when signage in healthcare libraries asks patrons not to consume “excessive” quantities of free hot drinks and biscuits, how much is too much? Tabner and colleagues explore this resource allocation conundrum

Aims: The DIALOG scale has been implemented as a routine patient outcome and experience measure (PROM/PREM) in East London Foundation Trust (EL FT). We used large routinely collected DIALOG data to assess impact of treatment across different domains of life and whether the impact of treatment changed with Community Mental Health Transformation CMH (NHS Long Term Plan). We also carried out secondary disaggregation analysis of pooled data based on protected characteristics interrogating through an equity lens. Methods: EL FT had commissioned University of Plymouth for the review of CMH transformation. Anonymised pooled data set was obtained from the electronic patient records that were collected as a part of routine clinical practice. DIALOG (PROM and PREM) scores captured routinely from CMH services in ELFT over two time periods (2018–19 and 2021–22) were collected for this purpose. The anonymised and pooled data was linked with stages of treatment e.g. assessment, review and at discharge and protected characteristics (age, gender, ethnicity and a proxy of social deprivation). 14,813 DIALOG scores from 6,538 unique patients were identified. We analysed each domain of DIALOG separately and the numbers of return of scores on each domain varied depending on response rate. We compared domain based descriptive statistical analyses of mean pooled DIALOG scores looking at means across a range of variables for each domain and then conducted a series of multiple regressions for each of the DIALOG domains, to control for multiple variables together Results: Our results showed that service user satisfaction in each domain improved with treatment stage (from assessment to review to discharge) reaching statistical significance at each stage. There were minor differences between the two time periods (2018–19 and 2021–22) in a few domains. There was variation in outcomes across ethnicity, age and gender in a few domains. Conclusion: Large data sets of routinely collected DIALOG data offer valuable insight into the needs of the local population and impact of treatment. Assessment of the impact of the CMH service transformation was confounded by the pandemic. Disaggregated data on protected characteristics reveal interesting and useful information about experiences and outcomes of different population groups over time. Our study also validates DIALOG as a quality of life measure and patient experience measure scale that is sensitive to measure change. It affirms the value and depth that intelligence routine outcome data gathering can offer both to measure change as well as offering an assessment of population need.

In recent years, coinciding with the increasing incidence of type 2 diabetes mellitus (T2DM) in children and adolescents, the global prevalence of diabetic kidney disease (DKD) and end-stage renal disease (ESRD) are rising year on year. In contrast, the mortality and morbidity due to cardiovascular disease (CVD) and stroke in people with diabetes have been declining. The precise cause of the disparate vascular outcomes in diabetes remains unexplored. To elucidate the relationship, we conducted a retrospective cohort study on the UK Biobank data. In our study, the exposure variables were the age of diabetes and hypertension diagnosis, while the outcome variables were ESRD, myocardial infarction, angina, and stroke. Univariable and multivariable logistic regression models were fitted to assess odds ratios (ORs) and 95% confidence intervals (CIs). Model performance was evaluated using the receiver operating characteristic (ROC) curve. Sensitivity analyses were conducted on participants who developed diabetes before and after the age of 20 years and with and without female participants. Univariable logistic regression showed that compared to those diagnosed after the age of 60, the odds of ESRD for those diagnosed at ages < 20, 20–40, and 41–60 years were 5.26 (3.00 – 9.40), 7.78 (4.81 – 13.16) and 2.33 (1.50 – 3.84), respectively. Myocardial infarction and stroke did not have a statistically significant relationship with younger age of diabetes diagnosis. In those with a dual diagnosis of diabetes and hypertension, irrespective of the age of diabetes diagnosis, the age of hypertension diagnosis at age < 20, 20–40, and 41–60 years, compared to those who developed it after the age of 60 years, had a greater risk of ESRD, 2.20 (1.58 – 3.11), 5.03 (3.79 – 6.81), and 1.53 (1.16 – 2.06), respectively. After adjusting for sex and albuminuria, multivariable logistic regression model 1 showed that compared to those who developed diabetes above the age of 60, those who developed it < 20, 20–40 and 41–60 had a higher risk of ESRD, 4.71 (2.47 – 9.28), 4.67 (2.63 – 8.78), and 1.94 (1.16 – 3.49), respectively. Likewise, in model 2, when the duration of diabetes was used as the explanatory variable, each year of increased duration of diabetes increased the odds of ESRD by 2%, with an odds ratio of 1.02 (1.01–1.03). Younger onset of hypertension but not diabetes increased the odds of myocardial infarction (MI). There was no statistically significant relationship between the age of diabetes, and hypertension diagnoses with angina and stroke. Model performance was excellent, with over 80% of the data points falling below the area under the curve. Sensitivity analyses showed young-onset diabetes as a significant determinant of ESRD. Young-onset and longer-duration of diabetes increase the risk of ESRD. For those with diabetes and hypertension, a younger onset of hypertension but not diabetes may also increase the risk of MI.

Background In the UK, tens of millions of working days are lost due to work‐related ill health every year, costing billions of pounds. The role of Occupational Health (OH) services is vital in helping workers to maintain employment when they encounter injury or illness. OH providers traditionally rely on a clinical workforce to deliver these services, particularly doctors and nurses with OH qualifications. However, the increasing demand for OH services is unlikely to be met in the future using this traditional model, due to the declining number of OH‐trained doctors and nurses in the UK. Multi‐disciplinary models of OH delivery, including a more varied range of healthcare and non‐healthcare professionals, could provide a way to meet this new demand for OH services. There is a need to identify collaborative models of OH service delivery and review their effectiveness on return‐to work outcomes. There is an existing pool of systematic review evidence evaluating workplace based, multi‐disciplinary OH interventions, but it is difficult to identify which aspects of the content and/or delivery of these interventions may be associated with improved work‐related outcomes. Objectives The aim of this evidence and gap map (EGM) was to provide an overview of the systematic review evidence that evaluates the effectiveness and cost‐effectiveness of multi‐disciplinary OH interventions intending to improve work‐related outcomes. Search Methods In June 2021 we searched a selection of bibliographic databases and other academic literature resources covering a range of relevant disciplines, including health care and business studies, to identify systematic review evidence from a variety of sectors of employment. We also searched Google Search and a selection of topically relevant websites and consulted with stakeholders to identify reports already known to them. Searches were updated in February 2023. Selection Criteria Systematic reviews needed to be about adults (16 years or over) in employment, who have had absence from work for any medical reason. Interventions needed to be multi‐disciplinary (including professionals from different backgrounds in clinical and non‐clinical professions) and designed to support employees and employers to manage health conditions in the workplace and/or to help employees with health conditions retain and/or return to work following medical absence. Effectiveness needed to be measured in terms of return to work, work retention or measures of absence, or economic evaluation outcomes. These criteria were applied to the title and and full text of each systematic review independently by two reviewers, with disagreements resolved through discussion. We awarded each systematic review a rating of ‘High’, ‘Medium’ or ‘Low’ relevance to indicate the extent to which the populations, interventions and their contexts synthesised within the review were consistent with our research question. We also recorded the number of primary studies included within each of the ‘High’ and ‘Medium’ reviews that were relevant to research question using the same screening process applied at review level. Data Collection and Analysis Summary data for each eligible review was extracted. The quality of the systematic reviews, rated as ‘High’ or ‘Medium’ relevance following full text screening, was appraised using the AMSTAR‐2 quality appraisal tool. All data were extracted by one reviewer and checked by a second, with disagreements being settled through discussion. Summary data for all eligible systematic reviews were tabulated and described narratively. The data extracted from reviews of ‘High’ and ‘Medium’ relevance was imported into EPPI‐Mapper software to create an EGM. Stakeholder Involvement We worked alongside commissioners and policy makers from the Department of Health and Social Care (DHSC) and Department of Work and Pensions (DWP), OH personnel, and people with lived experience of accessing OH services themselves and/or supporting employees to access OH services. Individuals contributed to decision making at all stages of the project. This ensured our EGM reflects the needs of individuals who will use it. Main Results We identified 98 systematic reviews that contained relevant interventions, which involved a variety of professionals and workplaces, and which measured effectiveness in terms of return to work (RTW). Of these, we focused on the 30 reviews where the population and intervention characteristics within the systematic reviews were considered to be of high or medium relevance to our research questions. The 30 reviews were of varying quality, split evenly between High/Moderate quality and Low/Critically‐Low quality ratings. We did not identify any relevant systematic review evidence on any other work‐related outcome of interest. Interventions were heterogenous, both within and across included systematic reviews. The EGM is structured according to the health condition experienced by participants, and the effectiveness of the interventions being evaluated, as reported within the included systematic reviews. It is possible to view (i) the quality and quantity of systematic review evidence for a given health condition, (ii) how review authors assessed the effectiveness or cost‐effectiveness of the interventions evaluated. The EGM also details the primary studies relevant to our research aim included within each review. Authors’ Conclusions This EGM map highlights the array of systematic review evidence that exists in relation to the effectiveness or cost‐effectiveness of multi‐disciplinary, workplace‐based OH interventions in supporting RTW. This evidence will allow policy makers and commissioners of services to determine which OH interventions may be most useful for supporting different population groups in different contexts. OH professionals may find the content of the EGM useful in identifying systematic review evidence to support their practice. The EGM also identifies where systematic review evidence in this area is lacking, or where existing evidence is of poor quality. These may represent areas where it may be particularly useful to conduct further systematic reviews.

BackgroundNHS England’s ‘Enhanced Health in Care Homes’ specification aims to make the healthcare of care home residents more proactive. Primary care networks (PCNs) are contracted to provide this, but approaches vary widely: challenges include frailty identification, multidisciplinary team (MDT) capability/capacity and how the process is structured and delivered.AimTo determine whether a proactive healthcare model could improve healthcare outcomes for care home residents.Design and settingQuality improvement project involving 429 residents in 40 care homes in a non-randomised crossover cohort design. The headline outcome was 2-year survival.MethodAll care home residents had healthcare coordinated by the PCN’s Older Peoples’ Hub. A daily MDT managed the urgent healthcare needs of residents. Proactive healthcare, comprising information technology-assisted comprehensive geriatric assessment (i-CGA) and advanced care planning (ACP), were completed by residents, with prioritisation based on clinical needs.Time-dependent Cox regression analysis was used with patients divided into two groups:Control group: received routine and urgent (reactive) care only.Intervention group: additional proactive i-CGA and ACP.ResultsBy 2 years, control group survival was 8.6% (n=108), compared with 48.1% in the intervention group (n=321), p<0.001. This represented a 39.6% absolute risk reduction in mortality, 70.2% relative risk reduction and the number needed to treat of 2.5, with little changes when adjusting for confounding variables.ConclusionA PCN with an MDT-hub offering additional proactive care (with an i-CGA and ACP) in addition to routine and urgent/reactive care may improve the 2-year survival in older people compared with urgent/reactive care alone.

Excessive engagement with (increasingly prevalent) loot boxes within games has consistently been linked with disordered gambling and/or gaming. The importance of recognising and managing potential risks associated with loot box involvement means understanding contributing factors is a pressing research priority. Given that motivations for gaming and gambling have been informative in understanding risky engagement with those behaviours, this qualitative study investigated motivations for buying loot boxes, through in-depth interviews with 28 gamers from across the UK. A reflexive thematic analysis categorised reasons for buying into seven “themes”; opening experience; value of box contents; game-related elements; social influences; emotive/impulsive influences; fear of missing out; triggers/facilitators. These themes are described in detail and discussed in relation to the existing literature and motivation theories. This study contributes to understanding ways in which digital items within loot boxes can be highly valued by purchasers, informing the debate around parallels with gambling. Findings that certain motivations were disproportionately endorsed by participants with symptoms of problematic gambling has potential implications for policy and warrants further study.

Background Play is pivotal in human development, and as such the human experience. In young people, play is increasingly taking the form of video gaming—capturing a large share of the entertainment sector and potentially exposing young people to new instances of harmful behaviour. Loot boxes (purchasable items where the contents are chance based, which have structural and psychological similarities to gambling) generate significant income for the sector. They have, however, been understudied in young people. The present research validates a lightly modified version of the Reasons and Facilitators for Loot box Engagement (RAFFLE) inventory in a youth population – the youth RAFFLE (yRAFFLE). Results Five hundred six young people were recruited from urban and rural schools, including both sexes and ages 10 to 14. The psychometric properties, internal and convergent validity of yRAFFLE were assessed. Through confirmatory factor analysis, a seven-factor ( Enhancement, Progression, Social Pressure, Distraction/Compulsion, Altruism, Fear of Missing Out, and ReSale) solution was established, confirming the same factor structure previously observed in an adult cohort. Inspection of standard fit measures showed the seven-factor solution to fit above benchmarks and the original RAFFLE. Cronbach’s alpha for the whole scale was 0.95 and subscales ranged from 0.73 to 0.89. yRAFFLE was highly correlated with the Video Game Addiction questionnaire. Finally, yRAFFLE showed measurement invariance to sex, location, and age. Conclusions The finalised yRAFFLE is a robust measure of 23 items for the motivations for loot box engagement in young people, that acts a parallel to its counterpart in adults. The yRAFFLE functions as a methodological tool to capture, measure, and understand young people’s engagement with loot boxes.

Qualitative studies have identified a diverse array of motivations for purchasing items within video games through chance-based mechanisms (i.e., “loot boxes”). Given that some individuals—particularly those at risk of disordered gaming and/or gambling—are prone to over-involvement with loot box purchasing, it is important to have a reliable, valid means of measuring the role of different motivations in driving purchasing behaviour. Building on prior qualitative research, this paper reports the development and validation of the “RAFFLE” scale, to measure the Reasons and Facilitators for Loot box Engagement. A 23-item, seven-factor scale was developed through cognitive interviews (n = 25) followed by two surveys of UK-based gamers who purchase loot boxes; analysed via exploratory (n = 503) and confirmatory (n = 1495) factor analysis, respectively. Subscales encompassed “enhancement’; “progression’; “social pressure’; “distraction/compulsion’; “altruism’; “fear of missing out’; and “resale”. The scale showed good criterion and construct validity (correlating well with measures of loot box engagement; the risky loot box index (r = 0.63) and monthly self-reported spend (r = 0.38)), and good internal validity (Cronbach’s alpha = 0.84). Parallels with, and divergence from, motivations for related activities of gaming and gambling, and alignment with broader theoretical models of motivation, are discussed.

In a pre-registered survey linked to this paper (Exploring the relationships between psychological variables and loot box engagement, part 1: pre-registered hypotheses), we confirmed bivariate associations between engagement with loot boxes (purchasable randomized rewards in video games) and measures of problem gambling, problem video gaming, impulsivity, gambling cognitions, experiences of game-related ‘flow’, psychological distress and reduced wellbeing. However, these variables have complex relationships, so to gain further insights, we analysed the dataset (1495 gamers who purchase loot boxes and 1223 purchasers of non-randomized content) in a series of Bayesian mixed-effects multiple regressions with a zero-inflation component. The results challenge some well-established results in the literature, including associations between loot box engagement and problematic gambling measures, instead suggesting that this relationship might be underpinned by shared variance with problem video gaming and gambling-related cognitions. An entirely novel discovery revealed a complex interaction between experiences of flow and loot box engagement. Distress and wellbeing are both (somewhat contradictorily) predictive of participants engaging with loot boxes, but neither correlate with increasing loot box risky engagement/spend (among those who engage). Our findings unravel some of the nuances underpinning loot box engagement, yet remain consistent with narratives that policy action on loot boxes will have benefits for harm minimization.