The impact of introducing alcohol-free beer options in bars and public houses on alcohol sales and revenue: A randomised crossover field trial.

AIMS: The study aimed to estimate the impact of introducing a draught alcohol-free beer, thereby increasing the relative availability of these products, on alcohol sales and monetary takings in bars and pubs in England. DESIGN: Randomised crossover field trial. SETTING: England. PARTICIPANTS: Fourteen venues that did not previously sell draught alcohol-free beer. INTERVENTION AND COMPARATOR: Venues completed two intervention periods and two control periods in a randomised order over 8 weeks. Intervention periods involved replacing one draught alcoholic beer with an alcohol-free beer. Control periods operated business as usual. MEASUREMENTS: The primary outcome was mean weekly volume (in litres) of draught alcoholic beer sold. The secondary outcome was mean weekly revenue [in GBP (£)] from all drinks. Analyses adjusted for randomised order, special events, season and busyness. FINDINGS: The adjusted mean difference in weekly sales of draught alcoholic beer was -20 L [95% confidence interval (CI) = -41 to +0.4], equivalent to a 4% reduction (95% CI = 8% reduction to 0.1% increase) in the volume of alcoholic draught beer sold when draught alcohol-free beer was available. Excluding venues that failed at least one fidelity check resulted in an adjusted mean difference of -29 L per week (95% CI = -53 to -5), equivalent to a 5% reduction (95% CI = 8% reduction to 0.8% reduction). The adjusted mean difference in weekly revenue was +61 GBP per week (95% CI = -328 to +450), equivalent to a 1% increase (95% CI = 5% decrease to 7% increase) when draught alcohol-free beer was available. CONCLUSIONS: Introducing a draught alcohol-free beer in bars and pubs in England reduced the volume of draught alcoholic beer sold by 4% to 5%, with no evidence of the intervention impacting net revenue.

Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn.

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark prototypes an implementation of a reproducible framework, where the provided Jupyter Book enables readers to reproduce or modify the figures on the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep. Most of the benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing was generally effective, but is incompatible with statistical analyses requiring the continuous sampling of brain signal, for which a simpler strategy, using motion parameters, average activity in select brain compartments, and global signal regression, is preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods.

A Multisite Study of a Quality Improvement Teaching Strategy to Build Competency.

BACKGROUND: Quality improvement (QI) is an essential part of nursing education. Although there are robust examples of teaching strategies for QI, there is a distinct lack of research on effective strategies for teaching QI in nursing education. METHOD: This multisite study included students from six nursing programs. A treatment fidelity plan was developed and followed to ensure consistency for implementation of the study and data collection. A quasiexperimental, nonpaired, pre- and posttest study design was used to examine changes in student perspectives of QI after participating in a QI teaching strategy. Pre- and posttest questions were mapped to the American Association of Colleges of Nursing's Essentials subcompetency statements for QI. RESULTS: A total of 254 pre- and 116 posttest responses were included for analysis. Significance (p = .05) was found at the beginning level within three questions. CONCLUSION: Findings from this study indicate beginning-level students can benefit from a competency-based QI learning activity. [J Nurs Educ. 2024;63(1):38-42.].

Perinatal Bereavement in Racially, Culturally, and Gender Diverse Families.

ABSTRACT: Perinatal loss, the tragic event of losing a baby before, during, or shortly after birth, is a profoundly distressing experience for any family. We focus on the unique challenges faced by diverse families, encompassing those from underrepresented racial, ethnic, religious, and LGBTQ+ backgrounds. Diverse families often encounter inadequate support, misunderstandings, and even mistreatment during their perinatal loss journeys due to cultural insensitivity and biases. This review underscores the necessity of a trauma-informed, person-centered approach to perinatal bereavement care that respects the diversity of those affected. We emphasize the importance of understanding various cultural perspectives on grief and mortality to provide appropriate and empathetic care.Our core purpose is to elucidate the challenges confronting diverse families dealing with perinatal loss and to offer actionable strategies for health care providers. By addressing these unique challenges, nurses and other health care professionals can offer culturally sensitive, person-centered support during this distressing time. This review can serve as a resource for nurses and other health care providers, enabling them to provide personalized, culturally sensitive care to diverse families experiencing perinatal loss through a trauma-informed lens. Recognizing and addressing these distinctive needs fosters healing and ensures that nurses and other health care providers are better equipped to guide families through the challenging journey of perinatal bereavement.

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Impact of health warning labels and calorie labels on selection and purchasing of alcoholic and non-alcoholic drinks: A randomized controlled trial.

AIMS: To estimate the impact on selection and actual purchasing of (a) health warning labels (text-only and image-and-text) on alcoholic drinks and (b) calorie labels on alcoholic and non-alcoholic drinks. DESIGN: Parallel-groups randomised controlled trial. SETTING: Drinks were selected in a simulated online supermarket, before being purchased in an actual online supermarket. PARTICIPANTS: Adults in England and Wales who regularly consumed and purchased beer or wine online (n = 651). Six hundred and eight participants completed the study and were included in the primary analysis. INTERVENTIONS: Participants were randomized to one of six groups in a between-subjects three [health warning labels (HWLs) (i): image-and-text HWL; (ii) text-only HWL; (iii) no HWL] × 2 (calorie labels: present versus absent) factorial design (n per group 103-113). MEASUREMENTS: The primary outcome measure was the number of alcohol units selected (with intention to purchase); secondary outcomes included alcohol units purchased and calories selected and purchased. There was no time limit for selection. For purchasing, participants were directed to purchase their drinks immediately (although they were allowed up to 2 weeks to do so). FINDINGS: There was no evidence of main effects for either (a) HWLs or (b) calorie labels on the number of alcohol units selected (HWLs: F(2,599) = 0.406, P = 0.666; calorie labels: F(1,599) = 0.002, P = 0.961). There was also no evidence of an interaction between HWLs and calorie labels, and no evidence of an overall difference on any secondary outcomes. In pre-specified subgroup analyses comparing the 'calorie label only' group (n = 101) with the 'no label' group (n = 104) there was no evidence that calorie labels reduced the number of calories selected (unadjusted means: 1913 calories versus 2203, P = 0.643). Among the 75% of participants who went on to purchase drinks, those in the 'calorie label only' group (n = 74) purchased fewer calories than those in the 'no label' group (n = 79) (unadjusted means: 1532 versus 2090, P = 0.028). CONCLUSIONS: There was no evidence that health warning labels reduced the number of alcohol units selected or purchased in an online retail context. There was some evidence suggesting that calorie labels on alcoholic and non-alcoholic drinks may reduce calories purchased from both types of drinks.

Artificial intelligence for dementia research methods optimization.

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

Continuous Evaluation of Denoising Strategies in Resting-State fMRI Connectivity Using fMRIPrep and Nilearn.

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark is implemented in a fully reproducible framework, where the provided research objects enable readers to reproduce or modify core computations, as well as the figures of the article using the Jupyter Book project and the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep software package. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods. Our reproducible benchmark infrastructure will facilitate such continuous evaluation in the future, and may also be applied broadly to different tools or even research fields.

Artificial Intelligence for Dementia Research Methods Optimization.

INTRODUCTION: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.

Impact on alcohol selection and online purchasing of changing the proportion of available non-alcoholic versus alcoholic drinks: A randomised controlled trial.

BACKGROUND: Increasing the availability of non-alcoholic options is a promising population-level intervention to reduce alcohol consumption, currently unassessed in naturalistic settings. This study in an online retail context aimed to estimate the impact of increasing the proportion of non-alcoholic (relative to alcoholic) drinks, on selection and purchasing of alcohol. METHODS AND RESULTS: Adults (n = 737) residing in England and Wales who regularly purchased alcohol online were recruited between March and July 2021. Participants were randomly assigned to one of 3 groups: "25% non-alcoholic/75% alcoholic"; "50% non-alcoholic/50% alcoholic"; and "75% non-alcoholic/25% alcoholic," then selected drinks in a simulated online supermarket, before purchasing them in an actual online supermarket. The primary outcome was the number of alcohol units selected (with intention to purchase); secondary outcomes included actual purchasing. A total of 607 participants (60% female, mean age = 38 years [range: 18 to 76]) completed the study and were included in the primary analysis. In the first part of a hurdle model, a greater proportion of participants in the "75% non-alcoholic" group did not select any alcohol (13.1%) compared to the "25% non-alcoholic" group (3.4%; 95% confidence interval [CI] -2.09, -0.63; p < 0.001). There was no evidence of a difference between the "75% non-alcoholic" and the "50% non-alcoholic" (7.2%) groups (95% CI 0.10, 1.34; p = 0.022) or between the "50% non-alcoholic" and the "25% non-alcoholic" groups (95% CI -1.44, 0.17; p = 0.121). In the second part of a hurdle model in participants (559/607) selecting any drinks containing alcohol, the "75% non-alcoholic" group selected fewer alcohol units compared to the "50% non-alcoholic" (95% CI -0.44, -0.14; p < 0.001) and "25% non-alcoholic" (95% CI -0.54, -0.24; p < 0.001) groups, with no evidence of a difference between the "50% non-alcoholic" and "25% non-alcoholic" groups (95% CI -0.24, 0.05; p = 0.178). Overall, across all participants, 17.46 units (95% CI 15.24, 19.68) were selected in the "75% non-alcoholic" group; 25.51 units (95% CI 22.60, 28.43) in the "50% non-alcoholic" group; and 29.40 units (95% CI 26.39, 32.42) in the "25% non-alcoholic" group. This corresponds to 8.1 fewer units (a 32% reduction) in the "75% non-alcoholic" compared to the "50% non-alcoholic" group, and 11.9 fewer alcohol units (41% reduction) compared to the "25% non-alcoholic" group; 3.9 fewer units (13% reduction) were selected in the "50% non-alcoholic" group than in the "25% non-alcoholic" group. For all other outcomes, alcohol selection and purchasing were consistently lowest in the "75% non-alcoholic" group. Study limitations include the setting not being entirely naturalistic due to using a simulated online supermarket as well as an actual online supermarket, and that there was substantial dropout between selection and purchasing. CONCLUSIONS: This study provides evidence that substantially increasing the proportion of non-alcoholic drinks-from 25% to 50% or 75%-meaningfully reduces alcohol selection and purchasing. Further studies are warranted to assess whether these effects are realised in a range of real-world settings. TRIAL REGISTRATION: ISRCTN: 11004483; OSF: https://osf.io/qfupw.

Key Factors Related to Household Emergency Preparedness Among Parents of Newborn Infants.

BACKGROUND: A natural disaster can have devastating consequences for newborn infants. Despite this fact, there are few studies that have explored household emergency preparedness (HEP) among parents of newborn infants or factors affecting HEP in this population. PURPOSE: The purpose of this study was to explore the relationship between various demographic and socioeconomic variables and levels of HEP among parents of newborn infants. METHODS: Parents of newborn infants born at a single medical center in Brooklyn, New York, completed a pre- and posttest to determine their level of HEP before and after implementing the Nurses Taking on Readiness Measures (N-TORM) intervention. For this study, a secondary statistical analysis was performed on the HEP scores gathered prior to the intervention and the demographic data collected from participants. RESULTS: There was a statistically significant relationship between HEP scores and homeownership t(62) = 2.75, P = .008, level of education t(66) = 2.31, P = .024, and income t(46) = 2.39, P = .021. IMPLICATIONS FOR PRACTICE AND RESEARCH: Findings from this study indicate that there are various demographic and socioeconomic factors that influence HEP. Another critical finding was that most participants were significantly underprepared for an emergency with an average HEP score of 4.75 (SD = 2.61) out of a possible 11 points. Findings from this study support the need for neonatal and pediatric providers to develop targeted interventions to enhance the preparedness of parents of newborns in general, and in particular, those with lower household incomes, lower levels of education, and those who rent their homes.

An Exploration of Resiliency Among Nurse Educators During the COVID-19 Pandemic.

AIM: The purpose of this study was to explore resilience among nursing faculty during the COVID-19 pandemic and to examine associations or differences that may exist among variables and between groups. BACKGROUND: Nurse educators' resilience has been identified as an important characteristic because of the challenges involved in nursing education. It is important to explore resilience among nurse educators to determine which factors promote or inhibit this important characteristic. METHOD: This correlational research study used an online survey to explore factors related to resilience among nurse faculty during the COVID-19 pandemic. Correlational analyses were used to determine the relationship between various resilience constructs and select demographic variables. RESULTS: Findings indicated that higher levels of resilience were associated with age (ρ = .21, p < .001), experience teaching online ( r = .22, p < .001), and professional development related to online teaching ( M = 80.7, SD = 11.0), t (276) = 2.41, p = .017. CONCLUSION: Resilience and related characteristics have the potential to assist nurse educators in adapting successfully to stressful circumstances. It is crucial that schools of nursing develop programs to enhance or develop resilience among nurse educators. Support and training in the area of online education are also of paramount importance.

An exploration of technology acceptance among nursing faculty teaching online for the first time at the onset of the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has brought to the forefront the importance for schools of nursing to use creative and innovative tools that are of high quality and accessible to learners. Faculty who may have been resistant to teaching online prior to the pandemic, no longer had the option to teach face-to-face and were mandated to teach online despite any apprehensions they may have had. PURPOSE: The purpose of this study was to learn more about faculty attitudes and acceptance of teaching online by applying the Technology Acceptance Model to nursing faculty teaching online for the first time during the COVID-19 pandemic. METHODS: This descriptive-correlational study used an online survey tool to explore factors related to technology acceptance among nursing faculty teaching online for the first time during the COVID-19 pandemic. A sample of 87 full-time and part-time nursing faculty completed an adapted version of the Faculty Acceptance Survey. RESULTS: Findings from this study revealed an overall enjoyment of teaching online, confidence in online teaching skills and comfort with technology. However, findings also indicated struggles with workload balance, inferior interactions with students and the need for additional support. CONCLUSION: Findings from this study demonstrate that nursing faculty are generally accepting of technology and positive outcomes are possible if identified concerns are addressed and positive feelings are fostered and supported.

Visualising health risks with medical imaging for changing recipients' health behaviours and risk factors: Systematic review with meta-analysis.

BACKGROUND: There is ongoing clinical and research interest in determining whether providing personalised risk information could motivate risk-reducing health behaviours. We aimed to assess the impact on behaviours and risk factors of feeding back to individuals' images of their bodies generated via medical imaging technologies in assessing their current disease status or risk. METHODS AND FINDINGS: A systematic review with meta-analysis was conducted using Cochrane methods. MEDLINE, Embase, PsycINFO, CINAHL, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched up to July 28, 2021, with backward and forward citation searches up to July 29, 2021. Eligible studies were randomised controlled trials including adults who underwent medical imaging procedures assessing current health status or risk of disease, for which personal risk may be reduced by modifying behaviour. Trials included an intervention group that received the imaging procedure plus feedback of visualised results and assessed subsequent risk-reducing health behaviour. We examined 12,620 abstracts and included 21 studies, involving 9,248 randomised participants. Studies reported on 10 risk-reducing behaviours, with most data for smoking (8 studies; n = 4,308), medication use (6 studies; n = 4,539), and physical activity (4 studies; n = 1,877). Meta-analysis revealed beneficial effects of feedback of visualised medical imaging results on reduced smoking (risk ratio 1.11, 95% confidence interval [CI] 1.01 to 1.23, p = 0.04), healthier diet (standardised mean difference [SMD] 0.30, 95% CI 0.11 to 0.50, p = 0.003), increased physical activity (SMD 0.11, 95% CI 0.003 to 0.21, p = 0.04), and increased oral hygiene behaviours (SMD 0.35, 95% CI 0.13 to 0.57, p = 0.002). In addition, single studies reported increased skin self-examination and increased foot care. For other behavioural outcomes (medication use, sun protection, tanning booth use, and blood glucose testing) estimates favoured the intervention but were not statistically significant. Regarding secondary risk factor outcomes, there was clear evidence for reduced systolic blood pressure, waist circumference, and improved oral health, and some indication of reduced Framingham risk score. There was no evidence of any adverse effects, including anxiety, depression, or stress, although these were rarely assessed. A key limitation is that there were some concerns about risk of bias for all studies, with evidence for most outcomes being of low certainty. In particular, valid and precise measures of behaviour were rarely used, and there were few instances of preregistered protocols and analysis plans, increasing the likelihood of selective outcome reporting. CONCLUSIONS: In this study, we observed that feedback of medical images to individuals has the potential to motivate risk-reducing behaviours and reduce risk factors. Should this promise be corroborated through further adequately powered trials that better mitigate against risk of bias, such interventions could usefully capitalise upon the widespread and growing use of medical imaging technologies in healthcare.