Class Model Research Articles

Abstract 4146301: Relationship Between Calf Muscle Pump Function and Exercise Stress Testing

Background: Exercise stress testing uses metabolic equivalents of tasks (METs) to measure the energy cost of activities, aiding in the assessment of exercise capacity and cardiovascular health. Despite its significance, the correlation between calf muscle pump function (CPF) and exercise stress testing remains unexplored. We aimed to evaluate the relationship between CPF and peak METs as determined by cardiopulmonary treadmill exercise stress testing. Methods: The study included adults who underwent exercise cardiopulmonary stress testing and venous plethysmography at Mayo Clinic between April 2017 and March 2020. The protocols other than Bruce, Mayo, Modified Naughton, and Naughton protocols were excluded. The CPF ejection fraction (EF) was calculated per leg based on refill volumes post-exercise as a percentage of passive drain refill. The classification of CEAP (Clinical-Etiology-Anatomy-Pathophysiology) was utilized to better understand chronic venous insufficiency (CVI). Results: A total of 155 patients who underwent both exercise stress testing and venous plethysmography were included, with a mean age of 61.31 ± 14.03 years, and 84 (54.2%) were male. The peak measured METs for normal, unilaterally reduced, and bilaterally reduced CPF were 8.5 (2.5), 7.3 (2.1), and 7.1 (2.4), respectively (p=0.004, Figure 1). Multiple linear regression models were developed with METs as the outcome to determine if CPF was an independent predictor of METs on cardiopulmonary exercise stress testing. IIn model 1, the following independent variables were included: resting heart rate, peak heart rate, peak systolic blood pressure, recovery heart rate at minute 1, and worst EF (Table 1). In model 1, with only exercise parameters, lower EF was associated with lower METs (p=0.03). In a second analysis, variables identified as statistically significant with METs in the initial model were included, along with CEAP class (model 2) and CCI (model 3) (Table 2). In model 2, CEAP class 3 or higher was associated with decreased METs on the exercise stress test. This correlation implies that individuals with moderate to severe CVI may influence exercise capacity, demonstrating the interconnectedness of the cardiovascular system. Moreover, in model 3, the CCI, a predictor for mortality, was not significantly associated with METs. Conclusion: Our findings revealed that more severe CVI (CEAP class and reduced CPF) was associated with reduced exercise capacity after accounting for other factors.

Model-Based Clustering of Categorical Data Based on the Hamming Distance

A model-based approach is developed for clustering categorical data with no natural ordering. The proposed method exploits the Hamming distance to define a family of probability mass functions to model the data. The elements of this family are then considered as kernels of a finite mixture model with an unknown number of components. Conjugate Bayesian inference has been derived for the parameters of the Hamming distribution model. The mixture is framed in a Bayesian nonparametric setting, and a transdimensional blocked Gibbs sampler is developed to provide full Bayesian inference on the number of clusters, their structure, and the group-specific parameters, facilitating the computation with respect to customary reversible jump algorithms. The proposed model encompasses a parsimonious latent class model as a special case when the number of components is fixed. Model performances are assessed via a simulation study and reference datasets, showing improvements in clustering recovery over existing approaches. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

The Impact Of-Problematic Learning Styles-On Learning Outcomes Of Cultural Diversity In My Country 1 Sub-Diameter 1 Unity

The Impact of Problematic Learning Styles on Learning Outcomes in My Country Cultural Diversity Teachers and Teaching Education Dissertation Pacuan University. 2566 This study is a quasi-experimental study using traditional and problem-based learning models to investigate how cultural diversity sub-factors affect learning outcomes in My Country State School in Bokor IV C 78 Problem-based learning models have more requirements than traditional control class learning problems based on the experimental class model compared to the model. Because the results of both samples are lower than the L table, they have a high N value, so to find out whether the sample <0.148 is distributed in normal class groups, the researcher conducted an additional normality test using the Liliefors test hypothesis test of 7.42843 is greater than 1.9944 in the table, therefore the alternative hypothesis is accepted and the null hypothesis H0 is rejected Based on the above findings, the problem-based learning model is more effective than the traditional approach. diversity in my country.

Consumer preferences toward new circular bioeconomy agri‐food products

AbstractThe integration of circular bioeconomy (CB) principles into food production is key for a sustainable food sector. This research analyzes consumers' preferences for food products that include CB‐related attributes; namely, zero (plastic/bio) waste generation, carbon neutrality, sustainable water use and soil management, biodiversity conservation, and contribution to the rural economy. A novel approach is applied, combining best‐worst scaling choice experiments with contingent valuation and using a scale‐adjusted latent class model specification. The results reveal consumers have intense preferences for CB attributes in food, especially those related to plastic waste management, sustainable water use, rural development, and biodiversity. Four different consumer classes are identified according to their preferences toward CB attributes (Eco‐multifunctionals, Utilitarians, Sustainable agrarians, and Circulars—the latter two newly identified here). Willingness to pay varies by attribute and class. Relevant implications are drawn for businesses, especially about potential differentiation marketing strategies.

Quantifying the economic value of a national hydrometric network for households

This study reports the results of a Choice Experiment to quantify households’ willingness-to-pay for river gauging programmes in Scotland. The hydrometric network is operated and maintained by the Scottish Environment Protection Agency (SEPA), Scotland’s principal environment regulator, a non-department public body of the Scottish Government. Results from mixed logit and latent class modelling show that most households (‘Hydrometric Maximisers’ − around 70 %) have significant, positive willingness-to-pay values for river gauging programmes, but a minority (‘Hydrometric Satisficers’ − around 30 %) do not view this as a major public policy priority. On average, hydrometric data collection delivers non-market benefits worth £84,625,562 to the Scottish economy, with a minimum economic Benefit-to-Cost ratio of 25:1. This is in addition to the infrastructure value and any private returns made by commercial users of the data. The findings demonstrate that traditional approaches to assessing the benefits of hydrometric networks often underestimate their value. The research also highlights the importance of public information campaigns and household engagement initiatives to increase awareness of hydro-meteorological services, and to develop the business case more fully for public investment in environmental observation networks.

음악과 개념기반 탐구학습 수업모형 개발 및 수업설계 방안 탐색

This study begins with the issue that existing models of concept-based inquiry processes generally fail to capture the distinctive characteristics of music education, which involves creation, practice, immersion, and expression. The aim of this research is to develop a music class model applicable to concept-based inquiry learning. To achieve this, we examined the features of concept-based curricula and the unique characteristics of music education, analyzed existing related instructional models to derive their features, and then developed a seven-stage instructional model. This model, which incorporates feedback from a music education research group and expert reviews, was applied to music classes centered around the concept of "change". The seven stages include: 'Observing', 'Exploring Contextual Information', 'Refining', 'Immersing and Expressing', 'Meaning Constructing', 'Transferring', and 'Reflecting'. The focus on 'Immersing and Expressing', a core characteristic of music education, is central to this model, with the structure allowing for flexible transitions to 'Immersing and Expressing' from other stages as needed. This model is suitable for forming music concepts and transferring them to other musical areas, thus promoting enriched musical experiences and practical applications in life. It is expected that this model will contribute to discussions on various applications of concept-based inquiry learning in music and practical approaches to instructional methods in the future.

Public preferences for edible invasive alien marine species - The Atlantic blue crab in southern Italy

Since 2014, the provision of the aquatic ecosystem services has been gradually affected due to the biological invasion of Callinectes sapidus (Rathbun, 1896, Crustacea, Decapoda, Portunidae), commonly known as Atlantic blue crab, across several lagoon-like locations in Italy. In addition, this serious aquatic invasive species, native of North American coasts, has already inflicted economic damage of about EUR 100 million to the Italian fishing and farming communities over the past year. To counter their severe and rapid spread, the Italian Government has encouraged the fishing communities to catch as many as possible and commercially exploit them for human consumption in an attempt to manage their expansion. Since there is an ongoing promotion for the consumption of blue crab by forging novel food businesses in Italy, this paper aims to predict the public preferences and their willingness to pay (WTP) towards this biological invader. For this purpose, a discrete choice experiment approach is used, by means of a multinomial logit model (MNL) and latent class model (LCM). The social field survey involves a representative sample of 440 valid respondents in Apulia Region, southern Italy. The descriptive statistics results reveal that 67.50% of the local citizens interviewed know about the blue crab invasion, while 29.09% of them have already consumed this seafood species. In addition, the MNL results show that the most appreciated attributes of the blue crabs by respondents are freshness and large size. Further, the LCM findings reveal two representative classes of Apulian consumers; the first group of citizens (70.9% of total respondents) expresses positive appreciation for consuming blue crabs, while the second group (29.1% of total respondents) is not willing to pay a premium price regarding this potential commercial activity. Furthermore, the econometric results show that the average value that Apulian’s (i.e. those belonging to Class/Group I of respondents) WTP for blue crabs’ consumption is about EUR 18 per kilogram. In this regard, this research has public and private implications and may reasonably promote the commercial exploitation of blue crabs, enhancing the reduction of its population density through human consumption as a promised management control strategy and forging a novel profitable business mainly for local small-scale artisanal fisheries.

CascAGS: Comparative Analysis of SNP Calling Methods for Human Genome Data in the Absence of Gold Standard.

The development of third-generation sequencing has accelerated the boom of single nucleotide polymorphism (SNP) calling methods, but evaluating accuracy remains challenging owing to the absence of the SNP gold standard. The definitions for without-gold-standard and performance metrics and their estimation are urgently needed. Additionally, the possible correlations between different SNP loci should also be further explored. To address these challenges, we first introduced the concept of a gold standard and imperfect gold standard under the consistency framework and gave the corresponding definitions of sensitivity and specificity. A latent class model (LCM) was established to estimate the sensitivity and specificity of callers. Furthermore, we incorporated different dependency structures into LCM to investigate their impact on sensitivity and specificity. The performance of LCM was illustrated by comparing the accuracy of BCFtools, DeepVariant, FreeBayes, and GATK on various datasets. Through estimations across multiple datasets, the results indicate that LCM is well-suitable for evaluating callers without the SNP gold standard, and accurate inclusion of the dependency between variations is crucial for better performance ranking. DeepVariant has a higher sum of sensitivity and specificity than other callers, followed by GATK and BCFtools. FreeBayes has low sensitivity but high specificity. Notably, appropriate sequencing coverage is another important factor for precise callers' evaluation. Most importantly, a web interface for assessing and comparing different callers was developed to simplify the evaluation process.

Body mass index trajectories and time in target range after delivery and long-term type 2 diabetes risk in women with a history of gestational diabetes mellitus.

This study aims to determine whether postpartum body mass index (BMI) trajectories and its time in target range (TTR) are associated with long-term type 2 diabetes risk in women with a history of gestational diabetes mellitus (GDM). The present study included 1057 women with a history of GDM who participated in the Tianjin Gestational Diabetes Mellitus Prevention Program (TGDMPP). Oral glucose tolerance tests or physician-diagnosed information were used to diagnose type 2 diabetes after a median follow-up period of 8.47 years. Latent class modelling was applied to identify trajectories of BMI after delivery. TTR was defined as the proportion of time that BMI was within the standard range (18.5 ≤ BMI < 24.0 kg/m2). The associations of BMI trajectories and TTR with type 2 diabetes risk were analysed using multivariable Cox modelling. Five distinct trajectories of postpartum BMI were identified. Compared with low-stable class, the multivariable-adjusted hazard ratios of type 2 diabetes were 2.02 (95% confidence interval 0.99-4.10) for median-stable class, 3.01 (1.17-7.73) for high-stable class, 2.15 (0.63-7.38) for U-shape class and 7.15 (2.08-24.5) for inverse U-shape class (p for trend = 0.012), respectively. Multivariable-adjusted hazard ratios of type 2 diabetes associated with postpartum BMI TTR of 100%, >43.4%-<100%, >0%-≤43.4% and 0% were 1.00, 1.84 (0.72-4.73), 2.75 (1.23-6.15) and 2.31 (1.05-5.08) (p for trend = 0.039), respectively. Postpartum BMI trajectories of high-stable and inverse U-shape class as well as lower TTR were associated with an increased risk of type 2 diabetes among women with a history of GDM. Reducing BMI to a normal range in the early postpartum period and maintaining stable over time could attenuate the development of long-term type 2 diabetes.

Patterns of intrinsic capacity trajectory and onset of activities of daily living disability among community-dwelling older adults.

Global population ageing has brought about new challenges for elderly care. Exploring intrinsic capacity (IC) over time, which is designed as a composite measure of an individual's physical and mental capabilities, is essential for promoting healthy ageing and preventing dependency, such as that emerging from disability in activities of daily living (ADL). We aimed to identify and examine the differences between classes of IC trajectory and onset of ADL disability. We conducted an observational study using data from three waves (2011-15) of the China Health and Retirement Longitudinal Study, comprising 2609 participants with 6034 observations. IC was measured by five domains, including locomotion, cognition, psychological, sensory capacities, and vitality. We used joint latent class modelling to identify distinct classes with similar patterns of IC trajectory and onset of ADL disability, as well as to explore the variation in IC trajectory and predict five-year risks of ADL disability considering the heterogeneity in the elderly population. The average baseline IC score was 7.15 (range: 0-15). We observed that IC scores slowly decreased with age, with 17.25% of participants developing ADL disability. We identified three classes of IC, which could be described as moderate health (class 1: n = 1634, 62.63%), at-risk (class 2: n = 716, 27.44%; had the highest risk of ADL disability), and optimal health (class 3: n = 259, 9.93%; had the lowest baseline risk of ADL disability). The probability of being in the moderate health class was decreased the most by emotional problems (odds ratio (OR) = 0.219; P < 0.001). Having a self-rated poor standard of living substantially reduced the chances of moderate (OR = 0.308; P = 0.001) and optimal health (OR = 0.110; P < 0.001). Observing IC trajectories and the onset of ADL disability can stratify the elderly into heterogeneous groups, as well as provide data for implementing person-centred care plans to reverse the trend and delay the adverse outcomes in clinical practice.

Exploring residents’ willingness-to-pay for benefits provided by Sustainable Drainage Systems

ABSTRACT Sustainable Drainage Systems (SuDS) have increasingly been used in the recent years since they help adapt cities to climate change and make them more sustainable and resilient. Since they are multifunctional, these stormwater control measures provide numerous benefits. However, the question of acceptability by inhabitants remains partially open. This article seeks to assess preferences of the inhabitants living nearby with regards to SuDS implemented on public land. It relies on a discrete choice experiments whose attributes are the different benefits provided by SuDS. The study was conducted in the Eurometropolis of Strasbourg (France). Results show a strong preference for a reduction of urban heat island effects as well as flood prevention and water protection. Based on a latent class model, we also highlight the heterogeneous nature of the preferences. These results are important for the acceptance of environmental programs and a better guarantee of desirable and effective results.

A Two-Step Q-Matrix Estimation Method.

Cognitive Diagnosis Models in educational measurement are restricted latent class models that describe ability in a knowledge domain as a composite of latent skills an examinee may have mastered or failed. Different combinations of skills define distinct latent proficiency classes to which examinees are assigned based on test performance. Items of cognitively diagnostic assessments are characterized by skill profiles specifying which skills are required for a correct item response. The item-skill profiles of a test form its Q-matrix. The validity of cognitive diagnosis depends crucially on the correct specification of the Q-matrix. Typically, Q-matrices are determined by curricular experts. However, expert judgment is fallible. Data-driven estimation methods have been developed with the promise of greater accuracy in identifying the Q-matrix of a test. Yet, many of the extant methods encounter computational feasibility issues either in the form of excessive amounts of CPU times or inadmissible estimates. In this article, a two-step algorithm for estimating the Q-matrix is proposed that can be used with any cognitive diagnosis model. Simulations showed that the new method outperformed extant estimation algorithms and was computationally more efficient. It was also applied to Tatsuoka's famous fraction-subtraction data. The paper concludes with a discussion of theoretical and practical implications of the findings.

MRI-Based Brain Tumour Classification Using Convolutional Neural Networks: A Systematic Review and Meta-Analysis

This research assessed advancements in brain tumour classification using convolutional neural networks (CNNs) and MRI data. An analysis of 37 studies highlighted the effectiveness of CNN architectures and pre-processing methods in accurately categorising brain tumours. Issues such as class disparities and model interpretability were identified, prompting recommendations for advanced deep learning techniques, ensemble methods, and diverse datasets to enhance diagnostic accuracy. The findings underscored the importance of these methods in achieving high accuracy, with a maximum rate of 98.80% from 154 MRI images. This systematic study also included a meta-analysis from 2018 to 2022, revealing patterns in MRI cases across demographics and providing insights into healthcare trends.

Novel Approach to Identify Severe Maternal Morbidity Clusters: A Latent Class Analysis.

Whether clusters exist within severe maternal morbidity (SMM), a set of life-threatening heterogeneous conditions, is not known. Our primary objective was to identify SMM clusters using a data-driven clustering technique, their associated predictors and outcomes. From 2008 to 2017, we used a delivery database supplemented by state data and medical record abstraction from a single institution in Pennsylvania. To identify SMM clusters, we applied latent class modeling that included 23 conditions defined by 21 Centers for Disease Control SMM indicators, intensive care unit (ICU) admission, or prolonged postpartum length of stay. Logistic regression models estimated risk for SMM clusters and associations between clusters and maternal and neonatal outcomes. Among 97,492 deliveries, 2.7% (N = 2,666) experienced SMM by any of the 23 conditions. Four clusters were identified as archetypes of SMM. Deliveries labeled as Hemorrhage (37.7%, N = 1,004) were characterized by blood transfusions and sickle cell anemia; Critical Care (28.1%, N = 748) by ICU admission and amniotic embolism; Vascular (24.5%, N = 654) by cerebrovascular conditions; and Shock (9.8%, N = 260) by ventilatory support and shock. Hypertensive disorders of pregnancy, depression, and Medicaid insurance were associated with Shock cluster. People in all clusters had a high risk of maternal death within 1 year (odds ratio: 12.0, 95% confidence interval: 6.2-23). Infants born to those in the shock cluster had the highest odds of neonatal death, low Apgar scores, and neonatal ICU admission. We identified four novel SMM clusters that may help understand the collection of conditions defining SMM, underlying pathways and the importance of comorbidities such as depression and social determinants of health markers that amplify the well-established risk factors for SMM such as hypertensive disorders of pregnancy. · A total of 2.7% of deliveries experienced SMM events.. · There are four distinct SMM clusters: Hemorrhage, Critical Care, Vascular, and Shock.. · Not all SMM clusters bear the same risk for adverse perinatal outcomes..

Air travellers’ attitudes towards carbon emissions: evidence from the Google Flights interface

Reducing carbon dioxide emissions in the aviation industry is a formidable challenge. Understanding airline travellers’ willingness to pay for emission reductions could lead to impactful changes in flight pricing and government policies. In this study, we employ a discrete choice experiment to more accurately gauge this willingness to pay. The experiment features a comprehensive analysis of both leisure and business traveller categories, utilising a realistic interface mirroring Google Flights for journeys between Los Angeles and New York. The findings demonstrate that, on average, business flyers exhibit a significantly higher willingness to pay for emission reductions compared to leisure flyers. Notably, a subset of emissions conscious leisure flyers also exhibits a markedly higher willingness to pay. Latent class modelling indicates that this emissions conscious group accounts for about 42% of the leisure flyer sample. The larger group of price focused leisure flyers does not display any significant willingness to pay for emission reduction. Another key finding is that ethical consumption behaviour, not just stronger perceptions related to climate change, is linked to emissions-reducing preferences among both types of flyers. These results provide valuable insights for policy and pricing strategies, emphasising the heterogeneity across and within the business and leisure flyer segments.

Residents' perceptions of what makes feedback valuable in workplace-based learning: A discrete choice experiment.

Research on feedback has shifted emphasis away from its 'delivery' to consideration of the interaction between individual learners and their 'feedback provider'. The complexity inherent in determining whether feedback is perceived as valuable by learners, however, can quickly overwhelm educators if every interaction must be considered completely idiosyncratic. We, therefore, require a better understanding of variability in the ways in which feedback is perceived. To that end, we ran a discrete choice experiment aimed at determining residents' preferences and whether discernible patterns exist across learners regarding factors that influence perceptions of feedback's learning value. We performed a discrete choice experiment in which respondents were asked to read a clinical case and select repetitively between two feedback scenarios that differed according to six attributes identified from the literature as influencing feedback credibility: Dialogue, Focus, Relationship, Situation, Source and Valence. By systematically varying the levels of each attribute contained in the scenarios and asking residents to choose which from each pair they deemed more valuable for learning, a mixed logit model and latent class analysis could be applied to determine learners' feedback preferences and whether clusters of preference exist. Ninety-five elderly care medicine residents in the Netherlands completed the questionnaire. Their responses indicated that Valence, Dialogue, Relationship and Focus each accounted for about 20% of their preferences regarding the type of feedback perceived to offer the most learning value. Source and Situation were less influential, each accounting for 11% of the choices made. A latent class model with three clusters of respondents best accounted for the heterogeneity in feedback preferences. A total of 62% of respondents could be assigned to one of the three profiles with at least 80% probability. None of the respondents' characteristics (seniority, residency programme nor sex) were related to the feedback preference profile. Our findings suggest that 'how' feedback is provided has a greater influence on perceived learning value than who provides it. That said, variability exists in resident perceptions with no evidence (as yet) of factors that predict individual preferences. As such, tailoring to the needs and reactions of individual learners is likely to require open and ongoing conversations, and we recommend using the learner profiles generated through this study as a starting point because they provide classifications that could facilitate effective connections for the majority of residents.

Water and energy deprivation: Addressing the problem of essential utility services poverty in Mayotte

Among the 17 sustainable development goals adopted by the United Nations in 2015 are ensuring “availability and sustainable management of water and sanitation” and “access to modern energy sources”. One problem for Mahorais households is access to safe, clean and affordable essential utility services, such as electricity, water and sewerage. Moreover, Mayotte, a French oversea department, is experiencing an unprecedented drought. The island, which is sinking into a severe water crisis, has been depriving its inhabitants of running water two days out of three since the beginning of September 2023. We define a new concept of utility services poverty (basic utility services deprivation) based on the theoretical capabilities framework of Sen and Nussbaum. Using a latent class model, we identify households that are poor in utility services and characterize four household profiles. One main advantage of this methodology is replicability in many Low and Middle Income Countries’ (LMICs). We demonstrate that access to water is more discriminatory than access to electricity in Mayotte. The top priority in fighting utility services poverty should be access to water and sanitary facilities. Public policies should be implemented not according to income but to facilitate water and energy access and improve basic hygiene conditions notably through social housing.

Pure anomaly detection via self-supervised deep metric learning with adaptive margin

We address the problem of anomaly detection (AD) by a deep network pretrained using self-supervised learning for an auxiliary geometric transformation (GT) classification task. Our key contribution is a novel loss function that augments the standard cross-entropy by an additional term that plays a significant role in the later stages of self-supervised learning. The proposed enabling innovation is a triplet centre loss with an adaptive margin and a learnable metric, which relentlessly drives the GT classes to exhibit continuously improving compactness and inter-class separation. The pretrained network is finetuned for the downstream task using non-anomalous data only, and a GT model for the data is constructed. Anomalies are detected by fusing the output of several decision functions defined using the learnt GT class model. In contrast to the majority of existing methods, our approach strictly adheres to the pure AD design philosophy, which relies on the use of purely non-anomalous data for the design. Extensive experiments on four publicly available AD datasets demonstrate the effectiveness of the proposed contributions and lead to significant performance gains compared to the state-of-the-art (1.8% on F-MNIST, 1.0% on CIFAR-10, 1.2% on CIFAR-100, and 1.7% on CatVsDog). https://github.com/12sf12/Deep-Anomaly-Detection

Plant the pot! Understanding consumer willingness to pay for sustainability in garden shop products

This study explores consumer preferences for various sustainability attributes in gardening pots using a discrete choice experiment. Data were collected from 499 respondents in garden shops across Milan and Monza, Italy. The study examines different aspects of sustainability, focusing on attributes that influence consumer purchasing decisions, such as the use of renewable energy, recycled material usage, ethical certification, and end-of-life options (recyclable, reusable, and biodegradable pots). The primary objectives are to determine consumers' preferences and the relative importance of these sustainability attributes in gardening pots and assess the impact of educational information on these preferences and willingness to pay. A mixed logit model is employed to capture the variations in preferences across the entire sample, providing a detailed analysis of the average willingness to pay for each sustainability attribute. A latent class model aimed to identify distinct segments within the sample, revealing specific groups of consumers with unique preference patterns and varying monetary evaluations for sustainable features. The results indicate strong preferences for reusable and recyclable pots, followed by biodegradable options and products made with recycled materials. Additionally, providing consumers with educational information about the environmental impacts of plastic pots significantly influenced their preferences and willingness to pay. This study contributes to the body of knowledge by offering detailed insights into consumer behaviour towards sustainable gardening products and their relative importance and providing actionable recommendations for policymakers and marketers to foster sustainable consumption practices.

Patient preferences for the provision of NHS medicines helpline services: a discrete choice experiment.

Patient medicines helpline services (PMHS) can reduce harm and improve medicines adherence and patient satisfaction after hospital discharge. There is little evidence of which PMHS attributes are most important to patients. This would enable PMHS providers to prioritise their limited resources to maximise patient benefit. Patient preferences for PMHS attributes were measured using a discrete choice experiment. Seven attributes were identified from past research, documentary analysis and stakeholder consultation. These were used to produce a D-efficient design with two blocks of ten choice sets incorporated into an online survey. Adults in the UK who took more than one medicine were eligible to complete the survey and were recruited via the Research for the Future database. Preferences were estimated using conditional logistic regression. Associations between participant characteristics and preferences were investigated with latent class models. 460 participants completed the survey. The most valued attributes were weekend opening (willingness-to-pay, WTP: £11.20), evening opening (WTP: £8.89), and receiving an answer on the same day (WTP: £9.27). Alternative contact methods, immediate contact with a pharmacist and helpline location were valued less. Female gender and full-time work were associated with variation in preferences. For one latent class containing 27% of participants, PMHS location at the patient's hospital was the most valued attribute. PMHS providers should prioritise extended opening hours and answering questions on the same day. Limitations include a non-representative sample in terms of ethnicity, education and geography, and the exclusion of people without internet access.