Meaning in life research: the importance of considering auxiliary assumptions

There have been significant advances in the science of meaning in life (MIL). Researchers have made empirical predictions about the antecedents and consequences of meaning and the best ways it can be enhanced. Yet, it is important that researchers in this area consider the auxiliary assumptions associated with their predictions. Auxiliary assumptions, which traverse the distance from nonobservational theoretical terms to observational terms at the level of the empirical hypotheses, have important implications for the appraisal of empirical victories and defeats. In this paper, we outline the importance of auxiliary assumptions in MIL research. To ensure the validity of findings associated with MIL, we hope this paper encourages researchers to pay close attention to the auxiliary assumptions associated with their predictions

Simulating hierarchical data to assess the utility of ecological versus multilevel analyses in obtaining individual-level causal effects

Understanding causality, over mere association, is vital for researchers wishing to inform policy and decision making – for example, when seeking to improve population health outcomes. Yet, contemporary causal inference methods have not fully tackled the complexity of data hierarchies, such as the clustering of people within households, neighbourhoods, cities, or regions. However, complex data hierarchies are the rule rather than the exception. Gaining an understanding of these hierarchies is important for complex population outcomes, such as non-communicable disease, which is impacted by various social determinants at different levels of the data hierarchy. The alternative of analysing aggregated data could introduce well-known biases, such as the ecological fallacy or the modifiable areal unit problem. We devise a hierarchical causal diagram that encodes the multilevel data generating mechanism anticipated when evaluating non-communicable diseases in a population. The causal diagram informs data simulation. We also provide a flexible tool to generate synthetic population data that captures all multilevel causal structures, including a cross-level effect due to cluster size. For the very first time, we can then quantify the ecological fallacy within a formal causal framework to show that individual-level data are essential to assess causal relationships that affect the individual. This study also illustrates the importance of causally structured synthetic data for use with other methods, such as Agent Based Modelling or Microsimulation Modelling. Many methodological challenges remain for robust causal evaluation of multilevel data, but this study provides a foundation to investigate these

Measuring Self-Reported Well-Being of Physicians Using the Well-Being Thermometer: Cohort Study

Background: Advancements in medical science have focused largely on patient care, often overlooking the well-being of health care professionals (HCPs). This oversight has consequences; not only are HCPs prone to mental and physical health challenges, but the quality of patient care may also endure as a result. Such concerns are also exacerbated by unprecedented crises like the COVID-19 pandemic. Compared to other sectors, HCPs report high incidence of stress, depression, and suicide, among other challenging factors that have a significant negative impact on their well-being. Objective: Given these substantial concerns, the development of a tool specifically designed to be used in clinical settings to measure the well-being of HCPs is essential. Methods: A United Kingdom–based cross-sectional pilot study was carried out to measure self-reported well-being in a cohort of 148 physicians, using the newly developed well-being thermometer. The aim of the tool is to allow respondents to develop an individual sense of “well-being intelligence” thus supporting HCPs to have better insight and control over their well-being and allow insights into how to manage it. The tool consists of 5 well-being domains—health, thoughts, emotions, spiritual, and social. Each domain can be measured individually or combined to produce an overall well-being score. Results: The tool demonstrated good internal consistency; the Cronbach α in this study was 0.84 for the total scale. Conclusions: Results from this cohort demonstrated that the well-being thermometer can be used to gather intelligence of staff well-being. This is a promising new tool that will assist HCPs to recognize their own well-being needs and allow health care organizations to facilitate change in policies and practices to reflect a better understanding of staff well-being

The Relationship Between Tensor Fascia Latae and Gluteus Maximus Has the Potential to Indicate Early Intra-articular and Degenerative Pathologies of the Femoral-Acetabular Joint: A Narrative Review.

Intra-articular and degenerative hip pathologies have become common place with the number of total hip replacements rising year on year in the United Kingdom (UK). Pathology is identified by clinicians using special tests which are researched maneuvers used by clinicians to rule in or rule out specific musculoskeletal pathologies. Special tests used for hip pathology usually have high specificity to exclude degenerative and intraarticular pathology but vary in sensitivity. These special tests are usually only conducted when a person is symptomatic and typically require radiological confirmation to diagnose. The aim of this review was to appraise research to determine whether functional changes in the TFL and UGM muscle complex could indicate degenerative and/or intra articular pathology, with a specific focus on the utility of the ratio in strength of TFL and UGM to assist clinical diagnosis. The hypothesis was that the ratio of the strength of Tensor Fascia Latae (TFL) and the upper fibres of Gluteus Maximus (UGM) could suggest early intra-articular hip pathology, and that changes to this ratio could indicate deterioration of the hip joint before symptoms present/progress. Level of Evidence

Multilevel Safety Climate for Lone Heavy Vehicle Drivers in the UK Quarrying Industry: Validation of the Heavy Vehicle Safety Climate Scale (HVSCS)

The primary goal of our research was to validate a context-specific safety climate measure (the Heavy Vehicle Safety Climate Scale: HVSCS) in a sample of heavy mobile equipment operators (N = 277). An exploratory strategy was adopted, using exploratory factor analysis (EFA) to validate the items. The statistical results revealed a five-factor structure, with two factors at the organisational level and three factors at the group level. In addition, a nomological analysis showed that both organisational and supervisory safety climate factors presented distinct correlation patterns with other safety-related variables, including situational and routine violations, safety citizenship behaviour, context-specific safety behaviours and risk propensity. In this study we developed and psychometrically validated a context-specific safety climate tool for lone heavy vehicle drivers in the quarrying industry: the Heavy Vehicle Safety Climate Scale (HVSCS). It is hoped that the final 37-item HVSCS will be utilised by those managing heavy vehicle operations, particularly in the quarrying industry, to identify context-specific opportunities for safety climate improvements and in turn reduce the risk of safety incidents

A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings

Improving energy efficiency is a major concern in residential buildings for economic prosperity and environmental stability. Despite growing interest in this area, limited research has been conducted to systematically identify the primary factors that influence residential energy efficiency at scale, leaving a significant research gap. This paper addresses the gap by exploring the key determinant factors of energy efficiency in residential properties using a large-scale energy performance certificate dataset. Dimensionality reduction and feature selection techniques were used to pinpoint the key predictors of energy efficiency. The consistent results emphasise the importance of CO2 emissions per floor area, current energy consumption, heating cost current, and CO2 emissions current as primary determinants, alongside factors such as total floor area, lighting cost, and heated rooms. Further, machine learning models revealed that Random Forest, Gradient Boosting, XGBoost, and LightGBM deliver the lowest mean square error scores of 6.305, 6.023, 7.733, 5.477, and 5.575, respectively, and demonstrated the effectiveness of advanced algorithms in forecasting energy performance. These findings provide valuable data-driven insights for stakeholders seeking to enhance energy efficiency in residential buildings. Additionally, a customised machine learning interface was developed to visualise the multifaceted data analyses and model evaluations, promoting informed decision-making

Integrating Mental Health in Curriculum Design: Reflections from a Case Study in Sport, Exercise, and Health Science

The rise of mental health concerns in today’s Higher Education (HE) students is a growing concern. Integrating mental health principles into curricular design can enhance the student experience and improve outcomes such as continuation, completion, and success. This paper aims to integrate mental health guidance into the design of a revalidated curriculum in sport, exercise, and health sciences. A further aim is to provide recommendations for future mentally healthy curricular design. A case study design exploring how mental health does, and might, feature across curricula was undertaken, employing a qualitative methodology. A range of stakeholders, including students, participated in workshops, interviews, and revalidation events over a 4-month period. The analysis and interpretation of the resulting transcripts proposed two main lines of action; ‘building students’ (centring on helping them navigate the nuances of the academic process and developing confidence in their university self, for example, through systematic scaffolding) and ‘building connections’ (focusing on enhancing social connections and psychological safety, for example learning student names). Recommendations proposed by this research highlight that induction should be primarily social, progressing to more fundamental skills, behaviours, and knowledge. Systematic scaffolding and mapping of relevant skills, behaviours, and knowledge throughout the course are fundamental to building students’ confidence. This could reduce both academic staff and students’ frustrations about a lack of ‘know-how’. Staff–student interactions and a focus on micro-behaviours could further enhance a student’s sense of belonging and desire to engage

Land cover change across the major proglacial regions of the sub-Antarctic islands, Antarctic Peninsula, and McMurdo Dry Valleys, during the 21st century

Land cover information is essential for understanding Earth surface processes and ecosystems. Here, we use K-means clustering to classify Landsat 8 Operational Land Imager (OLI) images covering six proglacial sites of sub-Antarctic islands, the Antarctic Peninsula, and the McMurdo Dry Valleys at 30-m resolution. We quantify spatial patterns of water, bedrock, vegetation, and sediments to an accuracy of 77 percent. Vegetation is most abundant on South Georgia (7 percent of the proglacial area) and the South Shetland Islands (1 to 2 percent). Furthermore, we use change vector analysis (CVA) to discriminate landcover change in the twenty-first century. A latitudinal pattern is evident in ice loss and proglacial landscape change; for example, loss of ice on South Georgia and proglacial landcover change is two orders of magnitude greater than in the McMurdo Dry Valleys. Four of the studied sites had similar landscape stability (64 to 68 percent unchanged), with Alexander Island an exception (50 percent change) due to recent enhanced glacier melt. Overall, we show how landcover of proglacial regions of the climaticallysensitive sub-Antarctic and Antarctica has changed since 2000, with a CVA accuracy of 80 percent. These findings inform understanding of geomorphological activity and sediment and nutrient fluxes and hence terrestrial and marine ecosystems

The multidimensional profiling of youth male rugby union players: a systematic scoping review, nominal group technique and survey

This three-part study aimed to 1) investigate the most common profiling practices in male rugby union; 2) identify factors profiled within youth players; and 3) assess the importance of these factors for player progression and their measurement feasibility. Part one employed a systematic scoping review. For part two, expert practitioners participated in a Nominal Group Technique session to identify factors to profile within youth male rugby union. Part three included practitioners from a Tier One rugby nation and researchers, who ranked their agreement for importance of the identified factors, and their measurement feasibility. The review identified 107 studies profiling 50 factors across five themes: physical (n=67 studies), demographic (n=25), psychological (n=20), technical (n=20), and tactical (n=6). Expert practitioners reported an additional 20 factors that should be profiled. Over 70% of survey participants agreed that 40 factors were important for progression and 28 factors were feasible to measure. Only 15 factors reached 70% agreement for both importance and feasibility, including strength, power, and games played. Factors across all themes were considered important, re-emphasising the need for multi-dimensional profiling within youth male rugby union. Further research is required to enhance the feasibility of measuring these factors and create a multidimensional player profile

Impact of Patients Decision Aids on Shared Decision‐Making and Patient Satisfaction Prior to Pelvic Floor Surgery

Objective Evaluate usability and utility of National Institute for Clinical Excellence (NICE) Patient Decision Aid's (PtDA's) for pelvic floor surgery. PtDA's reviewed were uterine prolapse, vault prolapse and stress urinary incontinence (SUI). Design Ten women given the PtDA's during routine clinical care were recruited from each cohort and underwent a semi‐structured interview with a clinical researcher. Setting Urogynaecology outpatients in an NHS tertiary teaching hospital. Population or Sample Women considering surgical management of uterine/vault prolapse or SUI. Exclusion criteria included those under 18, unable to communicate in English or not eligible for all surgical options discussed in the PtDA's. Methods A qualitative, semi‐structured interview evaluating women's opinions of the decision aid and the way in which they utilised the PtDA was conducted. The interviews were recorded and transcribed prior to undertaking thematic analysis utilising NVivo software. Main Outcome Measure The outcomes of interest were feedback for content, language, format and usage of the PtDA's and women's usage of PtDA's in decision‐making. Results Amendments suggested included removal of mesh from the SUI PtDA as this is not routinely available on the NHS and addition of a statement regarding the use of mesh in prolapse surgery. Additional anatomical diagrams were suggested. The need for a robust and regular update system was highlighted as was the provision of foreign language, audio and electronic versions. Conclusions PtDA's need updating to ensure relevant content. Clear, detailed and relevant information is required alongside active clinician engagement to reach a mutually agreeable treatment plan