11237 research outputs found
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The International Climate Psychology Collaboration: Climate change-related data collected from 63 countries
No full textClimate change is currently one of humanity’s greatest threats. To help scholars understand the psychology of climate change, we conducted an online quasi-experimental survey on 59,508 participants from 63 countries (collected between July 2022 and July 2023). In a between-subjects design, we tested 11 interventions designed to promote climate change mitigation across four outcomes: climate change belief, support for climate policies, willingness to share information on social media, and performance on an effortful pro-environmental behavioural task. Participants also reported their demographic information (e.g., age, gender) and several other independent variables (e.g., political orientation, perceptions about the scientific consensus). In the no-intervention control group, we also measured important additional variables, such as environmentalist identity and trust in climate science. We report the collaboration procedure, study design, raw and cleaned data, all survey materials, relevant analysis scripts, and data visualisations. This dataset can be used to further the understanding of psychological, demographic, and national-level factors related to individual-level climate action and how these differ across countries.</p
Financing and investing in sustainable infrastructure: A review and research agenda
No full textFinancing and investing in sustainable infrastructure play a pivotal role in achieving the United Nations Sustainable Development Goals, particularly considering their multifaceted benefits to the environment, society, and economy. This systematic literature review applies the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore the financing and investment aspects of sustainable infrastructure. With support of a comprehensive collection of 4,308 publications dated from 2009 to 2023, we analyse various types of sustainable infrastructure and their investment and financing strategies by employing bibliometric analysis on 74 most closely related journal articles in a network approach setting. Results show the growing significance of green finance as a central research theme and a prevailing trend within this domain. Drawing upon these findings, we propose a conceptual framework for the integration of green finance into sustainable infrastructure development, offering insights to policy makers and guide future research agendas aimed at advancing green finance in infrastructure sectors.</p
Co-developing a health literacy framework to integrate nutrition into standard care in sickle cell disease
No full textNutrition in Sickle Cell Disease (SCD) is a neglected part of standard care impacting patient outcomes, despite it being widely researched. Moreover, the clinical features of SCD, a marginalised genetically inherited blood disorder, are responsible for growth and nutritional deficiencies that require nutrition service provision. Thus, a need exists to identify the influencing factors affecting the lack of nutrition integration in SCD. Presently, a paucity of research exists on how to integrate nutrition into standard care in SCD using health literacy in a novel way to support policy and practice development.The study adopted a four phased sequential participatory Learning Alliance Methodology, involving sickle cell service users and carers (n=11) and service providers (n=7), between March to December, 2020, to co-develop a health literacy framework to support nutrition integration in SCD. Independent focus groups (phase one), network meetings (phase two, three and four) and an evaluation questionnaire, was used to collect the data.Thematic analysis of the focus groups outcomes revealed four common themes namely; (1) Invisibility of SCD, (2) Under-recognised importance of nutrition, (3) Lack of priority to nutrition and (4) Multi-level factors affecting nutrition and service provision that together reflect key influencing factors identified as knowledge and care gaps, essential to tailor policy and practice in nutrition in SCD. Following consensus development and validation through network meetings, the evaluation of the health literacy framework (phase four), found the framework to be a valuable educational, communication and policy tool.Overall the findings confirm the complexity, invisibility and neglect of nutrition service provision as part of standard care in SCD, a health inequality impacting patient experience, access and health outcomes, explained by the marginalisation of SCD. Hence, the influencing factors identified in the study require a whole systems policy and practice strategy to integrate nutrition into standard care in SCD.</p
Drivers That Affect Households to Reduce Food Waste: A UK Qualitative Study
No full textIndividual households make significant contributions to food waste. Combating this waste would allow for better food distribution globally that can help combat global hunger. As there is currently a limited understanding as to why people waste food, we conducted semi-structured interviews with people that had taken part in a food waste reduction study, to explore drivers that contribute to reduced food waste within UK homes. Using a thematic analysis, four themes were identified based around the importance of thinking about food waste, having a flexible approach to food waste, as well as being emotionally engaged in food waste reduction processes. It was also explored if others have a role to play in whether people try to reduce their waste; however, contrary to previous findings, such a notion was not supported here. The implications of the findings are discussed.</p
Integrated care systems in England: the significance of collaborative community assets in promoting and sustaining health and wellbeing
No full textUntil recently the healthcare system in England was based on a commissioning/provider model. However, this has been replaced with an Integrated Care Systems (ICSs) approach, aimed at improving health and wellbeing and reducing inequalities through local collaborative partnerships with public sector organizations, community groups, social enterprise organizations and other local agencies. Part of this new approach is an emphasis on the role of community assets (i.e., local resources), that are considered integral to promoting positive health and wellbeing outcomes. This paper presents research from a series of three research studies on “community assets” conducted in the East of England within a newly established ICS. Based on analysis of qualitative data highlighting the lived experience of community asset members, this paper shows the positive wellbeing impact on vulnerable community members that assets provide. Further insight on the local impact and the collaborative nature of the research is provided suggesting that new asset-based approaches recognize the social determinants of health. This presents a shift away from positivistic linear approaches to population health and wellbeing to a new non-linear collaborative approach to addressing health inequalities and promoting wellbeing. The authors suggest that exploring this through a complexity theory lens could illuminate this further. Finally, the authors warn that while community assets have an important role to play in empowering citizens and providing much needed support to vulnerable and disadvantaged communities, they are not a substitute for functioning funded public sector services that are currently being undermined by ongoing local governments funding cuts. As such, while community assets can help ameliorate some of the negative effects people experience due to economic, structural and health disadvantages, only a more fair and more equal distribution of resources can address growing health inequalities</p
Association between center procedure volume, socioeconomic factors, comorbidities, and adverse events related to procedural abortion: A nationwide population-based cohort study
No full textackground:
Limited evidence exists on the influence of center procedure volume, socioeconomic factors, and comorbidities on procedural abortion outcomes.
Objective:
Our study aimed to assess the association between center procedure volume, individual and neighborhood deprivation, comorbidities, and abortion-related adverse events.
Study Design:
A nationwide population-based cohort study of all pregnant persons admitted for procedural abortion day surgery was conducted from January 1, 2018, to December 31, 2019 in France. Annual procedure volume was categorized into four levels based on spline function visualization: very low (
Results:
Of the 112,842 day surgery stays, 4,951 (4.39%) had surgical-related adverse events and 256 (0.23%) had general adverse events. The multiple regression showed a volume-outcome relationship, with lower rates of surgical-related adverse events in very high-volume (2.25%, aOR=0.50, 95%CI [0.44-0.56], p
Conclusion:
The presence of a volume-outcome relationship suggests a need to enhance safety in low-volume centers, thereby ensuring equity in pregnant persons' safety during procedural abortions. However, our findings also highlight the complexity of this safety concern which involves multiple other factors including socioeconomic factors and comorbidities that policymakers
must consider.</p
Synchrony during music therapy and its relationship to self-reported therapy readiness: a mixed-methods case series study
No full textNonverbal synchronisation is a crucial aspect of therapeutic relationships, and heart-rate synchrony relates to shared states and joint participation. Differentiating who leads during the synchrony provides further important information.This mixed-methods PhD study investigated the emergence of these synchronies and their relationships to therapy readiness during single music therapy sessions. 11 patients from a neurorehabilitation setting, without restrictions of verbal processing or physical movements, participated with a music therapist. A comparison of dyadic therapy interactions and leading characteristics in segments of high and low heart-rate synchrony, and between dyads with high positive and negative change in nonverbal synchrony were made. Heart-rate synchrony and dyadic therapy interactions during moments of interest were also examined. The aim is to deepen the understanding of interpersonal synchrony in music therapy research, probe the tools available, and make recommendations to improve music therapy in neurology.Nonverbal and heart-rate synchrony were found to exist beyond randomness during music therapy sessions. After music intervention, nonverbal synchrony and patient leading increased, and a negative correlation was found between nonverbal synchrony and self-reported therapy readiness. Patients showed more empowerment and external awareness, and the dyads appeared more connected and relaxed, in segments of high heart-rate synchrony as compared to low heart-rate synchrony segments. Changes in nonverbal synchrony were found to be related to the relationship quality (independent vs dependent) and the revelations during music interventions. Heart-rate synchrony was negatively correlated to the therapist’s assessment of the patient’s therapy readiness. Moments of interest appeared during high and low heart-rate synchrony segments, and their prevalence suggests the perceptions of such moments. Based on the interaction observations, high heart-rate synchrony is desirable in neurorehabilitation. Music intervention duration was found to influence the level of heart-rate synchrony, and the results indicated an optimal active music intervention duration of about 25 minutes.</p
Learning from AI-Generated Annotations for Medical Image Segmentation
No full textLearning from AI-generated annotations is wellrecognized as a key advance of deep learning techniques in medical image segmentation. Towards this direction, in this paper, we investigate two questions: (1) how to accurately measure loss value on AI-generated annotations that often contain errors and (2) how to effectively update model’s parameters when the loss value is no longer a correct supervision for medical image segmentation. The main results are that (1) ‘error-tolerant’ loss functions exist and (2) ‘cross-training’, updating the model using data with a small loss of its ‘twin’ model, can tolerate the loss function to some extent. Per the main results, we yet derived a robust training algorithm, called confidence regularized coteaching, that helps deep models to combat annotation errors in medical image segmentation. This algorithm simultaneously trains two ‘twin’ segmentation models and updates model’s parameters by cross-training with disagreement confident data that are predicted differently by the two models, thereby being able to learning from data with annotation errors. The empirical evidence from a publicly available dataset shows that this new algorithm works better on combating annotation errors than existing methods for medical image segmentation, opening the opportunity to use AI-generated annotations to train segmentation model for medical image segmentation.</p
Validity and Reliability of the FlightScope Mevo+ Launch Monitor for Assessing Golf Performance
No full textValidity and reliability of the FlightScope Mevo+ launch monitor for assessing golf performance. J Strength Cond Res 38(4): e174–e181, 2024—The purpose of this study was to (a) assess the validity of the FlightScope Mevo+ against the TrackMan 4 and (b) determine the within-session reliability of both launch monitor systems when using a driver and a 6-iron. Twenty-nine youth golfers, with a minimum of 3 years of playing experience, volunteered for this study. All golfers completed 10 shots with a 6-iron and a driver, with 8 metrics concurrently monitored from both launch monitor systems in an indoor biomechanics laboratory. For both clubs, Pearson's r values ranged from small to near perfect (r range = 0.254–0.985), with the strongest relationships evident for clubhead speed (CHS) and ball speed (r ≥ 0.92). Bland-Altman plots showed almost perfect levels of agreement between devices for smash factor (mean bias ≤−0.016; 95% CI: −0.112, 0.079), whereas the poorest levels of agreement was for spin rate (mean bias ≤1,238; 95% CI: −2,628, 5,103). From a reliability standpoint, the TrackMan showed intraclass correlation coefficients (ICCs) ranging from moderate to excellent (ICC = 0.60–0.99) and coefficient of variation (CV) values ranged from good to poor (CV = 1.31–230.22%). For the Mevo+ device, ICC data ranged from poor to excellent (ICC = −0.22 to 0.99) and CV values ranged from good to poor (CV = 1.46–72.70%). Importantly, both devices showed similar trends, with the strongest reliability consistently evident for CHS, ball speed, carry distance, and smash factor. Finally, statistically significant differences (p < 0.05) were evident between devices for spin rate (driver: d = 1.27; 6-iron: d = 0.90), launch angle (driver: d = 0.54), and attack angle (driver: d = −0.51). Collectively, these findings suggest that the FlightScope Mevo+ launch monitor is both valid and reliable when monitoring CHS, ball speed, carry distance, and smash factor. However, additional variables such as spin rate, launch angle, attack angle, and spin axis exhibit substantially greater variation compared with the TrackMan 4, suggesting that practitioners may wish to be cautious when providing golfers with feedback relating to these metrics.</p
Adoption of Deep-Learning Models for Managing Threat in API Calls with Transparency Obligation Practice for Overall Resilience
No full textSystem-to-system communication via Application Programming Interfaces (APIs) plays a pivotal role in the seamless interaction among software applications and systems for efficient and automated service delivery. APIs facilitate the exchange of data and functionalities across diverse platforms, enhancing operational efficiency and user experience. However, this also introduces potential vulnerabilities that attackers can exploit to compromise system security, highlighting the importance of identifying and mitigating associated security risks. By examining the weaknesses inherent in these APIs using security open-intelligence catalogues like CWE and CAPEC and implementing controls from NIST SP 800-53, organizations can significantly enhance their security posture, safeguarding their data and systems against potential threats. However, this task is challenging due to evolving threats and vulnerabilities. Additionally, it is challenging to analyse threats given the large volume of traffic generated from API calls. This work contributes to tackling this challenge and makes a novel contribution to managing threats within system-to-system communication through API calls. It introduces an integrated architecture that combines deep-learning models, i.e., ANN and MLP, for effective threat detection from large API call datasets. The identified threats are analysed to determine suitable mitigations for improving overall resilience. Furthermore, this work introduces transparency obligation practices for the entire AI life cycle, from dataset preprocessing to model performance evaluation, including data and methodological transparency and SHapley Additive exPlanations (SHAP) analysis, so that AI models are understandable by all user groups. The proposed methodology was validated through an experiment using the Windows PE Malware API dataset, achieving an average detection accuracy of 88%. The outcomes from the experiments are summarized to provide a list of key features, such as FindResourceExA and NtClose, which are linked with potential weaknesses and related threats, in order to identify accurate control actions to manage the threats.</p
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