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A physiotherapist-led biopsychosocial education and exercise programme for patients with chronic low back pain in Ghana: a mixed-methods feasibility study
Get PDFData availability:
The data that support the findings of this study are available from the University of Nottingham, Faculty of Medicine and Health Sciences Research Ethics Committee, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of University of Nottingham, Faculty of Medicine and Health Sciences Research Ethics Committee.Electronic supplementary material is available online at: https://bmcmusculoskeletdisord.biomedcentral.com/articles/10.1186/s12891-024-08118-1#Sec34 .Background:
Low back pain is a common musculoskeletal condition which causes substantial disability globally. The biopsychosocial model of management has been recommended in national and international guidelines for the management of patients with chronic low back pain (CLBP). However, biopsychosocial approaches are predominantly delivered in high income countries (HICs), although the prevalence of LBP is substantially higher in low- and middle-income countries (LMICs) especially in Africa (39%; 95% CI 30–47). Understanding the effectiveness of BPS interventions in LMICs especially in Africa is underexplored, with substantial inequity between research from HICs and LMICs. Ghana is a LMIC where the effectiveness of biopsychosocial interventions has been underexplored. Therefore, the aim of this study was to explore the feasibility of delivering a physiotherapist-led BPS programme for the management of patients with CLBP in Ghana.
Methods:
This was a mixed-methods, sequential, pretest-posttest feasibility study. Participants involved thirty patients with CLBP. The biopsychosocial intervention involved an exercise and patient education programme based on principles of cognitive behavioural strategies with emphasis on self-management. The biopsychosocial intervention was delivered for six weeks for each participant. Feasibility outcomes regarding management and processes were captured pre-intervention, post-intervention, and three-months post intervention. Semi-structured interviews were conducted post-intervention to explore participants’ experiences with the biopsychosocial intervention. Patients’ demographics were collected at baseline. Patient reported outcome measures such as intensity of pain, disability, pain catastrophising, kinesiophobia, self-efficacy, and general quality of life, were collected pre-intervention, post-intervention and at three-months follow-up. Qualitative analysis explored participants’ experiences regarding the acceptability of the biopsychosocial intervention.
Results:
The results of this feasibility study demonstrated that the training programme was acceptable to physiotherapists. Recruitment rate (5 patient participants per week − 100% recruitment met), retention rate post-intervention (90%), data completion rate post-intervention (99.8%) and intervention fidelity (83.1%), all met feasibility thresholds. There were no adverse events. Qualitative data also demonstrated that the biopsychosocial intervention was acceptable to participants.
Conclusion:
This study has established the potential to deliver a biopsychosocial intervention programme in a Ghanaian hospital setting. This biopsychosocial intervention therefore shows promise, and the result of the study provides a platform to develop future clinical studies.There was no funding for this study
Mitigating Catastrophic Forgetting in Cross-Domain Fault Diagnosis: An Unsupervised Class Incremental Learning Network Approach
Get PDFWhile deep learning has found widespread application in fault diagnosis, it continues to face three primary challenges. First, it assumes that training and test datasets adhere to the same distribution, which is often not the case in industries with varying conditions. Second, it relies heavily on the availability of abundant labeled data for training, overlooking the reality that newly collected data are frequently unlabeled. Third, neural networks frequently encounter catastrophic forgetting, a critical concern in dynamic industrial settings with emerging faults. Therefore, this article proposes an unsupervised class incremental learning network (UCILN), to mitigate catastrophic forgetting in cross-domain fault diagnosis, particularly in situations where the target domain lacks labeled data. A memory module and a semifrozen and semiupdated incremental strategy are designed to balance the retention of old knowledge with the acquisition of new information. Test results obtained from the Case Western Reserve University (CWRU) and Paderborn University (PU) datasets demonstrate the exceptional performance of UCILN.Jiangsu Provincial Qinglan Project (2021);
Research Development Fund of Xi’an Jiaotong-Liverpool University (XJTLU) (Grant Number: RDF-20-01-18);
XJTLU Research Enhancement Fund (Grant Number: REF-23-01-008);
10.13039/501100018636-Suzhou Science and Technology Program (Grant Number: SYG202106)
Separable Convolutional Network-Based Fault Diagnosis for High-Speed Train: A Gossip Strategy-Based Optimization Approach
Get PDFWith the rapid development of high-speed train, health monitoring of high-speed train traction power system has gradually become a popular research topic. The traction asynchronous motor, as a key component in the traction power systems, greatly affects the reliability, stability, and safety of high-speed train operation. Normally, when faults occur, the train needs to immediately slow down or even stop to avoid unimaginable losses, resulting in limited fault data. Traditional data-driven fault diagnosis methods may face the local optimum problem during the optimization process when training samples are insufficient. In this study, a novel gossip strategy-based fault diagnosis method is proposed to prevent the local optimum problem, thus improving fault diagnosis performance. The proposed gossip strategy-based fault diagnosis method is validated on the hardware-in-the-loop high-speed train traction control system simulation platform, and the experimental results unequivocally show that the proposed method outperforms other well-known methods.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62233012);
Jiangsu Provincial Qinglan;
Research Development Fund of XJTLU (Grant Number: RDF-20-01-18);
XJTLU Research Enhancement Fund (Grant Number: REF-23-01-008);
Suzhou Science and Technology Programme (Grant Number: SYG202106)
Towards Algorithmic Luddism: class politics in data capitalism
Get PDFThis article examines responses to inequalities (re)produced by algorithms, particularly affecting disadvantaged social strata. Positioning class politics at the centre of the analysis of data capitalism, we turn attention to emerging pockets of collective action against algorithmic control. Drawing parallels to the Luddite movement of the nineteenth century, we develop the notion of Algorithmic Luddism along three intertwining tenets: refusal, resistance and re-imagining algorithmic futures. We attempt to reclaim Luddism from its reputation as an anti-technology movement towards one that centres around algorithmically accentuated inequalities. Advancing theorisation on social movements for the digital age, Algorithmic Luddism foregrounds the need for novel understandings of and engagement with class struggle in datafied societies.Lehtiniemi's work was supported by the REPAIR project, funded by the Strategic Research Council
Large AI Model Empowered Multimodal Semantic Communications
Get PDFMultimodal signals, including text, audio, image, and video, can be integrated into semantic communication (SC) systems to provide an immersive experience with low latency and high quality at the semantic level. However, the multimodal SC has several challenges, including data heterogeneity, semantic ambiguity, and signal distortion during transmission. Recent advancements in large AI models, particularly in the multimodal language model (MLM) and large language model (LLM), offer potential solutions for addressing these issues. To this end, we propose a large AI model-based multimodal SC (LAM-MSC) framework, where we first present the MLM-based multimodal alignment (MMA) that utilizes the MLM to enable the transformation between multimodal and unimodal data while preserving semantic consistency. Then, a personalized LLM-based knowledge base (LKB) is proposed, which allows users to perform personalized semantic extraction or recovery through the LLM. This effectively addresses the semantic ambiguity. Finally, we apply the conditional generative adversarial networks-based channel estimation (CGE) for estimating the wireless channel state information. This approach effectively mitigates the impact of fading channels in SC. Finally, we conduct simulations that demonstrate the superior performance of the LAM-MSC framework.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 41604117,41904127,62132004). This work was supported in part by the National Natural Science Foundation of China under Grants 41604117, 41904127, and 62132004, in part by the Hunan Provincial Natural Science Foundation of China under Grant 2024JJ5270, in part by the Open Project of Xiangjiang Laboratory under Grant 22XJ03011, in part by the Scientific Research Fund of Hunan Provincial Education Department under Grant 22B0663, and in part by the Changsha Natural Science Foundation under Grants kq2402098 and kq2402162
Revisiting the Income Inequality-Crime Puzzle
Get PDFSupplementary data: Supplementary material related to this article can be found online at https://doi.org/10.1016/j.worlddev.2023.106520 .The economics literature generally supports a positive theoretical link between income inequality and crime. However, despite this consensus, empirical evidence has struggled to yield definitive conclusions. To address this puzzle, I conducted a meta-analysis based on 1,341 estimates drawn from 43 studies in economics journals. The findings indicate a statistically significant but economically insignificant true effect of inequality on crime, ranging between 0.007 and 0.123 using UWLS FAT-PET and advanced methods. In essence, if there is an impact of inequality on crime, it is, at best, minimal. Additionally, there is some limited evidence suggesting positive publication bias. Results from Bayesian model averaging reveal that inequality does not affect exclusively property crime, as predicted by the rational choice models. Moreover, this analysis shows that inequality measures which are sensitive to changes in income at the middle and top of the distribution are associated with higher coefficients. The study also underscores the biases arising from the exclusion of relevant variables. The implications of this research suggest that inequality may not be the primary motivator for criminal behaviour, with other factors potentially playing more significant roles. Lastly, if inequality does affect crime, it might do so in different ways than those discussed by the majority of the existing empirical studies
Reliable uncertainties of tests and surveys – a data-driven approach
Get PDFMSC Classification 60J10, 91Exx, 91E45, 05A18.Supplementary material are available online at: https://www.metrology-journal.org/10.1051/ijmqe/2023018/olm . The article is accompanied by supplementary information that includes proofs to the theorems that are stated within the text of the article; linking our advanced methods to extant congeneric methods in the literature; comparison of the methods discussed herein, for partitioning a set of integers into 2 subsets and presentation of results on simulated data.Policy decisions are often motivated by results attained by a cohort of responders to a survey or a test. However, erroneous identification of the reliability or the complimentary uncertainty of the test/survey instrument, will distort the data that such policy decisions are based upon. Thus, robust learning of the uncertainty of such an instrument is sought. This uncertainty is parametrised by the departure from reproducibility of the data comprising responses to questions of this instrument, given the responders. Such departure is best modelled using the distance between the data on responses to questions that comprise the two similar subtests that the given test/survey can be split into. The paper presents three fast and robust ways for learning the optimal-subtests that a given test/survey instrument can be spilt into, to allow for reliable uncertainty of the given instrument, where the response to a question is either binary, or categorical − taking values at multiple levels − and the test/survey instrument is realistically heterogeneous in the correlation structure of the questions (or items); prone to measuring multiple traits; and built of small to a very large number of items. Our methods work in the presence of such messiness of real tests and surveys that typically violate applicability of conventional methods. We illustrate our new methods, by computing uncertainty of three real tests and surveys that are large to very-large in size, subsequent to learning the optimal subtests.There is no funding to be reported
Going DEEP - an evaluation of a social pedagogy informed approach to evidence enriched practice in social care
Get PDFAvailability of data and materials: The evaluation data is not publicly available as permission was not sought from respondents to archive their data in a repository. Public facing summaries of the case exemplars are available on the DEEP website: https://www.deepcymru.org/en/ .Social care workers benefit from multiple types of evidence to enhance citizen well-being, support their own well-being and improve social care services. Building capacity within social care to find, collect and use different forms of evidence is an international concern. The Developing Evidence Enriched Practice (DEEP) programme in Wales is informed by the values and aims of social pedagogy. It aspires to enhance both the generation and use of evidence in social care. To learn about what works in the programme, we conducted an evaluation based on contribution analysis that explored programme impacts between 2020 and 2023. Based on a co-produced theory of change the evaluation drew on exemplar cases, questionnaire responses, documentary evidence, process data and unsolicited feedback. There was evidence that the DEEP programme contributed to people better valuing and gaining a better understanding of different forms of evidence. Citizen voice could become more central in decision-making, and there were examples of practice, policy and research being informed by diverse evidence. Many people who attended the DEEP learning course enhanced their confidence and skills by using the DEEP approach and said that they would put their learning into practice. It was harder to evidence longer-term impacts and the sustainability of the approach. These findings suggest that there can be merit in developing capacity-building programmes informed by social pedagogy. Such programmes can be characterised as relational, holistic, practice-focused, multifaceted, contextualised and co-produced with intended beneficiaries.The Developing Evidence Enriched Practice programme 2020–3 was funded by the Welsh government through Health and Care Research Wales. The 2023–5 programme is funded by Social Care Wales
Remote Vision-Based Digital Patient Monitoring of Pulse and Respiratory rates in Acute Medical Wards
Get PDFData Sharing Statement: Data can be made available on reasonable request.Remote Vision-Based digital Patient Monitoring (VBPM) of pulse (PR) and respiratory rate (RR) was set up in six single rooms in an acute medical and an orthopaedic ward. We compared 102 PR and 154 RR VBPM measurements (from 27 patients) with paired routine nurse measurements. VBPM measurements of RR were validated by reviewing video footage. Nurse measurements of RR were often 16–18 breaths/minute, and did not match VBPM RR (overestimating at low RR and underestimating at high RR). Nurse measurements of pulse were on average 3.9 beats per minute greater than matched VBPM measurements. VBPM was unobtrusive and well accepted.Oxehealth Ltd
Green Human Resource Management, Green Supply Chain Management and Regulation and Legislation and its Effects on Sustainable Development Goals in Jordan
No full textData Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.A preprint version of this article is available at: https://www.preprints.org/manuscript/202312.1706/v1 - it has not been certified by peer review.In recent decades, sustainability and environmental concerns have become increasingly significant topics of discussion. This article aims to propose a conceptual framework of a research model including the correlations between government regulations and legislations, Green Human Resource Management (GHRM), Green Supply Chain Management (GSCM), and Sustainable Development Goals (SDGs). The methodological approach adopted in this study included conducting a review of the relevant literature and accessing databases and search engines to gather information. The current article presents a novel approach to understanding how organizations and regulators can collaborate to drive sustainable development in this domain. This study also adds significant value due to its unique contribution in connecting GHRM, GSCM, and government regulation and legislation, particularly in the context of sustainable development and its link to promoting decent work and economic growth (SDG8), responsible consumption and production (SDG12), and addressing climate action (SDG13). The rarity of articles addressing these interrelated topics, especially within the specific context of Jordan, where such research has been largely absent, underscores the distinctive nature of this study. Furthermore, this article stands out for its comprehensive incorporation of legal and regulatory aspects into the discourse on organizational GHRM and GSCM practices and their alignment with the pursuit of SDGs. By providing valuable insights for decision makers and organizations, including a thorough examination of the barriers involved, this article serves as an essential resource for understanding and navigating the complex interplay between environmental sustainability, GHRM, GSCM, and governmental regulations. Based on the analysis of the findings, a conceptual framework is proposed based on three environmental dimensions and six green practices that have discernible effects. Finally, it is envisaged that this study will offer directions for future research work to use another approach and another environment.This research received no external funding
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