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The Hummingbird Project year 2: decreasing distress and fostering flourishing in a pragmatic pre–post study
Full text linkMulti-component Positive Psychology Interventions (mPPIs) in secondary schools have been shown to improve mental health outcomes for young people. The Hummingbird Project mPPI is a six-week program of workshops designed to introduce a variety of positive psychology (PP) concepts to secondary school-aged children in schools to improve well-being, resilience, and hope. The effects on mental distress, however, were not explored. The current study, therefore, was designed to replicate the effects of the Hummingbird Project mPPI on positive mental health and to also explore the effects on symptoms of mental distress. Secondary school-aged children (N = 614; mean age = 11.46 years) from a sample of secondary schools located across the North West of England (N = 7) participated in the study; the majority of children were in Year 7 (94%). The PP concepts explored included happiness, hope, resilience, mindfulness, character strengths, growth mindset, and gratitude. The results showed significant improvements associated with the mPPI in well-being (as measured by the World Health Organization Well-Being Index; WHO-5), hope (as measured by the Children’s Hope Scale; CHS), and symptoms of mental distress (as measured by the Young Person’s Clinical Outcomes in Routine Evaluation; YP-CORE) from pre- to post-intervention. While acknowledging the limits due to pragmatic concerns regarding the implementation of a control group, the effectiveness of the Hummingbird Project mPPI on well-being was replicated alongside reducing the symptoms of mental distress. Future evaluation, however, will need to implement more robust designs and consider follow-up duration to assess the longer-term effects of the Hummingbird Project mPPI
Changes in the prevalence of perceived discrimination and associations with probable mental health problems in the UK from 2015 to 2020: A repeated cross-sectional analysis of the UK Household Longitudinal Study
Full text linkBackground Significant social and political changes occurred in the UK between
2015-2020. Few studies have examined population level trends in experiencing
discrimination and mental health problems during this period.
Aims To determine prevalence trends in perceived discrimination and probable
mental health problems amongst UK adults during 2015-2020.
Method Repeated cross-sectional data from the UK Household Longitudinal Study
was used to estimate nationally representative trends in perceived discrimination and
probable mental health problems (GHQ-12; 4+ threshold) among adults between
2015/2016-2019/2020 (25,756 observations). Weighted logistic regression models
with post-estimation margins commands determined changes between survey waves
controlling for sociodemographic characteristics. Mediation models explored whether
changes in perceived discrimination prevalence trends explained trends in probable
mental health problems.
Results From 2015/2016 to 2019/2020 perceived discrimination and probable
mental health problems increased significantly by 6·1% (95% CI: 3·4-8·8, p <·001)
and 4·5% (95% CI: 1·3-7·7, p =·006), respectively. These changes did not tend to
reliably differ by sociodemographic grouping. Increased prevalence of probable
mental health problems from 2015/2016 to 2019/2020 was partially explained by
(15·2% of association mediated) the increase in perceived discrimination observed
during the same time period.
Conclusions Amongst UK adults, the prevalence of perceived discrimination and
probable mental health problems increased between 2015/2016 to 2019/2020.
Increases in perceived discrimination partially explained increases in probable mental health problems. National measures designed to reduce both discrimination and mental health problems have potential to make substantial improvements to
public health and should be prioritised in the UK
An exploration of student perception toward interprofessional high-fidelity clinical simulation
Full text linkOBJECTIVES: Interprofessional education is recognized for its potential for collaboration and teamwork, reflecting clinical practice; however,
existing literature for simulation-based interprofessional education does not include Physician Associate (PA) students. This initiative aimed to
explore the students’ perception of interprofessional clinical simulation for PA students and allied health professional (AHP) students as part
of our program development.
METHODS: A high-fidelity simulation session was designed and conducted for volunteering students from the PA, paramedic science, and
physiotherapy courses. We used a mixed-method electronic questionnaire consisting of 15 statements rated on a numerical rating scale (0-5)
and four open-ended questions with unlimited free-text responses to explore student perceptions. Inductive thematic analysis was used for qualitative analysis. The session design was underpinned by Allport’s (intergroup) contact hypothesis with an emphasis on mutual intergroup differentiation.
RESULTS: Forty-six students participated in the simulation teaching, with 48% (n=22) providing feedback. Overall student perception was
mainly positive toward the interprofessional simulation; however, some barriers to learning were recognized. Based on the evaluation of our initiative and existing literature, we propose 5 top tips to promote an effective learning experience for students. (1) Understand the importance of
interprofessional collaboration. (2) Establish clear roles. (3) Plan the scenarios in advance. (4) Maintain equal status between groups. (5) Provide
clear instructions and expectations.
CONCLUSION: To our knowledge, this is the first study of high-fidelity interprofessional simulation involving PA and AHP students. We successfully explored student perception which highlighted aspects that can impact learning. This pilot study demonstrated that interprofessional simulation is a feasible and acceptable form of learning for our students and highlighted how to improve future interprofessional simulation teaching
sessions
Secure supervised learning-based smart home authentication framework
No full textSmart home possesses the capability of facilitating home services to its users with the systematic advance in
Internet of Things (IoT) and information and communication technologies (ICT) in the recent decades. The home
service offered by the smart devices helps the users in utilizing maximized level of comfort for the objective of
improving life quality. As the user and smart devices communicate through an insecure channel, the smart home
environment is prone to security and privacy problems. A secure authentication protocol needs to be established
between the smart devices and user, such that situation for device authentication can be made feasible in smart
home environments. Most of the existing smart home authentication protocols was identified to fail in facilitating
secure mutual authentication and increases the possibility of lunching the attacks of session key disclosure,
impersonation and stolen smart device. In this paper, Secure Supervised Learning-based Smart Home
Authentication Framework (SSL-SHAF) is proposed as the reliable mutual authentication that can be contextually
imposed for better security. The formal analysis of the proposed SSL-SHAF confirmed better resistance against
session key disclosure, impersonation and stolen smart device attack. The results of SSL-SHAF confirmed
minimized computational costs and security compared to the baseline protocols considered for investigation
Leading in an entrepreneurial context — present and future perspectives
No full textThis article introduces the special issue on entrepreneurial leadership, comprising six papers. Through
this special issue, our principal aim as guest editors is to promote the progress of this field by
deepening our knowledge of leading in an entrepreneurial context. In doing so, we invited submissions
that will enhance our present and future perspectives on leading in an entrepreneurial context. We
welcomed ‘research for’ rather than ‘research about’ entrepreneurial leadership. We focused
especially on empirical research on particular topics within the broad area of entrepreneurial
leadership. Yet we also sought studies that promote the value of entrepreneurial leadership in
education programmes. The resulting collection of six articles gives the readers wide exposure to
different thoughts and brings together a multidisciplinary perspective on the intersection between
entrepreneurship and leadership
Multivariate time series forecasting of municipal solid waste
Full text linkMunicipal solid waste is a major concern these
days. With industrialization and urbanization, the amount of
solid waste being generated has significantly increased over the
years. This waste is mostly disposed to landfills and only a
proportion of it is recycled. The waste on landfills has
detrimental effects on health. Hence, it is important for the
government to take appropriate measures by having enough
recycling sites for regular treatment of solid waste. However,
setting up of recycling units requires investment. Therefore, it
is important to forecast waste that will be generated in coming
years and at the same time predict the amount of waste that
will be recycled. If the current recycling units are inadequate
to handle the future quantities of waste generation, then
probably more such units can be set up. Thus, this requires
better forecasting of solid waste that will be generated and
recycled. This study uses time series data to forecast waste
generation and recycling. The study uses statistical models
ARIMAX and machine learning models such as random forest,
support vector regression, XGBoost for forecasting. The study
also builds hybrid models by combining these models to find a
model that provides higher accuracy, low error rate and thus
better prediction
Evaluation of venous thromboembolism risk assessment models for hospital inpatients: the VTEAM evidence synthesis
Full text linkBackground: Pharmacological prophylaxis during hospital admission can reduce the risk of acquired
blood clots (venous thromboembolism) but may cause complications, such as bleeding. Using a risk
assessment model to predict the risk of blood clots could facilitate selection of patients for prophylaxis
and optimise the balance of benefits, risks and costs.
Objectives: We aimed to identify validated risk assessment models and estimate their prognostic
accuracy, evaluate the cost-effectiveness of different strategies for selecting hospitalised patients for
prophylaxis, assess the feasibility of using efficient research methods and estimate key parameters for
future research.
Design: We undertook a systematic review, decision-analytic modelling and observational cohort study
conducted in accordance with Enhancing the QUAlity and Transparency Of health Research (EQUATOR)
guidelines.
Setting: NHS hospitals, with primary data collection at four sites.
Participants: Medical and surgical hospital inpatients, excluding paediatric, critical care and pregnancy related admissions.
Interventions: Prophylaxis for all patients, none and according to selected risk assessment models.
Main outcome measures: Model accuracy for predicting blood clots, lifetime costs and quality-adjusted
life-years associated with alternative strategies, accuracy of efficient methods for identifying key
outcomes and proportion of inpatients recommended prophylaxis using different models
Entrepreneurship education and internationalisation : Cases, collaborations and contexts
No full textEmpirical evaluation of deep learning approaches for predicting cervical cancer in the health care sector
No full textThis research paper addresses the urgent need
to combat the escalating mortality rates in cervical cancer,
impacting 570,000 women, with 311,000 fatalities, as reported
by the World Health Organization. Recognizing the potential
of digital solutions, we explore deep learning's untapped power
for early diagnosis. Amidst healthcare challenges due to
population growth and disease spread, traditional methods
prove inadequate. To bridge this gap, we introduce novel
techniques: Long Short-term Memory Networks and
Bidirectional Long Short-term Memory Networks. Leveraging
a comprehensive dataset of 15 attributes, including age,
pregnancies, partners, smoking, cytology, and biopsy, our
model achieves a noteworthy 97% accuracy, signifying a
ground-breaking advancement in cervical cancer management
Random Forest approach to predict emergency patients’ hospitalization duration for the health sector
No full textThe ability to accurately predict the
hospitalization duration of an accident and emergency (A&E)
patient's hospital stay is essential for improving patient care,
managing healthcare resources, and overall hospital efficiency.
This study examined several machine learning prediction
models, such as the Decision Tree regression model, Support
Vector Regression, and Random Forest regression model, to
predict hospitalization for A&E patients. The Random Forest
Regression model stands out as the best-fit model and a reliable
predictor of hospitalization duration among these models, with
a Mean squared error (MSE) value of 11.6079. In this
quantitative research-based study, we used a secondary dataset,
the MIMIC-III dataset, which has an abundance of patient and
medical information and parameters that helped to ensure the
authenticity and applicability of the generated predictive
models. The results of this study will improve healthcare
management by providing an accurate and efficient approach
for predicting the length of an A&E patient's hospital stay