3,170 research outputs found
An examination of the professional judgement and decision-making of strength and conditioning coaches
Athletic coaching is a complex profession, and coaches must perform a wide range of tasks in a variety of environments. In high-performance sporting environments, strength and conditioning coaches (SCCs) must fulfil a variety of roles that are growing in importance. Despite a recent broadening of the scope of SCC research beyond their knowledge, practical skills, experiences, and training preferences, a lack of attention continues to be paid to the professional judgement and decision-making (PJDM) of SCCs. First, in recognition of this lacuna in research, this thesis examined the thought processes of SCCs who possessed varying levels of experience and analysed the rationales that informed their approaches. Second, this thesis constructed and tested an intervention using the empirical findings of earlier investigations to enhance the PJDM of SCCs. This qualitative study employed a constructivist paradigm and was based on relativist ontology and interpretivist epistemology. The initial research used applied cognitive task analysis (ACTA) to examine the respective decision-making processes of participants who were engaged in training programme design and difficult common tasks. These studies, together with a focus group approach, used reflective thematic analysis (RTA) to engage with data sets and identify new patterns of meaning. The results indicated that the roles of SCCs require them to apply their theoretical knowledge and practical experiences to a wide range of tasks. An indication of the cognitive demands associated with these tasks were also generated as a consequence of the ACTA.
Furthermore, clear contrasts in the PJDM of high-level and early-career SCCs were discussed. The analysis of the focus group results was used to construct a revised model of thinking processes regarding training programme design. Crucially, this stage of the analysis identified the variables of context, collaboration, and communication as providing depth and breadth to the perceived impact of each proposed stage of the training programme design process. Considering the difficult situations that must be managed within strength and
conditioning (S&C) environments, the following three themes were identified as having the strongest impact on SCCs’ decision-making processes: situational awareness, improvisation, and metacognition. Both the ACTA and cognitive apprenticeship (CA) research enabled this study to make a unique contribution to the literature, as it provided empirical findings on the PJDM of SCCs with both high and low levels of experience. The application of a constructivist philosophy to the design and implementation of online S&C learning materials constitutes a novel contribution to existing traditional strategies for SCC preparation in the workplace. This CA study provides valuable preliminary findings that can be used by educators in the field to produce authentic, relevant materials for those aspiring to work in the S&C sector. Throughout this thesis, a case was developed that demonstrates the importance of experience for SCCs of all levels to be able to evaluate their thought processes and overall S&C coaching performance. Lastly, a platform for future research to build on was constructed
The Divided Self: Internal Conflict in Literature, Philosophy, Psychology, and Neuroscience
This thematic project examines the notion of self-division, particularly in terms of the conflict between cognition and metacognition, across the fields of philosophy, psychology, and, most recently, the cognitive and neurosciences. The project offers a historic overview of models of self-division, as well as analyses of the various problems presented in theoretical models to date. This work explores how self-division has been depicted in the literary works of Edgar Allan Poe, Don DeLillo, and Mary Shelley. It examines the ways in which artistic renderings alternately assimilate, resist, and/or critique dominant philosophical, psychological, and scientific discourses about the self and its divisions. This dissertation argues that the internal conflict portrayed by the writers of these literary characters is conscious: it is the conflict of the metacognitive “I” against akratic impulses, unwanted cognitions, and, ultimately, consciousness as a whole
Abstract Book of the II Congress of the Latin American Society for Vector Ecology
Recopilación de los resúmenes de las conferencias, simposios, paneles de discusión y "turbo talks" ofrecidos en el II Congreso de la Sociedad Latinoamericana de Ecología de Vectores (LA SOVE), realizado entre el 29 de octubre y el 3 de noviembre de 2022 en la ciudad de La Plata (Buenos Aires, Argentina).Sociedad Latinoamericana de Ecología de Vectores (LA SOVE
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Improving patient safety by learning from near misses – insights from safety-critical industries
Background
Patients are at risk of being harmed by the very processes meant to help them. To improve patient safety, healthcare organisations attempt to identify the factors that contribute to incidents and take action to optimise conditions to minimise repeats. However, improvements in patient safety have not matched those observed in other safety-critical industries.
One difference between healthcare and other safety-critical industries may be how they learn from near misses when seeking to make safety improvements. Near misses are incidents that almost happened, but for an interruption in the sequence of events. Management of near misses includes their identification, reporting and investigation, and the learning that results. Safety theory suggests that acting on near misses will lead to actions to help prevent incidents. However, evidence also suggests that healthcare has yet to embrace the learning potential that patient safety near misses offer.
The aims of this research, in support of this thesis, were to explore how best healthcare can learn from patient safety near misses to improve patient safety, and to identify what guidance non-healthcare safety-critical industries, which have implemented effective near-miss management systems, can offer healthcare. As this research progressed the aims were updated to include consideration of whether healthcare should seek to learn from patient safety near misses.
Methods
This research took a mixed-methods approach augmented by scoping reviews of the healthcare (study 1) and non-healthcare safety-critical industry (study 3) literature. A qualitative case study (study 2) was undertaken to explore the management of patient safety near misses in the English National Health Service. Seventeen interviews were undertaken with patient safety leads across acute hospitals, ambulance trusts, mental health trusts, primary care, and national bodies. A questionnaire was also used to help access the views of frontline staff.
A grounded theory (study 4) was used to develop a set of principles, based on learning from non-healthcare safety-critical industries, around how best near misses can be managed. Thirty-five interviews were undertaken across aviation, maritime, and rail, with nuclear later added as per the theoretical sampling.
Results
The scoping reviews contributed 125 healthcare and 108 non-healthcare safety-critical industry academic articles, published internationally between 2000 and 2022, to the evidence gained from the qualitative case study and grounded theory. Safety cultures and maturity with safety management processes were found to vary in and across the different industries, and there was a reluctance for healthcare to learn about safety and near misses from other industries.
Healthcare has yet to establish effective processes to manage patient safety near misses. There is an absence of evidence that learning has led to improvements in patient safety. The definition of a patient safety near miss varies, and organisations focus their efforts on reporting and investigating incidents, with limited attention to patient safety near misses. In non-healthcare safety-critical industries, near-miss management is more established, but process maturity varies in and across industries. Near misses are often defined specifically for an industry, but there is limited evidence that learning from them has improved safety. Information about near misses are commonly aggregated and may contribute to company and industry safety management systems.
Exploration of the definition of a patient safety near miss led to the identification of the features of a near miss. The features have not been previously defined in the manner presented in this thesis. A patient safety near miss is context-specific and complex, involves interruptions, highlights system vulnerabilities, and is delineated from an incident by whether events reach a patient.
Across healthcare and non-healthcare safety-critical industries the impact of learning from near misses is often assumed or extrapolated based on the common cause hypothesis. The hypothesis is regularly cited in safety literature and is used as the basis for justifying a focus on patient safety near misses. However, the validity of the hypothesis has been questioned and has not been validated for different patient safety near miss and incident types.
Conclusions
The research findings challenge long-held beliefs that learning from patient safety near misses will lead to improvements in patient safety. These beliefs are based on traditional safety theory that is unlikely to now be valid in the complexity of modern-day systems where incidents are the result of multiple factors and can emerge without apparent warning. Further research is required to understand the relationship between learning from patient safety near misses and patient safety, and whether the common cause hypothesis is valid for different types of healthcare safety event.
While there are questions about the value of learning directly from patient safety near misses, the contribution of near misses to safety management systems in non-healthcare safety-critical industries looks to be beneficial for safety improvement. Safety management systems have yet to be implemented in the National Health Service and future research should look to understand how best this may be achieved and their value. In the meantime, patient safety near misses may help healthcare’s understanding of systems and their optimisation to create barriers to incidents and build resilience. This research offers an evidence-based definition of a patient safety near miss and describes principles to support identification, reporting, prioritisation, investigation, aggregation, learning, and action to help improve patient safety
Computational Approaches to Drug Profiling and Drug-Protein Interactions
Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a
long period of stagnation in drug approvals. Due to the extreme costs associated with
introducing a drug to the market, locating and understanding the reasons for clinical failure
is key to future productivity. As part of this PhD, three main contributions were made in
this respect. First, the web platform, LigNFam enables users to interactively explore
similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly,
two deep-learning-based binding site comparison tools were developed, competing with
the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the
open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold
relationships and has already been used in multiple projects, including integration into a
virtual screening pipeline to increase the tractability of ultra-large screening experiments.
Together, and with existing tools, the contributions made will aid in the understanding of
drug-protein relationships, particularly in the fields of off-target prediction and drug
repurposing, helping to design better drugs faster
A Multi-level Analysis on Implementation of Low-Cost IVF in Sub-Saharan Africa: A Case Study of Uganda.
Introduction: Globally, infertility is a major reproductive disease that affects an estimated 186 million people worldwide. In Sub-Saharan Africa, the burden of infertility is considerably high, affecting one in every four couples of reproductive age. Furthermore, infertility in this context has severe psychosocial, emotional, economic and health consequences. Absence of affordable fertility services in Sub-Saharan Africa has been justified by overpopulation and limited resources, resulting in inequitable access to infertility treatment compared to developed countries. Therefore, low-cost IVF (LCIVF) initiatives have been developed to simplify IVF-related treatment, reduce costs, and improve access to treatment for individuals in low-resource contexts. However, there is a gap between the development of LCIVF initiatives and their implementation in Sub-Saharan Africa. Uganda is the first country in East and Central Africa to undergo implementation of LCIVF initiatives within its public health system at Mulago Women’s Hospital.
Methods: This was an exploratory, qualitative, single, case study conducted at Mulago Women’s Hospital in Kampala, Uganda. The objective of this study was to explore how LCIVF initiatives have been implemented within the public health system of Uganda at the macro-, meso- and micro-level. Primary qualitative data was collected using semi-structured interviews, hospital observations informal conversations, and document review. Using purposive and snowball sampling, a total of twenty-three key informants were interviewed including government officials, clinicians (doctors, nurses, technicians), hospital management, implementers, patient advocacy representatives, private sector practitioners, international organizational representatives, educational institution, and professional medical associations. Sources of secondary data included government and non-government reports, hospital records, organizational briefs, and press outputs. Using a multi-level data analysis approach, this study undertook a hybrid inductive/deductive thematic analysis, with the deductive analysis guided by the Consolidated Framework for Implementation Research (CFIR).
Findings: Factors facilitating implementation included international recognition of infertility as a reproductive disease, strong political advocacy and oversight, patient needs & advocacy, government funding, inter-organizational collaboration, tension to change, competition in the private sector, intervention adaptability & trialability, relative priority, motivation &advocacy of fertility providers and specialist training. While barriers included scarcity of embryologists, intervention complexity, insufficient knowledge, evidence strength & quality of intervention, inadequate leadership engagement & hospital autonomy, poor public knowledge, limited engagement with traditional, cultural, and religious leaders, lack of salary incentives and concerns of revenue loss associated with low-cost options.
Research contributions: This study contributes to knowledge of factors salient to implementation of LCIVF initiatives in a Sub-Saharan context. Effective implementation of these initiatives requires (1) sustained political support and favourable policy & legislation, (2) public sensitization and engagement of traditional, cultural, and religious leaders (3) strengthening local innovation and capacity building of fertility health workers, in particular embryologists (4) sustained implementor leadership engagement and inter-organizational collaboration and (5) proven clinical evidence and utilization of LCIVF initiatives in innovator countries. It also adds to the literature on the applicability of the CFIR framework in explaining factors that influence successful implementation in developing countries and offer opportunities for comparisons across studies
Runway Safety Improvements Through a Data Driven Approach for Risk Flight Prediction and Simulation
Runway overrun is one of the most frequently occurring flight accident types threatening the safety of aviation. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors.
To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach.
A series of experiments are performed to observe how the model accuracy change with different data pre-processing methods for the prediction model and different neural network models for the simulation model. The data pre-processing methods include indexing the data by different frequencies, by different window sizes, and data clustering. The neural network models include simple Recurrent Neural Networks, Gated Recurrent Units, Long Short Term Memory, and Neural Network Autoregressive with Exogenous Input. Another series of experiments are performed to evaluate the robustness of these models to adverse wind and flare. This is because different wind conditions and flares represent controls that the models need to map to the predicted flight states. The most robust models are then used to identify significant features for the prediction model and the feasible control space for the simulation model. The outcomes of the most robust models are also mapped to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, and high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk.
The main contribution of this work is on evaluating the accuracy and robustness of prediction and simulation models trained using Flight Operational Quality Assurance (FOQA) data. Unlike many studies that focused on optimizing the model structures to create the two models, this work optimized both data and model structures to ensure that the data well capture the dynamics of the aircraft it represents. To achieve this, this work introduced a hybrid genetic algorithm that combines the benefits of conventional and quantum-inspired genetic algorithms to quickly converge to an optimal configuration while exploring the design space. With the optimized model, this work identified the data features, from the final approach, with a higher contribution to predicting airspeed, vertical speed, and pitch angle near touchdown. The top contributing features are altitude, angle of attack, core rpm, and air speeds. For both the prediction and the simulation models, this study goes through the impact of various data preprocessing methods on the accuracy of the two models. The results may help future studies identify the right data preprocessing methods for their work. Another contribution from this work is on evaluating how flight control and wind affect both the prediction and the simulation models. This is achieved by mapping the model accuracy at various levels of control surface deflection, wind speeds, and wind direction change. The results saw fairly consistent prediction and simulation accuracy at different levels of control surface deflection and wind conditions. This showed that the neural network-based models are effective in creating robust prediction and simulation models of aircraft during the final approach. The results also showed that data frequency has a significant impact on the prediction and simulation accuracy so it is important to have sufficient data to train the models in the condition that the models will be used. The final contribution of this work is on demonstrating how the prediction and the simulation models can be used to increase awareness of runway overrun.Ph.D
The effect of uterine septum resection on fertility and live birth rates
Objectives: To determine if patients who undergo a hysteroscopic uterine septum
resection have higher live birth rates than patients with a normal hysteroscopy and
unexplained infertility.
Study Methods: Using surgical billing records from Newfoundland and Labrador
Fertility Services, a cohort of patients undergoing hysteroscopic uterine septum resection
from October 2003 to June 2011 were identified. The study patients were matched with
the next four patients from Newfoundland and Labrador Fertility Services undergoing a
diagnostic hysteroscopy who had otherwise unexplained infertility. The patients were
followed from surgery for at least one year to determine if they had a pregnancy and the
outcome of that pregnancy. Both groups included patients with primary infertility,
secondary infertility, and recurrent pregnancy loss. The primary outcome was live birth
rate, with a p value <0.05 defining statistical significance. Secondary outcomes included
pregnancy rate, preterm birth rate, and markers of obstetric and neonatal morbidity; with
p value <0.01 defining statistical significance.
Results: A total of 50 eligible patients underwent hysteroscopic uterine septum resection
(SR) during the specified timeline and were matched with 189 patients who had a
diagnostic hysteroscopy (DH) for unexplained infertility. The groups were similar in age,
BMI, years trying to conceive and surgeon. Univariate analysis demonstrated a
significant difference in live birth rates between the groups (52.0% (SR) compared with
33.3% (DH), RR 1.56 [1.12-2.18], p=0.015). Subgroup analysis demonstrated a
significant different in live birth rates between the secondary infertility group (66.7%
(SR) compared with 32.4% (DH), RR 2.06 [1.22-3.47], p=0.023). Logistic regression
analysis also found a higher live birth rate in the SR group (OR 2.35 [1.17-4.74],
p=0.016). Postoperative pregnancy rates were higher in the SR group (72.0% compared
with 41.2%, RR 1.74 (1.38-2.21), p<0.001), and this finding was confirmed by logistic
regression (OR 3.78 [1.80-7.93], p<0.001). The SR group had a higher proportion of
patients with risk factors for preterm delivery (29.6% compared with 10.8%, p=0.035)
and composite neonatal morbidity (11.5% compared with 6.5%, p=0.029); but these
differences did not meet statistical significance for secondary outcomes. Of live births,
there was no significant difference in rate of preterm birth between the two groups
(10.8% compared with 6.1%, p=0.37) or gestational age at delivery (268 days compared
with 274 days, p=0.10).
Conclusions: Hysteroscopic uterine septum resection may result in higher pregnancy
rates and live births in patients with infertility, compared to patients undergoing a
diagnostic hysteroscopy for unexplained infertility
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