Five fathers' experience of an adult son sustaining a cervical spinal cord injury: an Interpretative Phenomenological Analysis

The paper presents an in-depth idiographic study exploring the experience of fathers who have an adult son with a cervical spinal cord injury (SCI). Five participants were recruited and individual semi-structured interviews were conducted. The interviews were transcribed verbatim and analysed using Interpretative Phenomenological Analysis (IPA). Two super-ordinate themes are presented highlighting. Firstly, the ongoing negative impact of their sons’ injury on the participants’ role as fathers’. This comprises the negative impact on emotions with guilt common for failing in their perceived role as a father. The dissonance experienced between wanting to help encourage their sons’ independence. Concern experienced due to their sons altered life trajectory and anxiety because they won’t be alive to protect their son in the future. Secondly, how participants cope and adjust to their son’s SCI are presented. Comprising of how positive thinking, such as focusing on their son surviving the trauma; and the influence of seeing their son cope well affects how participants cope. Also, reflecting on how the injury has changed their life helps participants, to an extent, make sense of the trauma. The results are discussed in relation to the relevant extant literature to give a unique perspective about how SCI impacts their perceived role as fathers and the struggle to cope and adjust to the trauma. Future research investigating the impact of SCI on the family is warranted to better understand the wider implications

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013

A theoretical framework and research agenda for studying team attributions in sport

The attributions made for group outcomes have attracted a great deal of interest in recent years. In this article we bring together much of the current research on attribution theory in sport and outline a new conceptual framework and research agenda for investigating the attributions of team members. The proposed framework draws on multiple conceptual approaches including models of attribution, group dynamics and stress responses to provide a detailed hypothetical description of athletes' physiological, cognitive and affective responses to group competition. In describing this model we outline important antecedents of team attributions before hypothesising how attributions can impact hormonal and cardiovascular responses of athletes, together with cognitive (goals, choices, expectations), affective (self-esteem, emotions), and behavioural (approach-avoidance actions) responses of groups and group members. We conclude by outlining important methodological considerations and implications for structured context specific attribution-based interventions

Predicting no-show medical appointments using machine learning

Health care centers face many issues due to the limited availability of resources, such as funds, equipment, beds, physicians, and nurses. Appointment absences lead to a waste of hospital resources as well as endangering patient health. This fact makes unattended medi- cal appointments both socially expensive and economically costly. This research aimed to build a predictive model to identify whether an appointment would be a no-show or not in order to reduce its consequences. This paper proposes a multi-stage framework to build an accurate predictor that also tackles the imbalanced property that the data exhibits. The first stage includes dimensionality reduction to compress the data into its most important components. The second stage deals with the imbalanced nature of the data. Different machine learning algorithms were used to build the classifiers in the third stage. Various evaluation metrics are also discussed and an evaluation scheme that fits the problem at hand is described. The work presented in this paper will help decision makers at health care centers to implement effective strategies to reduce the number of no-shows

Rehabilitation needs for older adults with stroke living at home: perceptions of four populations

<p>Abstract</p> <p>Background</p> <p>Many people who have suffered a stroke require rehabilitation to help them resume their previous activities and roles in their own environment, but only some of them receive inpatient or even outpatient rehabilitation services. Partial and unmet rehabilitation needs may ultimately lead to a loss of functional autonomy, which increases utilization of health services, number of hospitalizations and early institutionalization, leading to a significant psychological and financial burden on the patients, their families and the health care system. The aim of this study was to explore partially met and unmet rehabilitation needs of older adults who had suffered a stroke and who live in the community. The emphasis was put on needs that act as obstacles to social participation in terms of personal factors, environmental factors and life habits, from the point of view of four target populations.</p> <p>Methods</p> <p>Using the focus group technique, we met four types of experts living in three geographic areas of the province of Québec (Canada): older people with stroke, caregivers, health professionals and health care managers, for a total of 12 groups and 72 participants. The audio recordings of the meetings were transcribed and NVivo software was used to manage the data. The process of reducing, categorizing and analyzing the data was conducted using themes from the Disability Creation Process model.</p> <p>Results</p> <p>Rehabilitation needs persist for nine capabilities (e.g. related to behaviour or motor activities), nine factors related to the environment (e.g. type of teaching, adaptation and rehabilitation) and 11 life habits (e.g. nutrition, interpersonal relationships). The caregivers and health professionals identified more unmet needs and insisted on an individualized rehabilitation. Older people with stroke and the health care managers had a more global view of rehabilitation needs and emphasized the availability of resources.</p> <p>Conclusion</p> <p>Better knowledge of partially met or unmet rehabilitation needs expressed by the different types of people involved should lead to increased attention being paid to education for caregivers, orientation of caregivers towards resources in the community, and follow-up of patients' needs in terms of adjustment and rehabilitation, whether for improving their skills or for carrying out their activities of daily living.</p

Patient grouping optimization using a hybrid self-organizing map and Gaussian mixture model for length of stay-based clustering system

Clustering is a major tool in data analysis, dividing objects into different groups, based on unsupervised training procedures. Clustering algorithms attempt to group a set of objects into well-defined subgroups, based on some similarity between them. The results of the clustering process may not be confirmed by our knowledge of the data. The self-organizing map (SOM) neural network is an excellent tool in recognizing clusters of data, relating similar classes to each other in an unsupervised manner. Basically, SOM is used when the training dataset contains cases featuring input variables without the associated outputs. SOM can also be used for classification when output classes are immediately available; the advantage in this case is its ability to highlight similarities between classes, thus assessing different previous classification approaches. This paper explores the above ability of SOM to validate length of stay-based (LOS) clustering results that obtained using Gaussian mixture modeling (GMM) approach, by comparing the classification accuracy (percentage of samples correctly classified) of different results. The idea behind this attempt is the following: in the first step, each GMM approach provides its own scheme of grouping LOS, and different classes are thus recognized and labeled. In this step, we have considered GMM with different LOS intervals. In the second step, SOM will first learn to recognize clusters of data and, secondly, will compare its clusters map with the previous labeled clusters provided by GMM. To conclude, a closer similarity between previous clustering schemes and SOM clusters map, will results in a better accuracy for clustering LOS data. Ultimately, by comparing different GMM component models, the SOM application will lead to an optimal number of patient groups. An application to a surgical dataset showed the effectiveness of this methodology in determining the LOS intervals

Sonographic Evaluation of the Mechanism of Active Labor (SonoLabor Study): Observational study protocol regarding the implementation of the sonopartogram

Introduction Over the last decades, a large body of literature has shown that intrapartum clinical digital pelvic estimations of fetal head position, station and progression in the pelvic canal are less accurate, compared with ultrasound (US) scan. Given the increasing evidence regarding the advantages of using US to evaluate the mechanism of labour, our study protocol aims to develop sonopartograms for fetal cephalic presentations. They will allow for a more objective evaluation of labour progression than the traditional labour monitoring, which could enable more rapid decisions regarding the mode of delivery. Methods/analysis This is a prospective observational study performed in three university hospitals, with an unselected population of women admitted in labour at term. Both clinical and US evaluations will be performed assessing fetal head position, descent and rotation. Specific US parameters regarding fetal head position, progression and rotation will be recorded to develop nomograms in a similar way that partograms were developed. The primary outcome is to develop nomograms for the longitudinal US assessment of labour in unselected nulliparous and multiparous women with fetal cephalic presentation. The secondary aims are to assess the sonopartogram differences in occiput anterior and posterior deliveries, to compare the labour trend from our research with the classic and other recent partogram models and to investigate the capability of the US labour monitoring to predict the outcome of spontaneous vaginal delivery. Ethics and dissemination All protocols and the informed consent form comply with the Ministry of Health and the professional society ethics guidelines. University ethics committees approved the study protocol. The trial results will be published in peer-reviewed journals and at the conference presentations. The study will be implemented and reported in line with the Strengthening the Reporting of Observational Studies in Epidemiology statement. © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ