Kay I Penny

Online video in clinical skills education of oral medication administration for undergraduate student nurses: A mixed methods, prospective cohort study.

BACKGROUND Improvements in the safety of the prescribing, dispensing and administration of medicines are identified as a priority across international healthcare systems. It is therefore essential that higher education institutions play their part in helping to meet this patient safety objective. New developments in clinical skills education which are aligned to emerging educational theory are available, but evaluations and supportive evidence are limited. OBJECTIVES To evaluate the use of an online best practice exemplar as an adjunct to the clinical skills teaching of oral medication administration to undergraduate student nurses. DESIGN Mixed-methods prospective cohort design. SETTINGS AND PARTICIPANTS Two intakes of undergraduate nursing students (n=168, n=154) undertaking a first year clinical skills based module at a British university. METHODS The Control group received standard teaching using lectures and skills classes facilitated by experienced clinical skills lecturers. The Intervention group received the standard teaching and unlimited access to an online video clip of medication administration. Performance and satisfaction were measured using module assessment results and a satisfaction questionnaire. Qualitative data were gathered using focus groups (n=16, n=20). RESULTS The Intervention group was significantly (p=0.021) more likely to pass the assessment and rate their satisfaction with the teaching significantly higher (p<0.05) on more than half of the items from the Student Satisfaction Survey. Two Categories were identified from focus group data; Classroom Learning and Transfer to Practice. Classroom Learning included four themes of Peers, Self, Teaching and Time and when Classroom Learning was positive, the Transfer to Practice of the clinical skill was enhanced. CONCLUSIONS An online video of a best practice exemplar as an adjunct to taught clinical skills sessions improves student assessment results and satisfaction ratings. The video was also reported to positively influence all themes identified in Classroom Learning and was perceived to promote the Transfer to Practice of teaching input.

Approaches for dealing with missing data in health care studies

AIM The aims of this study were to highlight the problems associated with missing data in healthcare research and to demonstrate the use of several techniques for dealing with missing values, through the use of an illustrative example. BACKGROUND In healthcare research studies, it is almost impossible to avoid at least some missing values during data collection, which in turn can threaten the validity of the study conclusions. A range of methods for reducing the impact of missing data on the validity of study findings have been developed, depending on the nature and patterns which the missing values may take. DESIGN A discursive study. METHODS Several techniques designed to deal with missing data are described and applied to an illustrative example. These methods include complete-case analysis, available-case analysis, as well as single and multiple imputation. CONCLUSIONS If research data contain missing values that are not randomly distributed, then the study results are likely to be biased unless an effective approach to dealing with the missing values is implemented. RELEVANCE TO CLINICAL PRACTICE If nursing and healthcare practice is to be informed by research findings, then these findings must be reliable and valid. Researchers should report the details of missing data, and appropriate methods for dealing with missing values should be incorporated into the data analysis.

The Level of Expressed Need--a measure of help-seeking behaviour for chronic pain in the community.

Chronic pain is a common and disabling condition, with a high impact on health and the health services in the community. The extent of help‐seeking behaviour and factors that influence this are complex, but poorly understood. A simple, valid measure of help‐seeking behaviour would be useful for community‐based research, with a view to developing and evaluating interventions.

Record linkage in Scotland and its applications to health research

AIMS AND OBJECTIVES   This paper will focus on the key concepts behind record linkage and describe how probability matching of Scottish health records can be used for national health research. BACKGROUND   Record linkage can bring together two or more records relating to the same individual. This allows information from multiple sources to be joined together to produce richer data sets for research purposes and has wide applicability in public health and epidemiological research. The probability matching techniques underpinning record linkage bring together records on a patient basis using key identifying information on each record. Scotland has a strong track record for performing linkage for research purposes owing to routinely collected and well-maintained national administrative health data sets, the emergence of the Scottish record linkage system and organisations like the Information Services Division of NHS National Services Scotland who centrally hold permanently linked patient-based databases. Design.  A record linkage retrospective population cohort study is described within this paper. METHODS   The paper will describe current linkage methodology before discussing typical applications in the setting of Information Services Division and focusing on a particular linkage study investigating rates and risk factors for gastroschisis. RESULTS   Conclusions from the gastroschisis study are typical of the types of important findings drawn from analysing linked health data. CONCLUSIONS   Scotlands good track record for linking records for health research is evidenced by the high volume of research projects, publications and findings resulting from probability matching of national health data. Relevance to clinical practice.  Record linkage allows information relating to the same person held across different data sources to be brought together. Probabilistic record linkage can overcome data quality issues, producing accurate matches. This allows linked, analysable, patient-based databases, capable of answering complex research questions, to be produced from several data sources with wide applications in the field of health research.

The use of data-mining to identify indicators of health-related quality of life in patients with irritable bowel syndrome

AIM To examine the health-related quality of life in a cohort of individuals with irritable bowel syndrome and to explore the use of several data-mining methods to identify which socio-demographic and irritable bowel syndrome symptoms are most highly associated with impaired health-related quality of life. BACKGROUND Health-related quality of life can be adversely affected by irritable bowel syndrome. Little is presently known about the predictive factors that may influence the quality of life in these patients. DESIGN Cross-sectional survey design involving the general population of the UK. Methods.  Individuals with symptoms of irritable bowel syndrome were recruited to a longitudinal cohort survey via a UK-wide newspaper advert. Health-related quality of life was measured using a battery of validated questionnaires. Several data-mining models to determine which factors are associated with impaired health-related quality of life are considered in this study and include logistic regression, a classification tree and artificial neural networks. RESULTS As well as irritable bowel syndrome symptom severity, results indicate that psychological morbidity and socio-demographic factors such as marital status and employment status also have a major influence on health-related quality of life in irritable bowel syndrome. CONCLUSION Health-related quality of life is impaired in community-based individuals in the UK with irritable bowel syndrome. Although not always as easily interpreted as logistic regression, data-mining techniques indicate subsets of factors that are highly associated with impaired quality of life. These models tend to include subsets of irritable bowel syndrome symptoms and psychosocial factors. RELEVANCE TO CLINICAL PRACTICE Identification of the role of psychological and socio-demographic factors on health-related quality of life may provide more insight into the nature of irritable bowel syndrome. Greater understanding of these factors will facilitate more flexible and efficient nursing assessment and management of this patient group.

Factors that Influence Student E-learning Participation in a UK Higher Education Institution

E-learning involves the use of information and communication technologies to deliver teaching and learning and is becoming increasingly important in the delivery of higher education. An online questionnaire survey was designed to gather information on students’ participation and opinions of the use of e-learning in a UK higher education institution, and the results show that different student groups are more likely to participate regularly in certain types of study activities than others. An exploratory factor analysis reveals three underlying factors which may be used to classify the different types of e-learning activities, namely, information and communication use, general educational use, and the use of specialised software. These three factors which represent the different applications of e-learning should be considered individually in terms of design, delivery, and management of e-learning support systems, and provision of training for both staff and students.

Data mining trauma injury data using C5.0 and logistic regression to determine factors associated with death.

Trauma injury data collected over 10 years at a UK hospital are analysed. The data include injury details such as patient age and gender, the mechanism of injury, various measures of injury severity, management interventions, and treatment outcome. Logistic regression modelling was used to determine which factors were independently associated with death during hospital stay. The data mining algorithm C5.0 was also used to determine those factors in the data that can be used to predict whether a patient will live or die. Logistic modelling and C5.0 show that different subsets of injury severity scores, and patient age, are associated with survival. In addition, C5.0 also shows that gender, and whether the patient was referred from another hospital, is important. The two techniques give different insights into those factors associated with death after trauma.

Data Mining Medical Information: Should Artificial Neural Networks Be Used to Analyse Trauma Audit Data?

Trauma audit is intended to develop effective care for injured patients through process and outcome analysis, and dissemination of results. The system records injury details such as the patient’s sex and age, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the outcome of the treatment including whether the patient lived or died. Ten years’ worth of trauma audit data from one hospital are modelled as an Artificial Neural Network (ANN) in order to compare the results with a more traditional logistic regression analysis. The output was set to be the probability that a patient will die. The ANN models and the logistic regression model achieve roughly the same predictive accuracy, although the ANNs are more difficult to interpret than the logistic regression model, and neither logistic regression nor the ANNs are particularly good at predicting death. For these reasons, ANNs are not seen as an appropriate tool to analyse trauma audit data. Results do suggest, however, the usefulness of using both traditional and non-traditional analysis techniques together and of including as many factors in the analysis as possible.

The Impact of Repeated Lying on Survey Results

We study the effects on results of participants completing a survey more than once, a phenomenon known as farming. Using data from a real social science study as a baseline, three strategies that participants might use to farm are studied by Monte Carlo simulation. Findings show that farming influences survey results and can cause both statistical hypotheses testing Type I (false positive) and Type II (false negative) errors in unpredictable ways.

The use of data-mining to identify indicators of health related quality of life in patients with irritable bowel syndrome

Health-related quality of life can be adversely affected by irritable bowel syndrome (IBS). The aims of this study were to examine the health-related quality of life in a cohort of individuals with IBS and to determine which socio-demographic and IBS symptoms are independently associated with reduced health-related quality of life. Several data-mining models to determine which factors are associated with impaired health-related quality of life are considered in this study and include logistic regression, a classification tree and artificial neural networks. As well as severity of IBS symptoms, results indicate that psychological morbidity and socio-demographic factors such as marital status and employment status also have a major role to play.