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This study investigated the predictive validity of the selection methods and demographic characteristics of applicants for the pre-registration undergraduate nursing programmes at one university in the United Kingdom (UK). The UK average attrition rate for pre-registration undergraduate nursing programmes is 24 percent, with the average rising to as much as 50 percent in the United States of America. Yet the number of applications received for places on these programmes far exceeds the number of positions available. People who are awarded a position on a nursing programme must have the ability to meet the minimum standards of clinical and academic work to enable them to complete the course, making them eligible to apply to register as a nurse. The decision to admit someone onto a nursing programme rests on the selection process, which the literature tells us comprises elements of prior academic achievement, admissions tests, interviews, psychometric tests or autobiographical essays. Little is known if these methods have the ability to predict those who will complete and excel in nursing programmes, and it is not clear if a particular group of students is more or less likely to fail - i.e., not achieve their outcomes. Using the work of Dr Alan Seidman into the retention of university students in higher education as the conceptual framework, this study sought to explore the relationships between selection scores and demographic characteristics with programme outcomes for the undergraduate nursing programmes at Ulster University. Application and demographic data pertaining to the 2012 - 2016 cohorts for the Adult and Mental Health pre-registration nursing programmes were gathered, including participants' age, gender, entry route, socioeconomic status and specific learning difficulty status. The participants' selection scores (personal statements, Universities and College Admission Service (UCAS) scores and interview scores) were also gathered, and their programme outcomes which included enrolment rates, course completion rates and academic grading throughout the programme. A range of statistical analysis were employed to determine the relationships between the variables including Pearson's correlations, cross-tabulations, analysis of variance (ANOVA) and linear regression. The demographic characteristics and selection scores were simultaneously entered into a model using path analysis to determine the effect that these variables had on academic outcomes. The findings showed that there is an association with increasing age and successful enrolment on the nursing courses. Applicants who had attended further education colleges undertaking programmes such as the Access Diploma or Business and Technology Education Council (BTEC) were more likely to enrol than those who were attending school and undertaking Advanced (A) -levels. There was also an association with increasing age, affluence, UCAS scores and interview scores with increasing grade averages. Students with previous degrees outperformed their peers. Nevertheless, the relationships between all variables were weak at best. Gender and personal statements did not correlate with any programme outcomes. The largest correlations could be seen between the academic grade averages in year 1, 2 and 3, which told us that first-year performance was the strongest predictor of year three outcomes. Those with specific learning difficulties performed almost on par with those who did not have specific learning difficulties. The selection process for nursing programmes should be based on evidence and not tradition or expert opinion. The weak relationships uncovered between selection methods and programme outcomes should be considered when reviewing selection processes at universities. In the absence of clear demographic characteristics that can predict student outcomes, a system of self-referral for students who need support with learning needs should be encouraged by academic staff. This would assist students at the earliest opportunity in their educational journey to help them to achieve their educational goals, and subsequently maximise the number of nursing students transitioning into the nursing profession.

Introduction: Research has been undertaken to examine the use of eye tracking and other sensors for assessing the performance of healthcare professionals carrying out clinical tasks and procedures. The work is underpinned by the hypothesis that computational eye tracking metrics can be a surrogate to measuring competency. Methods: The thesis involves two distinct studies that use eye tracking technology and data science techniques, producing three datasets (two belonging to the first study). The first study investigated how eye tracking, using a Tobii X60 (stationary) eye tracker, could predict the competency of nurses whilst reading vital signs on a bedside monitor. Data science methods were applied to the collected data with first regression modelling and then classification modelling (e.g., decision trees). The second study used wearable eye tracking glasses by SMI and other wearable computing sensors to measure the performance of interventional cardiologists when carrying out angiographic procedures in a catheterisation laboratory using a state-of-the-art simulator (Mentice VIST). As this was a smaller sample, the data science approach was less ambitious and was focused on finding significant differences between groups and strong correlations if they existed. Results: In the first study, a total of 59 participants were recruited and concluded that eye-tracking could be used to distinguish between subjects, by performance level and characteristics. This was first, with an initial dataset of n=48, linear regression model with 62 eye tracking metrics included as predictors that had a adjusted r-squared of 0.80 and mean square error of 1.2 (on a target 0-10, p value=3.712e-12). Secondly with a larger dataset (n=59), classification showed significant accuracy of 91% (p value=0.0003) in predicting the group (novice vs expert). In the second study a total of 14 participants were recruited, and the study demonstrated that significant differences could be seen in eye tracking metrics on selected areas of interest between trainee and expert interventionist cardiologists. Experts had a significantly larger dwell % (11.1 ±4.3 vs 4.7 ±1.6, p = 0.006) and fixation % (8.5 ±3.5 vs 3.5 ±1.4, p = 0.007) on the instruments 14 screen. In addition, experts had a significantly higher totalled dwell % (63 ±10% vs. 42 ±20%, p = 0.03) and fixation % (50.2 ±9.6 vs 33.5 ±17, p = 0.04). Conclusions: This thesis provides evidence that eye tracking behaviour can measure some of the variation in how competent a user is, whether that involves diagnostic tasks (reading clinical data) or carrying out a clinical intervention (cardiac treatment). The first study conducts the first ever capturing of visual attention measurements, from a set of nurses (with varying experience and expertise) while reading and interpreting from a simulated vital signs monitor. The data collected, specifically the data analysis of eye tracking metrics, has shown that visual attention and the Performance Level for this specific task (measured by performance score) are not independent of each other. Also, a subset of metrics from the eye tracking data can accurately classify the interpreter as a novice or expert. The second study captured a unique dataset with psychophysiological metrics along with a novel measurement of attentional capacity recorded during an important highly skilled clinical procedure. Significant differences between groups have been found when using these metrics; most notably the dwell % and fixation % spent on the display screens.