Mental illness or mental distress; stigma and concealment in University students - a discussion paper

This discussion paper addresses the issue of mental distress, sometimes mis- perceived or misinterpreted as mental illness. The
focus is on positive psychology. Reflecting in part on a UK-based study with younger University students studying to health
related degrees, nursing, midwifery and medicine (N = 12), many of the students were apparently suffering dis-stress with
disordered eating at least in part being used as a coping mechanism. However notwithstanding that they were at the end of
their first year studies in health, a significant number of the students interpreted their approach to eating as a mental illness.
Consequently, many within the study felt stigmatised and were reluctant to acknowledge certainly to the University health care
authorities that there was an issue; perceiving both academic and career/professional consequences of mental health labelling. The
paper approaches the issue of mental health from a health promoting perspective, reflecting against the theory of salutogenesis
and its focus within the three dimensions of comprehensibility, manageability and meaningfulness as an approach to building
resilience and managing stressors to better facilitate a sense of coherence. Complex manifestations of distress and poor coping
mechanisms can in some cases be misinterpreted or miss perceived as mental illness. Promoting mental health and reducing the
stigma of mental illness or the misperception of mental distress as mental illness, would need to be addressed in order to more
effectively outreach certainly to younger University students who might be at risk. The focus should be on how better to promote
their sense of coherence.

Predictors of numeracy performance in undergraduate psychology, nursing and medical students

Psychology, nursing and medicine are undergraduate degrees that require students to attain a level of numerical competence for graduation. Yet, the numeracy aspect of these courses is often actively disliked and poorly performed. This study's aim was to identify what factors most strongly predict performance in such courses. Three hundred and twenty-five undergraduate students from these three disciplines were given measures of numeracy performance, maths anxiety, maths attitudes and various demographic and educational variables. From these data three separate path analysis models were formed, showing the predictive effects of affective, demographic and educational variables on numeracy performance. Maths anxiety was the strongest affective predictor for psychology and nursing students, with motivation being more important for medical students. Across participant groups, pre-university maths qualifications were the strongest demographic/educational predictor of performance. The results can be used to suggest ways to improve performance in students having difficulty with numeracy-based modules.

The role lipid aldehydes and ALEs in the pathogenesis of diabetic retinopathy

Can publicly available webcameras and mechanical Turks be used to evaluate physical activity policy and built environment change?

Introduction: Fewer than 50% of adults and 40% of youth meet US CDC guidelines for physical activity (PA) with the built environment (BE) a culprit for limited PA. A challenge in evaluating policy and BE change is the forethought to capture a priori PA behaviors and the ability to eliminate bias in post-change environments. The present objective was to analyze existing public data feeds to quantify effectiveness of BE interventions. The Archive of Many Outdoor Scenes (AMOS) has collected 135 million images of outdoor environments from 12,000 webcams since 2006. Many of these environments have experienced BE change. Methods: One example of BE change is the addition of protected bike lanes and a bike share program in Washington, DC.Weselected an AMOS webcam that captured this change. AMOS captures a photograph from eachwebcamevery half hour.AMOScaptured the 120 webcam photographs between 0700 and 1900 during the first work week of June 2009 and the 120 photographs from the same week in 2010. We used the Amazon Mechanical Turk (MTurk) website to crowd-source the image annotation. MTurk workers were paid US$0.01 to mark each pedestrian, cyclist and vehicle in a photograph. Each image was coded 5 unique times (n=1200). The data, counts of transportation mode, was downloaded to SPSS for analysis. Results: The number of cyclists per scene increased four-fold between 2009 and 2010 (F=36.72, p=0.002). There was no significant increase in pedestrians between the two years, however there was a significant increase in number of vehicles per scene (F=16.81, p