Demystifying Research for Medical and Healthcare Students. John L. AndersonЧитать онлайн книгу.
colleague at BSMS, Harry Witchell, worked with Brighton and Hove City Council to help solve a common urban problem – that of safety in tunnels, or underpasses, specifically the Brighton Beach Tunnel. In the past, there were problems with public safety because of anti‐social behaviours which led to the tunnel's closure. In an attempt to try to make this a ‘more inviting and safer’ environment, due to increases in its use, an experiment was carried out. Video cameras and a sound system were installed in the tunnel.
The music interventions were played between the hours of 07:00 pm and 07:00 am on Thursday nights, Friday nights, and Saturday nights; for the duration of the pilot study, the tunnel was left open all night on these nights. Playlists of traditional, archetypal representatives of classical, jazz, and contemporary dance music (and silence) were cycled repeatedly to tunnel users, most of whom passed through the music intervention in approximately 30 seconds; the music was chosen to be non‐aversive, and the played sound level was measured to have a LAeq ranging from 68 to 81 dB(A).
Extensive data were gathered in the form of video files; based on motion sensing, over 15,000 filmed episodes were recorded, with almost all of these having one or more individuals moving in the tunnel (Easteal et al., 2015).
So, in this natural space, it was possible to introduce one of four sound interventions:
classical music;
jazz;
contemporary dance music; and
silence.
‘Participants’ were naïve members of the public who were passing through the tunnel. There was no randomisation as such, but the four conditions (classical music; jazz; contemporary dance music; and silence) were equally cycled, so there was a ‘random’ effect in terms of exposure to each of these. All participants were unknown – they were anonymous. The results were interesting.
Classical music seemed to lessen loitering when compared to silence or other music. Music with a faster tempo led to faster walking speeds. The researchers also noted an unexpected effect of music – dancing in the tunnel. Also, in a daytime experiment, ‘brief exposure to music led to an increase in charitable donations to collectors for the Martlets Hospice’.
They concluded that ‘At the end of the experiment, no vandalism or weather damage occurred to any of the equipment, suggesting that this intervention strategy can work in an open public space at night.’
I like this experiment. It's nice to have an experiment which notes an unexpected result – dancing! It demonstrated a good partnership between the Local Authority and the University; and it gave meaningful results which had an immediate practical application.
Note: There is no requirement for REC approval for studies which are ‘naturalistic observations’ of people. Filming in public – as long as no individuals can be identified in any publications or presentations of the results – does not raise any untoward ethical issues.
Example 4: An Experiment to Examine T2DM Decision Making
John McKinlay is an old teacher, friend, and mentor of mine. We met in Aberdeen in 1966, and he left there to go to MIT in Boston (USA) where he established the New England Research Institute and has become one of the most prolific and successful sociologists of all time. Along with others in his Institute, he conducted a series of studies of diabetes – including some very interesting and innovative experiments. In one (McKinley et al. 2012), they were interested in finding out if there were race/ethnic differences in the diagnosis of diabetes when physicians are experimentally presented with signs and symptoms of diabetes.
Previous work in the USA had found that Type‐2 Diabetes Mellitus (T2DM) varied considerably by race/ethnicity. They noted that:
Both the National Institutes of Health ( NIH ) and the American Diabetes Association ( ) report race/ethnicity to be a major independent contributor to T2DM. Assuming the race/ethnic disparity in T2DM to be real, researchers seek its explanation in either: (a) social and behavioral risk factors or life styles; or increasingly, (b) genetic contributions and family history. We consider a third possible contributor to race/ethnic disparities in T2DM – the racial/ethnic patterning resulting from diagnostic decisions, principally by primary care providers. Notwithstanding the possibility of a modest race/ethnic contribution, we question whether the reported wide race/ethnic variation in the prevalence of physician diagnosed T2DM accurately reflects its actual distribution in the general population. We hypothesize the actual prevalence of signs and symptoms of T2DM, when undiagnosed in the community, is patterned far more strongly by SES (than by race/ethnicity), but when eventually diagnosed by physicians it is patterned more by race/ethnicity (than by SES).
To investigate the size and distribution of undiagnosed T2DM in the community, they designed and conducted a random sample survey in the general population in the Boston area – The Boston Area Community Health (BACH) Survey. This was an epidemiological survey of Boston residents aged between 30 and 79 years. A ‘stratified two‐stage cluster sample was used to recruit residents of Boston with approximately equal numbers of participants by gender, race/ethnicity (non‐Hispanic black, Hispanic, non‐Hispanic white), and age group (30–39, 40–49, 50–59, 60–79)’. Altogether, 5503 adults participated (1767 black, 1877 Hispanic, 1859 white; 2301 men and 3202 women) – a response rate of 63.3% of eligible participants. Anyone who reported five of the six cardinal symptoms – fatigue; being overweight; frequent urination; thirst; not feeling well; hypertension – was considered to be highly likely to have undiagnosed T2DM.
This part of the study showed ‘no significant race/ethnic differences in the prevalence of the (undiagnosed) signs and symptoms indicative of diabetes within a socioeconomic level (lower class χ2 p = 0.79, middle class χ2 p = 0.34, upper class χ2 p = 0.40). However, significant differences are evident by SES (χ2 p < 0.0001), and they are consistent within each race/ethnic category’. So – no racial differences in the prevalence of T2DM, but there were differences according to socio‐economic status. This set the scene for the experimental study.
In the experimental part of the study, they used video scenarios of real clinical cases, with professional actors and actresses, trained to realistically simulate a ‘patient’ presenting to a primary care doctor. There were 24 identical versions of the clinical scenario. These varied only with the patients' age, gender, socio‐economic status, and race. The vignettes simulated an initial consultation of five to seven minutes.
The ‘subjects’ were 192 primary care doctors who viewed the vignettes and were asked to give the most likely diagnosis and their degree of certainty. They were then interviewed and asked to say how they would manage the case in their practice.
As they describe:
A factorial experiment is a research design consisting of two or more factors (e.g. race/ethnicity, gender, and socioeconomic status) each with discrete values (or ‘levels’). All possible combinations of these levels across the factors are then randomly assigned to subjects. Such experiments permit estimation of the effect of each factor on the response variable, as well as the effects of interactions between factors and the response variable. This approach permits estimation of the unconfounded effect of a ‘patient's’ race/ethnicity (also age, gender and SES) on diagnostic decision making when primary care physicians encounter different randomly assigned patients presenting with exactly the same signs and symptoms strongly suggesting undiagnosed diabetes.
An ordered version of a clinical vignette varying only the ‘patient's’ race/ethnicity (non‐Hispanic black, Hispanic, or non‐Hispanic white), age (35 or 65 years), gender, and SES (as depicted by their dress and occupation as a janitor or a lawyer) was shown to each of 192 licensed internists, family physicians, or general practitioners practicing in New Jersey, New York, or Pennsylvania. Physicians were also