Let’s assume for a while that Google shows you what is important. So Google’s Behavioural Science Top 5 could be translated as “The 5 most important websites about Behavioural Science”.
Well let’s take a look at them:
So there are about 10 million hits for that keyword and this page is ranking #3, just behind Wikipedia (no chance to beat them) and the FBI (probably better not to provoke them). This is actually pretty incredible. Thank you Google!
We will do our best to deliver high quality content.
PS: If you wonder: This picture was taken using a proxy – so no faking with the results, but they might look different on your computer, because Google does track you *creepy…I know*.
Just a quick post today to let you know what you can expect in the next few weeks. I am very excited about these upcoming posts, as I have only seen some concept versions – and yeah – they did look very promising!
Pamela Smith is writing on an article about power. She is a powerful woman and it’s also her topic of expertise. Thanks Pam – I am really looking forward to reading your article!
Sanne Nauts just called me about an article we have written together (ready to be published – yeah!). But that’s not what you are gonna get to read (however thinking about it…that might also be interesting) – she really knows all about the backlash effect and what women on a job interview should and should not do.
Fred Hasselman is my personal hero of Nonlinear time series analysis & Dynamical modeling. Don’t worry – he says he has come up with an ingenious way to communicate these topics to people with an IQ lower than 150 (world première!).
Hubert de Mey did give the best lecture I have ever had the opportunity to listen to (about why Skinner got it right and Chomsky got it wrong – such a pitty that battle was lost a few decades ago). He is going to write about why it is really really important to have a theory when doing research (and why mapping brain regions to “something” – does not make sense).
I have also been talking to Daniel Fitzgerald about a possible contribution to this blog. I can tell you more about it in a couple of weeks, but it will most probably be a series of short video interviews on fMRI research and technology.
I am really excited that this blog is growing and attracting such high quality writers. However if you are reading this and you are a student (=like me) I would like to tell you this: Don’t be scared – we’re all a big family. Your contribution is just as valuable – so keep it coming!
Your pretty excited
Obesity is becoming more common in western cultures. Because the genetic component linked to obesity remains the same as it has in the past, there is an increased concern about the cultural and environmental causes of overeating (Lissner, 1997). Affluent western cultures have higher incidences than cultures that have more limited resources. Western cultures also hold more beliefs about individual control over outcomes and value thinness more than non-western cultures (Klaczynski, Goold, & Mudry, 2004). Both of these observations contribute to the increased problem of weight control and overeating in western cultures. Beliefs about individual control are associated with negative affect toward people with obesity and these beliefs are internalized by people who struggle with overeating themselves (Fabricatore & Wadden, 2004). Additionally, the pressures to be thin in western societies contribute to feelings of failure in people battling overeating. In turn, the prejudice and negative attitudes toward obese individuals negatively impacts body esteem subsequently leading to binge eating (Klaczynski, Goold, & Mudry, 2004).
The influence of prejudice and negative attitudes toward people struggling with overeating is widespread. In 2004, Fabricatore and Wadden stated that in the U.S. “ridicule and disparagement of obese individuals seems to remain a socially acceptable form of prejudice” (332). Unfortunately, this prejudice extends from the medical community to the mental health community to the population of overeaters themselves (Fabricatore & Wadden, 2004; Klaczynski, Goold, & Mudry, 2004; Brownell & Puhl, 2003). The scope of this prejudice is so widespread that it does not leave much of a window of opportunity for individuals who are stigmatized by this stereotype to seek the help that they need to overcome their eating problem. Additionally, it increases the risk for low body image, thus enhancing the likelihood for further binge eating. The relationship becomes almost circular in nature.
Another implication of the prejudices that people hold toward overeating and obesity is that it leaves a much smaller window of opportunity for treatment of overeating. Brownell and Puhl (2003) suggest that “negative attitudes in physicians may lead obese persons to avoid seeking health care” (p.16). Additionally, having the belief that overeating and bingeing is due to internal flaws such as laziness also decreases the likelihood that overweight people will seek help or continue with their treatment programs if they fail to see improvement (Brownell & Puhl, 2004).
A study by Klacynski, Goold, and Mudry (2004) investigated people’s attributions of the causes of obesity and found that after stereotypes of obesity were primed, scores that attributed obesity to internal causes increased whereas scores that attributed physical and social causes for obesity remained the same. That is, being thin is an achievement of will and, therefore, being fat (the antithesis) is likely due to lack of will. They also found a negative correlation between self-esteem and anti-fat attitudes and negative stereotypes of the obese. More crucially, this correlation remained significant among participants who’s BMIs were 25 or higher (with 24 being the upper limit in the “normal” weight category). This suggests that the prejudice is so strong that it permeates into the group at which it is targeted.
Body esteem has been linked to obesity such that people with low body esteem who are also exposed to other risk factors are more likely to engage in binge eating (Klacynski, Goold, & Mudry, 2004). Low body esteem is related to negative attitudes about obesity and overeating and is related to the beliefs people hold about obesity (whether it is due to internal or external causes). More specifically, the negative attitudes people hold about obesity mediate the relationship between beliefs about control and body esteem: people who believe that overeating is due to lack of motivation and control over their own eating habits are more likely to hold negative views toward those who overeat, and consequently, negatively influences body esteem (Klacynski, Goold, & Mudry, 2004). Another explanation is that individuals who suffer from the negative stereotypes adopted from beliefs about control and negative attitudes toward obesity are more likely to experience low body esteem which contributes to binge eating. Both explanations are in accordance with the dual-pathway model which shows that low body esteem is negative affect which is a risk factor for binge eating (Van Strien & Ouwens, 2007).
Due to the rampant prejudice toward individuals with obesity and the unfortunate consequences of that prejudice, it is not surprising that there is a steady increase in weight related disorders, particularly overeating. Additionally concerning is the notion that people generally hold internal causes of obesity as the strongest influence on overeating with lesser consideration for the physical and social causes. Because these beliefs are associated with prejudice, and subsequently low body imagine and binge eating, it is particularly difficult for those who struggle with binge eating to overcome it. The prevalence of prejudice within the mental health and medical realms is overwhelming. Awareness and training of the known risk factors and maintenance of overeating is called for in these fields, and perhaps more thoroughly with the public in general, to help individuals at risk for overeating.
Brownell, K., & Puhl, R. (2003). Stigma and Discrimination in Weight Management and Obesity. Health Systems, 16-18.
Fabricatore, A.N., & Wadden, T.A. (2004). Psychological Aspects of Obesity. Clinics in Dermatology (22), 332-337.
Klaczynski, P.A., Goold, K.W., & Mudry, J.J. (2004). Culture, Obesity Stereotypes, Self-Esteem, and the “Thin Ideal”: A Social Identity Perspective. Journal of Youth and Adolescence,33, 307-317.
Lissner, L. (1997). Psychosocial aspects of obesity: Individual and societal perspectives. Scandinavian Journal of Nutrition (41), 75-79.
Polivy, J. & Herman, C.P. (2002). Causes of eating disorders. Annual Review of Psychology (53), 187-213.
Van Strien, T., & Ouwens, M.A. (2007). Effects of distress, alexithymia and impulsivity on eating. Eating Behaviors (8), 251-257.
It has been postulated (for example see Persons & Miakami) that psycho diagnostic evaluation is an important phase in the treatment of psychopathology. However there is a fierce battle going on between those that argue for a stepped-care model in mental health care and those that argue that we should rather look at the specific needs of an individual (matched care – could not find a suitable link -why?).
Connected to that is the question of how much information about the client you need in order to start treatment and if there is an amount of client information that is enough (that does not enhance the quality of the treatment plan any more).
If you can suffice with the DSM or ICD classification for treatment planning – why should you waste time on making an elaborate functional analysis of the client’s problems? However research (Persons & Miakami) has shown that treatment is not successful in about 40% of the cases and can be improved if diagnosticians make a functional analysis. This can sometimes help to focus treatment on the most important factor that keeps the client from getting healthy.
On the other hand Garb (1998 – it’s a book – for an overview read his 2004 paper: Clinical Judgment and Decision Making) has reported that lively details can influence clinicians to the degree that they make a wrong judgment about treatment decisions.
Thus we would like to know how much information should a therapist have in order to make a good treatment planning? Below I give you are plan for study 1 (total of 5 studies) with which we would like to start-up this line of research. It’s still in the planning phase and things have been changed around a lot, but we have found the direction I guess. I would like to ask you some feedback on this research.
Do you think this study answers the research question?
What do you think is the minimum amount of information that still feels “natural”?
What kind of disorders and empirically supported treatments (EST) would you chose?
What do we need to take care of when writing the vignettes?
Thanks a lot for your feedback. This is an experiment in itself: Can the global community of researchers and therapist help on improving this research idea? Can we work together – even though we will never meet? Or will someone steal this idea and conduct the research themselves? I am very thankful for your critical comments!
Research Proposal Study 1
Study 1 addresses the question whether increasing amounts of diagnostic information, from classification only to classification plus extensive case formulation, changes clinicians’ treatment plans. On the basis of the literature and in line with national and international treatment guidelines, three frequently occurring DSM-IV Axis-I (clinical syndromes) disorders are selected for which familiar first-choice psychological ESTs are available. Using real patient files as a starting point, clinical vignettes are constructed, each one presenting a patient suffering from one of the selected DSM-IV disorders. These three vignettes are rewritten in such a way as to generate three Diagnostic Classification (DC) versions: DC, DC+, and DC++. Starting with the most complete DC++ versions, increasingly information is deleted to come to the DC+ and the DC versions. DC vignettes comprise 5-8 lines. From the DC++ versions information is removed, save: 1. demographic information (e.g., sex, age, marital status, children, current job), 2. treatment history (in all vignettes: ‘moderately successful pharmacological treatment only’), 3. current complete DSM-IV diagnosis, and 4. a recommended EST. DC+ vignettes are twice as long as DC vignettes and comprise 10-16 lines. They contain all the information that is available in DC vignettes, plus further anamnestic and psychodiagnostic information (e.g., family background, life history, personality). On the other hand, all lively details in the DC++ versions have been removed, in line with findings of Garb (1998) that clinicians are biased towards lively details in patients’ life histories. DC++ vignettes comprise 20-32 lines. They contain lively details originating from the patient files of real patients that are used as a starting point. The vignettes are piloted and tested for their ecological validity (cf. De Kwaadsteniet, Krol, & Witteman, 2008; Hutschemaekers, Tiemens, & Kaasenbrood, 2005; Witteman & Koele, 1999).
By contacting mental health institutes, 90 psychologists or psychiatrists are sought who have been involved in the intake and diagnosis of outpatients at least once a week for at least five years, and who are willing to participate in a study on diagnostic decision-making carried out by email. Each participant receives 3 vignettes. The participants are told that these three diagnostic reports were made by experienced clinicians of a large mental health institute. The participants are asked per vignette to address the following three issues: 1. What do you think of the recommended psychological treatment?, 2. Do you have recommendations for additional treatment or interventions?, and 3. Do you want to add contra-indications for certain interventions? Using a Latin Square Design for the selection of vignettes, it is ensured that for each participant all three patients and each of the three versions (DC, DC+ or DC++) are represented, and that all combinations (patients x versions) occur equally as often. For each of the three patient vignettes 30 DC, 30 DC+, and 30 DC++ versions are available. Power tables show that with alphas set at .05 and n = 30 in each cell, moderate to large effects can be identified with a power of .80 (Garssen & Hornsveld, 1992). When comparing the vignette versions without further regard to the patients, there are 90 versions in each cell, enough power to detect small effects.
We expect that the more diagnostic information is left out of the vignettes, the lower the variability in treatment suggestions of the participants. The hypotheses are that the evaluations of DC++ versions compared to evaluations of DC+ and DC versions, and evaluations of DC+ versions compared to DC versions show: 1. more deviations from recommended, first choice EST, and 2. larger numbers of indicated and contra-indicated treatment suggestions.
Please give us some feedback – we are also thankful for questions and remarks about what is clear/not clear and about what you like/dislike! Also you might want to suggest a flash title for our research!
Bias in treatment recommendations is a problem health care in general. The focus on my research looks specifically at the biases in mental health care. In this field we find literature that suggests certain disorders are viewed as more “psychological” in nature and others are viewed as more “biological” in nature, and to some degree, there is evidence for this distinction (for example, see Ahn et al., 2009). However, this thinking can be problematic for two reasons: (1) mental health clients with a disorder that is viewed as more psychological in nature than biological in nature are seen to be more at fault for having their illness (Miresco & Kirmayer, 2006); and (2) holding someone accountable for his or her illness is associated with recommending psychotherapy for treatment rather than medication (Miresco & Kirmayer, 2006; Ahn et al., 2009). Perhaps this seems logical, if the cause is psychological then psychotherapy should be the best treatment choice, and when the cause is biological then medication should be the best treatment. However, this implies dualistic thinking. That is, when considering that psychological symptoms are best treated by medication it implies that there is a separation between the psychological and the biological self. Dualism assumes that our mind is non-material and therefore separate from our physical beings. The current paradigm in psychology rejects this notion and teaches materialism. According to the materialist paradigm our mind and body are both made of matter and therefore are not to be treated as separate parts of the person that need to be treated different. This is confirmed from our knowledge that changing our cognition can change the physical aspects of our brains, and also that medication can change our cognition. Additionally, we know that psychotherapy and medication both affect the brain (Kandel, 1998). Therefore, the bias that distinguishes psychological and biological causes of disorders can negatively affect the way we view individuals with mental illness and the treatment that is recommended for them.
In the current literature we see that when people are held responsible for the cause of their illness, whether it is a physical or mental illness, they are stigmatized (i.e. Crisafulli, von Holle, & Bulik, 2008). My current research seeks to understand whether the treatment choice of clinicians (and opinions of laypeople) differs when the client is clearly to blame for the causes of his/her mental disorder compared to when the client is not at blame for the causes of his/her mental disorder. One prediction of my research is that explicitly ascribing blame to the client will influence treatment choices such that those at blame are more likely be prescribed psychotherapy over medication for treatment.
To fully understand the influences of attitudes and biases in clinical decision making it is imperative to examine the biases themselves, the nature of the biases, and how they affect decision making and client care. Researching what biases exist in mental health care is important to further understand how these biases develop and the impact they might have in the mental health field. It is also imperative to look for ways to reduce biases in practice, either by awareness, training, or use of decision making aids.
Ahn, W., Proctor, C., & Flanagan, E.H. (2009). Mental Health Clinicians’ Beliefs About the Biological, Psychological and Environmental Bases of Mental Disorders. Cognitive Science (33), 147-182.
Crisafulli, M. A., Von Holle, A., & Bulik, C. M. (2008). Attitudes toward anorexia nervosa: The impact of framing on blame and stigma. International Journal of Eating Disorders 41(4), 333-339.
Kandel, E. R. (1998). A new intellectual framework for psychiatry. American Journal of Psychiatry (155), 457-469.
Miresco, M. J. & Kirmayer, L. J. (2006). The persistence of mind-brain dualism in psychiatric reasoning about clinical scenarios. American Journal of Psychiatry (163), 913-918.