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Communication Between Patients And Professionals Health And Social Care Essay

This chapter examines existing studies on communicating risk using different formats, discusses the effectiveness, accuracy and presentation of patient risks information, particularly looking at studies conducted on communication with young patients.

3.4 . Risk Communication – Existing studies on use of graphic tools for a/effective risk communication.

Effective and affective risk communication is important for both patients and medical professionals and has an impact on decision-making, diagnosis, testing, further medical treatment and successful recovery. To allow people to make an informed decision, particularly in terms of risk, can also help to improve patient-doctor relationship. It is not only a matter of content but also how information is presented. (Timmermans, Molewijk, Stiggelbout and Kevit 2004).

Many studies have been conducted concerning patients’ needs in terms of informed choices. (e. g. Panton, R , 2009, Ulph, F., 2008, Peters, E., 2008, Coad, J., 2007, Price M., 2007, Paling, J., 2003, Timmermans, D.R.M., 2004, 2005, Briss, P., 2004 O'Connor, A., 2002, Fischhoff B., 1999). Paling points out that “effective risk communication is the basis for informed patient consent for medical treatment, yet until recently doctors have lagged behind other professionals in learning this skill” (Paling, J., 2003). “Professionals need to support patients in making choices by turning raw data into information that is more helpful to the discussions than the data” (Edwards, A., 2002). Encouraged by a number of researchers health professionals recently more often try to enable patients to adequately comprehend the risk as its understanding can be crucial for appropriate decision-making. They are facing a range of obstacles and problems of different kinds.

Effective risk communication, says Fischhoff, “uses audience members’ time well by providing them with the information that they most need, in a form that they can easily comprehend”. Furthermore, he stresses, that “accomplishing this task can be hard because of problems with both the transmitter and the receiver” (Fischhoff B., 1999).

Communicating risk is certainly not an easy process because of its complexity and therefore can be challenging for the health professionals. Thun gives a brief overview of main communication difficulties which American doctors are struggling with; such as patient’s poor numeracy skills, limited knowledge about the causes of cancer, or risk of cancer, and also problems with recalling or interpreting probabilities (Thun, M., J., 2008). Many different dimensions and inherent uncertainties need to be taken into account, says Paling. Recent findings on the perception of risks and benefits from a psychological perspective further complicate the task.” (Paling, J., 2003). Paling also brings out the example of Lloyd and colleagues’ research, which suggested that “patients just extract the gist of any information—not the detail—to make decisions” (Lloyd A, et al. 2001). Moreover, most patients’ comprehension of risks is primarily determined not by data they receive but by emotions (Paling, J., 2003, Timmermans, R.D.M., 2005, Klein, W., M., P., Stefanek, M., E., 2007, Finucane, M.L., 2008). “Thus, although most doctors can readily provide a competent account of the biomedical data relating to a particular risk, this alone is likely to be sterile. If the patient’s feelings skew an understanding of the facts, then his or her ability to make objective decisions about clinical management will be impaired” (Paling, J., 2003).

3.4.2 Using visual aids for presenting probabilities

Paling advises health professionals to use appropriate visual aids thus patients from all backgrounds can understand their explanations. “Even in developed countries substantial numbers of patients have poor numeracy or literacy skills and are likely to have difficulty understanding the meaning of the numbers that doctors wish to share. For these people, visual aids can help by showing the numbers in perspective. The pie chart (pioneered by Florence Nightingale, fig. 1) is a prime example of a simple yet effective visual aid, helpful to people at all academic levels” (Paling, J., 2003).

Figure 1. Diagram of the Causes of Mortality in the Army in the East, graphs often described as roses, created by Florence Nightingale. As a pioneer in establishing the importance of sanitation in hospitals she aimed to communicate the gathered data on relating death tolls in hospitals to cleanliness in most, as she assumed, effective way by using graphic representation, similar to commonly used now pie charts.

(www.understandinucerntainty.org/node/213)

Paling has developed several tools for ‘helping to explain the risks of different orders of likelihood’ (figs 2-3).

Figure 2. Paling Palette© —for displaying most medical risks with a probability of higher than 1 in 1000. The doctor or genetic counsellor fills in the relevant data while sitting beside the patient. This format shows the estimates of positive and negative outcomes simultaneously and presents unambiguous visual representations of the probabilities.

The patient may take a printout home for further consideration, or the form may be signed by the patient and a copy kept on file (Paling, J., 2003).

The way doctors communicate risk can affect a patient’s perception of risks and therefore, as Paling stresses that numerical data should be enhanced with verbal explanations, doctors are supposed to use absolute numbers instead using relative risks or percentage improvements, he advises also stating the odds from a positive and negative perspective and using a consistent denominator.

Figure 3. Revised Paling Perspective Scale© - for displaying risks covering widely different orders of magnitude (Paling, J., 2003).

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O'Connor reviewing present decision aids; include booklets, tapes, videodiscs, interactive computer programs, or paper based charts, sees them as valuable and helpful for presentation and discussion of risk information with patients. However, as she concludes “there continue to be too few studies to determine the effects of decision aids on persistence with the chosen therapy, costs, or resource use” and there is a need for further evaluation. (O'Connor A., 2009).

Timmermans distinguishes three formats for communicating risk: verbal terms, a numerical format, and a graphical format. Using graphics is considered to be useful for expressing uncertainty. “When a thing is difficult to understand, he says, it seems obvious to use graphics to explain it. Graphical risk information is assumed to help individuals to understand and summarize risk information” (Timmermans,R.D.M., 2005). However according to Timmermans studies there is no significant evidence on superiority of graphic over other formats in terms of communicating risks. Nevertheless the presentation of icons was evaluated as very helpful, with indication that grouped icons might be better than allocated icons. Vertical bars were evaluated as less suitable way to present risk (Timmermans, R.D.M., et al, 2004).

Center for Prenatal Diagnosis of the VU University Medical Center uses icons to explain the results of a screening test, (Fig. 5) (Timmermans,R.D.M., 2005). Similar to Paling Palettes however, instead of neutral human silhouettes, emoticons were introduced. Smiling faces represent not affected individuals whereas black dots show the number of chances of being pregnant with a child with Down’s syndrome.

Figure 5. Example of the risk formats:

the 1-year mortality risks of the low-risk patient as presented, respectively, in the numerical format, as stacked vertical bars and as icons (randomly located icons) (Timmermans, R.D.M., et al, 2004).

Figure. 6. The left picture shows a normal chance (i.e. not increased) and the right picture shows an increased chance of being pregnant with a child with Down’s syndrome.

(Timmermans,R.D.M., 2005)

Parallel risk communicating graphic formats, derived from those designed by Paling, are presented by Edwards. One of them combines numerical data, scale, and language data conveying levels of increasing risk (figure 7) (Edwards, A., 2002).

Figure 7. Risk language proposal, derived from Paling

Edwards presents also Visual Rx, an available on-line graphic tool, which is designed to help in the process of translation of evidence into practice, the relative measure into an absolute measure. And again emoticons represent human participants, this time four types of faces differing in facial expression and colours to signify the data, fig.8.

Figure 8. Portrayal of risks and benefits of treatment with antibiotics for otitis media designed with Visual Rx, a program that calculates numbers needed to treat from the pooled results of a meta­analysis and produce a graphical display of the result (Edwards, A., 2002). For original examples visit:

www.nntonline.net/visualrx/examples/

Edwards’ studies emphasize that information must be presented clearly. “Sometimes numerical data alone may suffice. The visual presentation of risk information has also been explored. Some empirical studies suggest that many patients prefer simple bar charts to other formats such as thermometer scales, crowd figures (for example, showing how many of 100 people are affected), survival curves, or pie charts; other studies have found that people may prefer presentations that lead them to less accurate perceptions of risk” (Edwards, A., 2002).

Lipkus and Holland present an overview of graphic formats for communicating risk; they give the examples of visual displays that have been introduced to provide effective risk information such as risk ladder, Chernoff faces, line graphs, dots, marbles, pie chart and histogram.

Figure 9.

Examples of visual displays that have been used to communicate risk.

Researchers have used the following to illustrate risk:

(a) risk ladder; (b) stick, human, Chernoff faces;

(c) line graph; (d) dots and Xs in which the Xs represent those affected by the hazard;

(e) marbles;

(f) pie chart (data are fictitious); and histogram.

Reprinted with permission of author.

(Lipkus, and Hollands, 1999)

Figure 10. Example of a Nightingale rose. For each rose, a circle is divided into multiple regions of equal angle; the radius of each slice is used to depict the quantity of interest. Because the data for each season are in the same position in each rose, it is easy to compare them. The data are fictional. (Lipkus, and Hollands, 1999)

Figure 11. Example of a risk ladder conveying the risks of radon. Radon levels are being compared with the number of cigarettes smoked and the number of extra cancer deaths. On the right, the ladder displays an action standard (pointing arrow of 4 pCi/L), along with advice on how to interpret radon levels and the action that is required, if any. Reprinted with permission of author.

(Lipkus, and Hollands, 1999)

Figure 12. Pie chart developed by the National Cancer Institute and evaluated by focus groups to depict lung cancer risk as a function of smoking and radon exposure. Reprinted with permission from the National Cancer Institute (49).

Fig. 13. A graph with a low data–ink ratio. Notice the amount of ink devoted to objects that do not contain the data of interest (pictures, busy background, horizontal grid lines, patterned fills on the bars, etc.) (Lipkus, and Hollands, 1999).

Figure 14. Ibrekk and Morgan’s recommended graphical plots to communicate quantitative uncertainties. This example of a cumulative distribution function is plotted directly below the probability density function with the same horizontal scale and with the location of the mean marked by a dot. Reprinted with permission. (Ibrekk H, Morgan GM, 1987, in Lipkus, and Hollands, 1999)

Presenting these information format examples, Lipkus and Holland were on the early stages of their research on how “providing visual displays of cancer risk per se affects risk perception, decision-making processes, and, ultimately, behaviour”. They stressed that due to multidimensionality of risk, collaborations between various disciplines and organizations are needed. “Working collaboration between experts in human factors, psychology, sociology, psychophysics, graph perception, and the mass media is likely to lead to more integrative and novel approaches than research within a single discipline” (Lipkus and Hollands, 1999). The research indicates a need to “ascertain the extent to which graphics and other visuals enhance the public’s understanding of disease risk to facilitate decision-making and behavioural change processes” (Lipkus and Hollands, 1999).

Anckner and colleagues more recently searched for evaluation studies of graphs describing, probabilities, frequencies, or chances of health events that had not been covered in Lipkus and Hollands’ review (Anckner et al, 2006). They excluded commentaries and instructions covered already by Edwards and colleagues (Edwards et al., 2002) also studies of pain scales, utility measures, or illustrations that communicated treat or casual relationships, and studies in which graphics were not used as an independent variable (Elwyn et al., 2004, Schapira et al., 2000). According to the findings the choice of graphic format for risk communication depends upon the purpose; different formats should be used for enhancing quantitative understanding or promote good arithmetic judgments, whereas others to promote behavior change (Anckner et al, 2006).

Moreover Anckner points out that “for good quantitative judgments the size of graphic element should be proportional to the number it portraits”, otherwise people can be more influenced by the size than by the number. Research showed that part-to-whole bar charts and part-to-whole sequentially arranged icons arrays can be used to help viewers comprehend the mathematical proportion (Stone et al, 2003, Schirillo et al., 2005). Furthermore “this may help them de-emphasize the emotional content of accompanying text” (Anckner et al, 2006, Fagerlin A, 2005). With experts and lay users given some instruction, survival curves can be useful for drawing attention to information that is otherwise ignored, such as middle-term outcomes (Anckner et al, 2006). Patients can distinguish proportions quite successfully with part-to-whole sequential icon arrays. However, say Anckner et al., proportions are difficult to evaluate in randomly arranged icon arrays and possibly also when the icons are jittered. This could account for the dislike of random-arrangement arrays found in qualitative studies (Feldman-Stewart et al., 2000) “Thus, sequentially arranged icon arrays may be better than random ones in any situation that requires the viewer to estimate a proportion or compare two proportions” (Anckner et al, 2006). Researchers stressed that additional work may be needed to confirm the hint in some studies that randomly arranged icon arrays help convey the difficult concept of chance or uncertainty (Baty et al., 1997, Witte K., 1997).

Anckner and colleagues found that relatively few studies have attempted to express the even more difficult concept of uncertainty around a probability estimate (confidence intervals).Therefore communicating an uncertainty in risks “should be a topic for continuing study, given older findings that laypeople are often unfamiliar with the concept of scientific uncertainty” (Anckner et al, 2006).

They also state that qualitative research is important to learn more about how patients interpret graphs, however “relying too heavily on patients’ likes and dislikes may pose a problem because they sometimes like graphics that lead to poor quantitative judgments” Researchers expect that future research will help develop graphics that are both acceptable and successful in promoting quantitative judgments or behavioral outcomes (Anckner et al, 2006).

Furthermore they advice to take in account interactions with education level, literacy, numeracy, and culture, thus they are important continuing areas of research. In conclusion they point out that although graphs often seem to be more intuitive than words, the literature shows that graphical literacy is strongly affected by expertise and familiarity with specific graphical formats. Moreover the instruction might be needed to enable patients to interpret certain formats. (Anckner et al, 2006).

A recently issued set of guidelines for creating patient decision aids recommends the use of multiple risk presentation formats (O’Connor AM, 2007, 2009). This recommendation supports the results of research conducted by Dolan (Dolan J. G., 2008). According to his study the most preferred was a combined format (combined augmented bar chart + flow diagram) and all three combined formats were more preferred than the three single format options included in the study, Fig. 17 (Dolan J. G., 2008). Dolan’s study has several limitations, however there is a clear suggestion that patients may prefer combined, rather than single, graphic risk presentation formats and that augmented bar charts and icon displays may be useful for conveying comparative information about small risks to clinical decision makers. Nevertheless Dolan suggests that further research to confirm and extend these findings is needed (Dolan J. G., 2008). Whether patient preferences are affected by different colour schemes, axis formatting, the size of the display, and other design characteristics remains unknown.

Figure 15. The augmented bar chart.

The left hand panel is a standard bar chart showing the entire dataset. The right hand panel magnifies the differences between the two options so the magnitude of the differences can be seen more clearly (Dolan J. G., 2008).

Figure 16. The augmented icon display.

The left hand panel is a standard icon display showing the entire dataset. The right hand panel magnifies the differences between the two options so the magnitude of the differences can be seen more clearly. The red diamonds indicate patients with cancer, the green diamonds indicate patients without cancer, and the broken diamond symbol indicates cancers prevented through screening and screening-related interventions (Dolan J. G., 2008).

Figure 13. The flow diagram.

Figure 17. Example preference comparison screenshot.

This figure shows the screen used by the study subjects to make the comparisons among the risk presentation formats. The slider used to indicate their strength of preference, if any, is shown in the top panel. The magnitude of preference was indicated in the numeric box to the right and in the linked horizontal bar charts and pie chart below. The panel in the upper left is the menu screen used to move from one comparison to the next (Dolan J. G., 2008).

Most recently Lin and colleagues carried on research on presenting the risks of fatal abnormality to pregnant women as an important in counseling prior to offering prenatal screening tests. Moreover they state that these risks must be balanced against the risks of harm caused by diagnostic investigations that often means that patients and professionals are faced with difficult judgments. Research considered how these visual presentation tools can be developed to communicate risk more effectively, especially in the dilemma decision making process. Related studies have revealed that visual presentation such as graphics; illustration and pictures affect perceived risk, attitude and behavior. A questionnaire method was applied to this research to evaluate 9 different formats of dilemma decision making tools (Lin, F-S. et al. 2009).

Figure 18. 9 different formats of dilemma situation were developed in this research and all of them were adopted the same information of the risk for pregnant women to conceive babies with Down’s syndrome, and the chance of amniocentesis causing abortion. Two comparative data were juxtapose together to see if the dilemma situation will affect their choices, including text format, ratio data format, proportion data format, histogram format, pie chart format, abstract image format, discrete concrete image format (the icons are arranged as a block and touching each other), sequential concrete image format (the icons are not touching each other), and a composite format (Lin, F-S. et al. 2009).

Similarly to previous related research, Lin and colleagues found that different visual tools will affect people’s risk perception; however it would not affect their choices of testing, although there is distinction accordingly to the age groups. The research shows that any instructions provided to people in any time or any places will all affect their decision making. When trying to communicate the treatment options with patients, the researchers advised, take patients’ “life styles, backgrounds, or even the social phenomena in to consideration to provide balanced value-neutral and most helpful information to them to make appropriate decisions” (Lin, F-S. et al. 2009).

One of the studies conducted by Fillingham on ‘best practice in design for patient information’ suggests that “using statistics, photos and illustrations are amongst the most popular choices for how participants think risk should be explained to them. Furthermore, photographs and illustrations allow people to understand and visualise procedures explained within the text of a leaflet” (Fillingham, S., 2008).

Figure 19. Risk perception piece inspired by Paling Palette

(Fillingham, S., 2008)

Fillingham designed a range of icons for based on the Paling Palettes information sheets. His aim was to create an educational and interactive form using graphics, icons and illustrations. As an outcome he produced a breast cancer risk game and breast cancer screening perception game and also redesigned risk informing leaflets.

Figure 16. Cervical cancer risk chart (Fillingham, S., 2008)

Figure 20. Cervical cancer answer sheet (Fillingham, S., 2008)

Introducing icons-stickers along with a game format made a design more interactive, which can improve patient-doctor relationship by allowing the risk information to flow in both directions; both participant and physician can benefit from, gathering important information. Moreover, as Fillingham suggests this game experience could be more entertaining and pleasurable than reading a text based leaflet and therefore the information can be recalled more effectively by the participant (Fillingham, S., 2008).

Importantly, while carrying on his research, Fillingham managed to collect essential data on perceived risk as well as participants’ personal preference of text or a graphic based medium. The study shows that patients favoured lighter and brighter colours over darker colours, which often have negative associations. Therefore the author recommended use of these lighter colours within risk leaflets for positive associations. Furthermore colour data collected shows that light blue, pink and yellow were amongst the most popular/favourite colours chosen by participants (Fillingham, S., 2008). [More about colour and graphics analysis in chapter 4]

A study conducted by Panton in her research looks at risk information provided to parents of children with cancer. (eCancerCare system, DePICT Roadmap cards, fig. 21, 22). Parents are often confronted with incomprehensible, complex information that is badly designed to effectively communicate multiple treatment options, risks, and outcomes. Therefore “a clear understanding of risk is particularly important in these discussions, and necessary for fully informed consent to achieve optimal patient care” (Panton, R., 2009).

Figure 21. eCancerCare is a system of point-of-care disease-specific databases that ‘dock’ with the standard electronic medical record to provide details not available in the institutional record: (a) Individual patient data are viewed under tabs that accommodate the needs of each disease site, designed by the site group team. For example, eCancerCareRB incorporates retinal drawings and digital images that provide detailed information on intraocular tumours. (b) DePICT provides a graphical representation of each eye, indicating the severity of disease at diagnosis (Group D in each eye in this case) with symbols indicating the treatments delivered (Panton, R., 2009).

Figure 22. Legend and DePICT Roadmap cards representing treatments over 5 years after initial diagnosis for nine eyes presenting with the same severity of intraocular retinoblastoma for Groups A to E of the International Intraocular Retinoblastoma Classification (Panton, R., 2009).

Panton’s studies shows that “understanding risk is related to parent age, with older parents averaging higher scores, regardless of education attainment or first language. Our results, says Panton, may also imply that parental understanding of risk is related to their command of the language used by the clinician” (Panton, R., 2009).

3.4.3 Communicating risk to children/young patients.

Health professionals make an attempt to involve children in the decision making process and provide both verbal and written information. The majority of health information is designed by adults and is in the form of leaflets. There is no evidence whether such information is appropriate for children. Moreover there is still too small number of studies concerning visual risk information addressed to children. Which format of information is most suitable for young patients to communicate risk? Can they perceive risk equally to adults; does their response to the formats differ?

The study of risky decision-making have been relatively rare, however several investigators have approached this complex subject and managed to develop, suitable for kids, tasks, which aim to capture developmental trends in risky decision-making process (e. g. Harbaugh et al., 2002, Reyna, V. F., & Ellis, S. C., 1994, Schlottmann, 2000, 2001).

One of these conducted by Schlottmann aims to determine “children’s strategy for evaluating complex gambles with alternative prizes for alternative outcomes” (Schlottmann A., 2001). To find the winning outcome, a marble is shaken in a clear tube inset with a bicolored strip. Probability is manipulated by varying the number of small or very large prizes that could be won on one outcome (1 or 10 crayons on yellow), while the other outcome carried intermediate prizes (3 or 6 crayons on blue). Children judged how happy a puppet would be to play the game, the judgment taken as a measure of Expected Value, fig. 23 (Schlottmann A., 2001).

Figure 23. Schematic of two sample games. A marble could land on either tube segment, and the puppet would win the prize placed by that segment. The two games illustrate that the same physical cue has different meaning in the context of different games: In the top example, the one unit yellow segment represents .2 probability, in the bottom example .5. In the top example, the six crayon prize for blue makes it the higher value, risky alternative, but in the bottom example this is the lower value sure thing.

(Schlottmann, A., 2001)

The study found that ‘all age groups (6 years old, 9 years old and adults) used similar intuitive operations’. The author suggests that there is similar intuitive potential for the teaching of judgment/ decision in children and adults (Schlottmann A., 2001). This study does not include risk factor, which can significantly affect probability perception.

Levin and Hart (Levin et al., 2003, 2007) addressed the question about the age that children should be provided with the risk information at and when they become capable to comprehend risk information, and probability issues in particular. Researchers used cups’ task game where probability is conveyed by the number of cups from which choose. The research found that 6-year-old children make their decisions on the basis of both probability and outcome information, however they made more risky choices than adults (they parents).

On the basis of previous studies current authors (Levin et al., 2007) and others (e. g. Harbaugh et al., 2002, Reyna, V. F., & Ellis, S. C., 1994, Schlottmann, A., & Tring J., 2007) concluded that young children possess the basic understanding and the ability to consider both probability and outcome information in terms of risk associated decision-making process. Furthermore they anticipate that future research will be able to “track how different stages of neutral development separately impact the emotional and cognitive components of adaptive decision making” (Levin et al., 2007).

Latest studies by Figner and colleagues seem to confirm that there is still a lack of essential research looking into “the mechanism underlying developmental differences in risky decision making”, there is still not enough data on individual differences in risk taking, such as reliance on affective/deliberative strategies and information use which could aim this process (Figner et al., 2009).

Ulph and colleagues carried on research to find out how risk should be communicated to children, comparing different formats of probability information.

Similarly to earlier researchers (Levin et al., 2007), she used cup game trial to examine child ability to comprehend complex risk information fig. 24 (a, b, c). “In each trial the child was asked to select the cup which was most likely to have a ball underneath it based on the probability provided under each cup. The children were asked if they recognised each format and whether they required an explanation” (Ulph F., Townsend E., Glazebrook C., 2009). If the child selected the cup with the highest probability depicted below it the child was given one point. The study showed that there was a significant relationship between format and comprehension scores and children performed significantly better when probability was presented as a pie chart, in comparison to percentages, proportion – notation, proportion-word and mixed format trials. Furthermore, most children (84%) got all trials correct for this format and children were significantly more certain that their response was correct in the pie chart trials compared to all the other formats (p < 0.001)” (Ulph F., Townsend E., Glazebrook C., 2009).

Figure 24a. Illustration of one cup game trial (Ulph F., Townsend E., Glazebrook C., 2009)

Figure 24b. Illustration of a pie chart format trial in which the light section indicates the likelihood of the ball being under that cup. (Ulph F., Townsend E., Glazebrook C., 2009).

Figure 24c Illustration of mixed format trial (Ulph F., Townsend E., Glazebrook C., 2009).

The results of Fiona Ulph and colleagues’ studies suggest “that 7–11 year olds can understand probability information, but that the format used will significantly affect the accuracy and confidence with which children in this age group make judgements about the likelihood of an event. Of the formats studied, pie charts appear to be the optimal method of presenting probabilistic information to children in this age group”. She concludes that health professionals and designers of health messages should be aware of this when communicating medical information to children aged 7–11 years old (Ulph F., Townsend E., Glazebrook C., 2009).

Figner and colleagues investigated risk taking and underlying information use in 13- to 16- and 17- to 19-years-old adolescents and adults, using a novel dynamic risk-taking task, the Columbia Card Task (CCT), fig. 25 (Figner et al., 2009). They used digital based trials of risky cart game; smileys (emoticons) mark the successfully uncovered cards.

Figure 25. Screenshots of the hot (left panel) and cold (right panel) Columbia Card Task

(Figner et al., 2009).

As shown in Figure 25, both the hot and the cold versions of the CCT involve 32 cards, displayed in four rows of 8 cards each. At the beginning of each trial, all cards are shown face down. The rules of the game are as follows: Within a given trial, cards can be turned over as long as gain cards are encountered. Each gain card adds a specified gain amount to the trial payoff, and the player can voluntarily stop the trial at any point and claim the obtained payoff. As soon as a loss card is encountered, the trial terminates; that is, no more cards can be turned over and a specified loss amount is subtracted from the previous payoff. The top of the screen displays the following information for a given trial: number of hidden loss cards (out of 32), amount of gain per gain card, amount of loss, and current trial number.

A full factorial within-subject design varied the three game parameters or factors between trials: (a) probability of a loss (1, 2, or 3 loss cards), (b) gain amount (10, 20, or 30 points per gain card), and (c) loss amount (250, 500, or 750 points). Presenting each of the 27 combinations of factor levels twice resulted in 54 trials, with the trials randomly ordered within each of the two blocks of 27 trials (Figner et al., 2009).

The research showed that there is no significant difference in terms of making choices in cold (more deliberative) or hot (affective) dilemma situations, they seem to respond equally. Moreover, as observed in this study “risk taking occurs when the impulse from the affective system overrides deliberative impulses to avoid risk” and also relaying too much on deliberation can lead to “increased risk taking in adolescents in situations in which adults would never ever consider the pros and cons but instinctively would avoid a risk because of strong fear response” (Figner et al., 2009). As it was mentioned before Figner hopes that further research will bring more information on childhood, adolescence and adulthood risk perception and its developmental transitions.

3.4.4 Conclusion

According to existing studies different visual tools can affect people’s risk perception, however how people perceived risks would not affect their choices, the decision making process can differ according to age groups. Therefore developing graphic format for risk communication we need to take into account patients age, literacy level, their life styles, backgrounds, or individual preferences to provide most comprehensive and approachable information to aid them to making appropriate decisions. Thus multidimensionality of risk requires collaborations between various disciplines and organisations. All researchers urge that further research is needed and anticipate that area of visual risk communication for making informed choices will continue to expand and develop.

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