Effects Of Education And Media Framing On Genetic Knowledge And Attitudes

Abstract

Genetic education is paramount to continued genetic advances, as well as for beneficial use of the outcomes of such advances. However, a large body of literature suggests that the public’s genetic literacy remains inadequate. Previous research has found a positive relationship between educational level and genetic literacy. The current research explores this in 2 samples of UK students: school (A-Level) and undergraduates. A between groups ANOVA revealed a significant difference in genetic literacy scores between the educational levels. The current study further explored genetic literacy of Psychology undergraduates, as psychologists are likely to play a key role in the genomic era, for example contributing to genomic research and providing genetic counselling. Results revealed low genetic literacy in Psychology undergraduate students, highlighting the need for genetic education improvements. To this end, experimental manipulations were conducted to investigate the effects of media framing and feedback on views of genetic determinism and knowledge calibration. A between groups ANOVA showed no significant difference between high and low determinism media framing. The knowledge calibration findings suggested that participants were underconfident in their genetic knowledge. No correlation was found between knowledge and perceived knowledge (confidence) in the group of participants who received feedback. In contrast, a positive correlation between knowledge and confidence was found in the no-feedback condition. Future research is needed to build on these findings.

Keywords: Knowledge CalibrationGenetic LiteracyPsychology UndergraduatesScience CommunicationMedia Framing

Introduction

Developments in science and technology, both their independent and collective implications, permeate the culture and politics of modern day citizenship. As Fischhoff (2013) argues, whilst people can elect not to do science, they cannot elect to ignore it. Following the completion of the Human Genome Project in 2003, there have been subsequent profound developments and discoveries in genomic science. For example, in 2013, the government demonstrated its confidence in the merits of increased genomic information by its investment of £200 million in the genome sequencing of 100,000 National Health Service (NHS) patients. The aim of the 100,000 Genomes Project is to create a new genomic medicine service for the NHS, which will improve and transform personalised treatments, and potentially offer patients a diagnosis which hasn’t been previously available (Genomics England, 2014). Furthermore, the UK genomics industry currently contributes around 10% to the global market (£0.8 billion), but is set to outpace the global market, increasing at a 20% compound annual growth rate (CAGR), due to investments and national projects (Deloitte, 2015). The emerging ramifications in the “post genomic era” (Duyk, 1999) require the need for genetic literacy.

Genetic literacy can be defined as the working knowledge of genetics and related areas, including genomics, pharmacogenomics and gene therapy (Carver et al., 2017). Despite the extensive advances in genomic data via novel analytical approaches and technologies (Zhao and Grant, 2011), and genetic issues playing a considerable role in public health policy (Burton, Jackson and Abubakar, 2014), the public’s genetic literacy is still lacking (Mills Shaw, Horne, Zhang and Boughman, 2008; Miller et al., 2006; Bowling et al., 2008). Genetic education research has highlighted genetic understanding to be particularly low in school students (e.g. Saka et al., 2006; Duncan, Rogat, and Yarden, 2009; Duncan and Reiser, 2007). More specifically, research has shown students have distinct misconceptions about basic concepts (e.g. Venville and Treagust, 1998; Lewis and Kattman, 2004). A lack of understanding means individuals will be unable to make informed judgments about genetic diagnoses, or the implications of lifestyle choices for personal health and that of offspring; therefore genetic knowledge seems increasingly to be a prerequisite for modern citizenship (Marks, 2016).

Research has identified mass media and formal education as two of the main gateways of scientific information from the scientific community to the public (see Figure 01 ; and also Wellington, 1994; Falk, Storksdieck and Dierking, 2007; Wellington and Osborne, 2001). Research has also shown that the informal presentation of genetic information, used by the media, is often inaccurate (Lanie et al., 2004). In 2000, the UK government issued an official calling for initiatives to improve science communication to the public. This report was, for the most part, a consequence of concerns that the public’s confidence in science had dramatically decreased following scientific issues (such as genetically modified crops) receiving misleading media coverage (Carver, 2014). Academic institutions established the introduction of academic programmes at both undergraduate and postgraduate level in science communication (Mulder, Longnecker and Davis, 2008). Consequently, a vast body of literature has grown concerning science communication, which has focused on the media’s presentation of scientific information (e.g. Eyck and Williment, 2003; Petersen, 2001; Nisbet and Mooney, 2007).

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Problem Statement

This study aims to address some of the gaps in the existing literature. A wide body of literature has demonstrated that school students exhibit particularly low genetic knowledge (e.g. Bowling et al., 2008), However, there is only limited research on the genetic literacy of undergraduate students, particularly in the UK. This demographic is critical as the future users of genetic developments.

Previous research has highlighted that higher education leads to improved genetic literacy (e.g. Calsbeek, Morren, Bensing and Rijken, 2007; Haga et al., 2013). However, the measure of education level in previous studies has often been collected retrospectively, which complicates conclusions regarding the effect of higher education. For example, individuals selecting ‘undergraduate degree’ as their highest obtained level of education vary greatly in how much time has passed since their graduation. Therefore, it is not possible to evaluate whether it is higher education per se that leads to better genetic literacy, or whether there are other age related contributing factors, such as exposure and experience. Therefore, research is needed to test whether those currently pursuing higher education exhibit on average better genetic literacy than those currently completing lower level education.

In addition, Psychology undergraduate students present a particularly interesting group for investigation. There is a notable dearth of literature on psychology student’s genetic knowledge, in comparison to students in medical fields. However, psychology students are a crucial demographic as they are likely to be facilitators of genetic advances in the population (Laegsgaard and Mors, 2008). Rapid developments in the behavioural genetics field (Kruger, Korsten and Hoffman, 2017) and subsequent findings of functionally relevant candidate genes for various mental-health disorders (e.g. Soronen, 2012; Gibson et al., 2017) provide new roles to psychologists in the genomic era.

Finally, focusing on young adults allows for an investigation into how prepared they are for parenthood in the genetic era. Research is needed to assess the level of young people’s understanding of issues related to prenatal testing, newborn screening and probabilistic prediction.

Research Questions

The present study has 3 main aims. First, to identify whether a significant difference in genetic knowledge exists between current UK A Level and undergraduate students. Second, to explore the effects of media framing on post-exposure self-reported views on genetic determinism. Third, to investigate whether feedback improves genetic knowledge calibration.

• Hypothesis 1: Current undergraduate students (higher educational level) will demonstrate better genetic literacy compared to current A-Level students (lower educational level).

• Hypothesis 2 : Media framing will have an effect on self-reported deterministic attitudes towards genetics.

• Hypothesis 3: Greater knowledge miscalibration will on average be observed in participants who receive feedback after rating how confident they are in their genetic knowledge, than those who receive feedback before reflecting on their knowledge.

Purpose of the Study

The ultimate goal of this study is to provide new insights into factors affecting genetic literacy acquisition, including education, media framing and feedback. Better understanding of these factors will lead to more accurate and efficient transferal of scientific information from the science community to the general population. This will ultimately lead to a more genetically literate population, better equipped to function in the genomic era.

Research Methods

Study Design

The study included an exploratory part (Hypothesis 1) and an experimental part (Hypotheses 2 and 3). Figure 02 presents the design of the 2 experiments.

Experiment 1: A between participants design was used. The independent variable was word cloud (high-determinism or low-determinism) and the dependent variable was post-exposure self-reported views on genetic determinism.

Experiment 2: A between participants design was used. The independent variable was feedback (feedback or no-feedback) and the dependent variable was self-reported confidence in genetic knowledge.

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Materials, Measures and Variables

Word Cloud

Word clouds are a visual representation of the frequency tabulation of the words in a selected piece of text (Miley and Read, 2012). The word cloud method was employed in this study to increase the salience of the particular words and phrases (as suggested by Condit et al., 2001), much like would be interpreted from skim reading (Fatmawati, 2014). Two word clouds were generated using online newspaper articles (see Figure 03 ), with the most repeated words from each article appearing as larger words in the cloud. Online articles were chosen as the mass media platform for the experiment, as they are a widespread, relatively in-depth source of information for the public (Cavaar, 2013; Jarman and McClune, 2007). To retrieve genetics related articles, a search for “genes online article UK” was conducted. Only National broadsheet papers were considered because they address the broader society and are representative of genetic information being presented to society. Two broadsheet articles were selected, similar in topic and length: “Genes influence academic ability across all subjects, latest study shows” (Guardian); and “Genetic screening of pupils would herald a Huxleyan nightmare” (Telegraph). The Telegraph article fitted the hypothetical deterministic media frame : seeing genes as the definite cause for a trait (e.g. Nelkin and Lindee, 1995). The Guardian fitted the hypothetical gene versus environment media frame : which focuses on the effects of nature and nurture separately (e.g. Condit, 2007).

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The word clouds were created on www.wordclouds.com. From the Telegraph text, “Brave New World” was removed, as social familiarity may lead to response bias. As media framing requires a certain exposure time to be effective (Buturoiu and Corbu, 2015), each participant was presented with their word cloud for exactly 60 seconds.

Prior to the experiment, a manipulation check was carried out on a separate group (n=8) of a similar demographic profile, to test whether the two word clouds were significantly different in their level of determinism (as suggested by Condit et al., 2001). Participants were asked to answer for both word clouds: “what word best describes the authors conclusion about the relationship between genes and intelligence”, on a scale of 1 to 4, where 1 = unrelated, 2 = associated, 3 = predisposing and 4 = cause. The two clouds were found to be significantly different: high determinism: mean=3.38, SD= .518; low determinism: mean = 1.50, SD = .535 (t = 7.94, df = 7, p = < .05).

Genetic Literacy

The present study used The International Genetic Literacy and Attitudes Survey (iGLAS; Chapman et al., in press) developed by The Accessible Genetics Consortium (TAGC). iGLAS consists of 3 parts: knowledge, opinions, and a demographic survey. In addition, participants also completed a personality measure (shortened Big Five inventory; Rammstedt and John, 2007). Questions were formatted in a variety of ways including: Yes/No, multiple choice, Likert and slider scales - to help reduce the effects of Common Method Variance (Lindell and Whitney, 2001). The number of knowledge questions answered correctly (out of 18 total), was transformed into a percentage score, which functioned as a genetic literacy indicator.

Feedback

Feedback was provided as a total raw score of correct answers, e.g. 12/18. As detailed feedback on each item was not provided, this feedback was non-informative. The presentation position of feedback, as per the design detailed in Figure 02 , resulted from a random computerized allocation used in Qualtrics (see Figure 02 ).

Determinism

Determinism measure was calculated by the question “ I believe that my destiny is written in my genes” on a 1-7 Likert scale ranging from strongly disagree to strongly agree.

Confidence

Respondents were asked to measure their confidence in their genetic knowledge on a continuous scale between 0-100, as is common practice in task specific self-efficacy research. The use of continuous scale in online surveys has been found valid and reliable (Neibecker, 1984; Treiblmaier and Filzmoser, 2009). Confidence was only measured as task-specific, and only one measure was taken due to time limitations ( see limitations in discussion).

Knowledge Calibration

In line with previous research on judgment accuracy (Schraw and Roedel, 1994; Pearson and Liu-Thompkin, 2012), knowledge calibration score was calculated by the total difference between genetic knowledge score and reported confidence in genetic knowledge. To make genetic knowledge score comparable to reported confidence, it was translated into a percentage; such that answering 9 out of 18 questions correct would be 50%. The absolute difference between the actual knowledge and perceived knowledge (confidence) indicates miscalibration, where a positive integer is overconfidence and a negative integer is under confidence.

Findings

Conclusion

Scientific knowledge on genetics and genomics is increasing at a rapid pace. However, this has not yet translated to an improved genetic literacy in A Level or undergraduate students. The results of this study demonstrate that both A Level and undergraduate students are underperforming in their genetic knowledge scores. These results are in line with a wide body of literature (e.g. Bowling et al., 2008; Agorram et al., 2010).

The ANOVA revealed a significant difference in genetic literacy scores between the educational levels. These results provide further support to previous research, which have demonstrated a positive relationship between educational level and genetic knowledge scores (e.g. Calsbeek et al., 2007; Haga et al., 2013). Second year A-level students are only 1 year away from being Undergraduate students and should arguably have ‘fresher’ knowledge of biology and genetics, as covered by the compulsory curriculum. Moreover, most of the A-Level students in this study are likely to progress to University as they were recruited for the study whilst attending a university open day. It is therefore remains unclear why University students outperform their only slightly younger peers. Further research is needed to explore factors that explain why higher educational level leads to increased genetic literacy score. The findings also require replication, as the sample size of A Level students was small.

The second aim of this paper was to investigate whether a deterministic-framed word cloud would affect subsequent self-reported deterministic attitudes towards genetics. This is an important area of research, given the wealth of literature suggesting that the mass media is the main source of scientific information for the public after formal education (e.g. Eyck and Williment, 2003). The results suggest that word cloud exposure does not significantly affect deterministic views. There was no significant difference in deterministic views (following exposure to the word cloud) between the two groups (high determinism vs. low determinism). Whilst this finding is in line with previous literature (e.g. Condit et al., 2001; Condit et al., 2004), its generalisability must be considered against several limitations. First, the presentation of the news article, using a word cloud, was not a typical method of communication, particularly not for a newspaper article. The strict instructions given to participants, that they would have to subsequently describe the word cloud, might have resulted in lower ecological validity, as such a requirement would not be made of skim readers in a real life situation (Pannucci and Wilkins, 2010). The use of word clouds was chosen as a means of presenting fewer, more salient words, much like the interpretation one would expect from a skim-reader (Rayner et al., 2016). Investigation of skim readers is important as this is a style of reading often adopted by Internet users (Duggan and Payne, 2011), and as the Internet is often a source of over-simplistic genetic concepts (Kampourakis, 2017). However, the results suggest that this form of ‘media framing’ does not have a significant effect, at least on determinism. Future research is needed to investigate other forms of media framing in student populations.

Additionally, some researchers have reported an interaction between linguistic text structure and prior knowledge (McNamara, Kintsch, Songer and Kintsch, 1996; McNamara, 2001). Readers with a deeper prior knowledge would better interpret or “benefit” from an implicit text, whereas readers with less prior knowledge would perform better on text comprehension following explicit versions of text. Therefore, interpretation of the implicit/reduced information provided in the word cloud might have been particularly problematic for those with less prior knowledge.

Future research should extend to include university students of other disciplines, as well as different ages and birth cohorts. Young adults are immersed and arguably dependent on the mass media, which acts as a gateway to social networking, education and political participation (Towner and Lego Munoz, 2016). Whereas, older people on average spend more time watching television (Bureau of Labor Statistics, 2016; Moody, 2016). Moreover, more recent birth cohorts may be fundamentally different in media use from earlier cohorts of the same age. Given the tendency to utilize different platforms of media, it is important to establish any potential differences in genetic attitudes between ages and cohorts.

The third aim of this paper was to investigate Psychology undergraduate students’ genetic knowledge and the accuracy of their genetic knowledge calibration. The study also explored whether feedback would improve genetic knowledge calibration. These research questions are important as very few previous studies focused on this particular demographic. Previous research (Lanie et al., 2004) suggested that “ illusion of knowing” can hinder people’s ability to recognise a necessity for genetic literacy improvement and discourage individuals from seeking more information (Park, 2001).

The results of the present study showed that miscalibration scores in both the feedback and the no feedback conditions reflect underconfidence in participants’ genetic knowledge. These findings are consistent with some research (e.g., Pearson and Liu-Thompkin, 2012), but not other studies that demonstrated overconfidence (e.g. Alba and Hutchinson, 2000). Overall, the results support previous findings of self-perceived level of knowledge being frequently inaccurate (Lichenstein, Slovic and Fischoff, 1977).

There was no significant difference between feedback (-27.16%) and no feedback (-21.69%) conditions, therefore the hypothesis was not supported. Moreover, a positive correlation was found in the no-feedback condition (r = .408) between genetic knowledge and confidence in knowledge, whereas no significant correlation (r = 0.237) was identified in the feedback condition. This further highlights the lack of support for the original hypothesis. The higher miscalibration score in the feedback group was unexpected, as feedback would provide participants with a clearer indication of their genetic knowledge. Furthermore, previous literature suggests individuals are typically more accurate at postdictions rather than predictions. The postdiction advantage is thought to be a result of self-reflection and adjustment that occurs whilst completing the test (e.g. Glenberg and Epstein, 1987; Zabrucky, Lin and Moore, 2002).

The social cognitive theory emphasizes the relationship between cognitive, behavioural, environmental and personal factors and their interaction in terms of determining ones affect and motivation (Crothers, Hughes and Morine, 2008). Logically, it would be expected that the individuals who were informed of their genetic knowledge as feedback would re-evaluate their self-efficacy and confidence and adjust to the new self-evaluation information (Barling and Beattie, 1983). However, there are various reasons for their lack of subjective knowledge accuracy, which have wider implications for genetic education. Firstly, as described by Schunk and Zimmerman (1994), the ability to readjust one’s self-efficacy in accordance with feedback requires a certain threshold of motivation or desire to achieve. Secondly, various self-evaluative processes must occur for successful readjustment of task-specific self-efficacy. For example, one should be a self-regulated learner who, in terms of a metacognitive process, will organise, self-monitor and self-evaluate at various intervals during learning acquisition (Pressley, Borkowski and Schneider, 1987). However, acquisition of learning in a relatively unfamiliar knowledge domain requires excessive cognitive resources, which in application to the current study’s results may explain the high miscalibration scores. As undergraduates have on average a relatively low genetic knowledge (e.g. Bowling et al., 2008), answering genetic questions may require much cognitive resources, leaving fewer resources for metacognitive processes, and leading to greater miscalibration.

Overall, the results of this study suggest that genetic literacy remains relatively low in student populations. This contributes to existing evidence, which suggests that the provision of improved genetic education is required at both the formal and informal educational level. This study found no significant relationship between types of media frame and reported genetic determinism. These results are, in a practical sense, encouraging as they suggest that peoples level of genetic determinism are robust against media framing. However, future research should investigate different framing formats in social media, particularly focusing on the varying social media audiences in terms of age, gender, income and educational level and/or background.

The relationship between genetic knowledge, genetic knowledge calibration and availability of feedback seems complex, and requires further investigation. The method of feedback adopted in this research (total answers correct) may have lacked enough detail for the participants to engage with, hence their genetic knowledge confidence score might not have been affected by their genetic knowledge score. This would explain why participants given feedback had higher miscalibration average than those not given feedback. Future research should investigate multiple forms of feedback with varying levels of detail to decipher the relationship between feedback and confidence in genetic knowledge.

The present study has identified that media framing had no effect on participants’ reported views on genetics determinism. Whilst this may partially be a product of the limitations discussed above, it suggests that the views people hold on genetic essentialism are likely to be robust and not easily altered by media exposure. The feedback on Genetic Knowledge provided in this study seems to have done little to reduce knowledge miscalibration. Indeed, those participants that were asked to rate their genetic knowledge in the absence of feedback could do this more accurately than those who were provided with feedback on their performance. For future studies, especially in genetic knowledge, it appears that more detailed feedback may not only be more effective for knowledge calibration, but also less misleading. Both findings should contribute to the development of an open and productive discourse between genetic researchers and the general public so that genetic research findings can be used for the benefit of all.

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