542818 research-article2014
HPQ0010.1177/1359105314542818Journal of Health PsychologyGreen et al.
Article
Implicit and explicit attitudes towards conventional and complementary and alternative medicine treatments: Introduction of an Implicit Association Test
Journal of Health Psychology 1–7 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1359105314542818 hpq.sagepub.com
James A Green1, Cynthia Hohmann1,2, Kelsi Lister1, Riani Albertyn1, Renee Bradshaw1 and Christine Johnson1
Abstract This study examined associations between anticipated future health behaviour and participants’ attitudes. Three Implicit Association Tests were developed to assess safety, efficacy and overall attitude. They were used to examine preference associations between conventional versus complementary and alternative medicine among 186 participants. A structural equation model suggested only a single implicit association, rather than three separate domains. However, this single implicit association predicted additional variance in anticipated future use of complementary and alternative medicine beyond explicit. Implicit measures should give further insight into motivation for complementary and alternative medicine use.
Keywords complementary and alternative medicines, Implicit Association Test, implicit measures, medicines
Introduction The use of complementary and alternative medicine (CAM) has increased over the past 30 years with 12 month prevalence in many countries between one-third and a half of the population (Harris et al., 2012). It is increasingly integrated in primary care settings (Gray and Orrock, 2014); however, physicians are often uncertain about the safety of herbal medicines (Clement et al., 2005), and people who take herbal medicines do not always tell their healthcare professionals (e.g., Faith et al., 2013; Jong et al., 2012; Shelley et al., 2009). Perceived advantages of herbal medicines include being seen as natural and holistic,
containing no chemicals, having little or no side effects and allowing personal control and instant access (Vickers et al., 2006). CAM is assumed to be more natural (Lynch and Berry, 2007), and natural may be associated with safer. CAM usage has been associated with non-smoking status and higher satisfaction with medical 1University 2Charité
of Otago, New Zealand - Universitätsmedizin Berlin, Germany
Corresponding author: James A Green, School of Pharmacy, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand. Email: [email protected]
Downloaded from hpq.sagepub.com at UNIVERSITE LAVAL on September 29, 2015
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Journal of Health Psychology
advice (Borneman, 2007), delaying seeking medical care (Ayers and Kronenfeld, 2012) and various personality traits (e.g. Agnieszka, 2013; Sarris et al., 2011; Wheeler and Hyland, 2008), as well as influenced by culture and social contacts (Patterson et al., 2008). The first aim of this research was to develop implicit measures for CAM and conventional medicine. Implicit measures assess automatic associations between different mental representations which contribute to decision making alongside explicit ‘deliberative’ processes (see, for example, Conner et al., 2007; Olson and Fazio, 2009). For medicines, conscious explicit choice will contribute, but so might unconscious habit and past experience. Furthermore, CAM use is less likely to be revealed to healthcare professionals (e.g. Jong et al., 2012; Shelley et al., 2009), and implicit measures may be more resistant to social desirability (e.g. Brunel et al., 2004). The Implicit Association Test (IAT; Greenwald et al., 1998) is the most popular and frequently used reaction time instrument to assess automatic associations. The IAT and other implicit measures have been adapted from social cognition to investigating health behaviours, including drug abuse (Field et al., 2004), sexual risk behaviour (Czopp et al., 2004), fruit consumption (De Bruijn et al., 2012) and attitudes towards dermatological conditions (Grandfield et al., 2005), smoking (Glock et al., 2014), exercise (Berry and Shields, 2014) and body shape (Roddy et al., 2010). The second aim was to determine whether the medicines IATs would predict anticipated future medicine use, in addition to the variance explained by explicit measures. Following prior research, low-to-moderate correlations were expected between implicit and explicit measures, with additive prediction.
Method Participants A convenience sample was recruited via a student work agency and participants were reimbursed NZ$15 for travel costs; 196 participants were
recruited, with 195 completing the study. Nine participants were excluded for getting less than 80% correct in the IAT. Of the 186 remaining, 46% were male and 53% female (2 not stated), aged between 18 and 48 years (M = 21.7 years, standard deviation (SD) = 3.9 years). This project was approved by the School of Pharmacy as a low risk study and reviewed by the University of Otago Human Ethics Committee.
Measures Explicit measures. A questionnaire surveyed explicit attitude as well as current and anticipated future medicine use. Each scale included questions on each of the following types of treatment: acupuncture, homeopathy, osteopathy, chiropractic and herbal medicines (representing CAM); prescription medicines, pharmacy medicines, antibiotics and physiotherapy (conventional). Participants were first asked about their current behaviour: when they last used the treatments above on a 4-point Likert scale (‘used in the last month’, ‘used in the last year’, ‘used, but not in the last year’, ‘never’). Their intended future behaviour was recorded by asking how likely future use of the treatment was on a 7-point Likert scale. Sources of advice were evaluated on a 7-point Likert scale: friends and family, health food store, pharmacist, doctor and other health practitioner. Attitude was measured by a scale anchored at 1 = very negative to 7 = very positive. The aggregated scale for conventional medicine had coefficient omega of .79, 95% confidence interval (CI) of (.72, .85); CAM ω = .76 (.69, .82). Efficacy was evaluated on Likert scales from 1 = not at all effective to 7 = very effective; conventional ω = .74 (.67, .79), CAM ω = .70 (.62, .76). Safety was evaluated on Likert scales from 1 = very unsafe to 7 = very safe; CAM treatments ω = .74 (.66, .80); conventional treatments ω = .76 (.68, .82). Implicit measures. After the explicit measures, three IATs measured associations between safety, efficacy and overall attitudes towards
Downloaded from hpq.sagepub.com at UNIVERSITE LAVAL on September 29, 2015
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Green et al. herbal and conventional medicines. In each IAT, the concept classifications herbal (Echinacea, St John’s Wort, Fish Oil, Herbal, Ginseng) and conventional (Panadol, Nurofen, Antibiotics, Conventional, Codral) were presented alongside attribute classifications representing the categories safe (safe, harmless, healthy, risk free, nontoxic) versus unsafe (unsafe, harmful, unhealthy, dangerous, toxic), effective (effective, proven, useful, helpful, powerful) versus ineffective (ineffective, hopeless, useless, unhelpful, pointless) or positive (positive, good, fun, happy, love) versus negative (negative, sad, bad, boring, hate). Participants sorted the presented stimuli into the two categories using ‘e’ and ‘i’ keys on the computer keyboard. Participants were randomly assigned to complete the IATs in one of two orders (safety, efficacy, overall; or overall, efficacy, safety), and presentation side was counterbalanced across participants. Each IAT consisted of five blocks. Initially, in a 12-trial block, participants practised sorting concept items (herbal vs conventional). For each IAT then, participants started with a 12-trial block practising sorting attribute categories (e.g. safe vs unsafe). Then, participants had to categorise attributes interspersed with herbal and conventional words for 26 trials. In block 3, the concept categories were practised again for 12 trials, except that the response keys were reversed. A further 26 trials in the fifth block with re-paired concept classifications and attribute classifications followed. The time between the trials was 250 ms; after a false classification, an error message occurred immediately, which required a correction before the onset of the next trial. The IAT script is available at http://osf.io/5buh7/. Statistical approach. Inquisit 2.0 was used to run the IAT task. Data were cleaned following Greenwald et al. (2003), deleting trials with very long response times (>10,000 m). IAT D scores were calculated such that high values indicated a positive association towards conventional medicines or a negative association towards herbal medicines (or both). Similarly, low IAT scores indicated a negative association
towards conventional medicines or a positive association towards herbal medicines (or both). For convenience, however, high IAT scores were referred to as positive associations towards conventional medicines and low IAT scores as positive associations towards herbal medicines. Analyses were conducted with R3.10/ RStudio 0.98 and AMOS 21.0. Maximum likelihood estimation was used for the structural model. Due to the nature of the IAT, no missing values occurred. For the other data, missing values were excluded listwise. Explicit and implicit instruments were piloted with 20 participants to check for ambiguous stimuli.
Results Reported current and future medication use Participants had used more conventional than CAM treatments during the past year or past month, with 66% having taken prescription medicines, 64% pharmacy medicines, 52% antibiotics and 31% having had physiotherapy. Herbal medicine was the most common CAM treatment with 37% use in the last year, while acupuncture (4%), homeopathy (9%), osteopathy (3%) and chiropractic treatment (3%) were rare. Aligned to their past use, participants expressed a greater likelihood for future use of conventional medicines M = 5.3 (SD = 1.3) than for CAM M = 2.7 (SD = 1.2), t(185) = −25.0, p
HPQ0010.1177/1359105314542818Journal of Health PsychologyGreen et al.
Article
Implicit and explicit attitudes towards conventional and complementary and alternative medicine treatments: Introduction of an Implicit Association Test
Journal of Health Psychology 1–7 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1359105314542818 hpq.sagepub.com
James A Green1, Cynthia Hohmann1,2, Kelsi Lister1, Riani Albertyn1, Renee Bradshaw1 and Christine Johnson1
Abstract This study examined associations between anticipated future health behaviour and participants’ attitudes. Three Implicit Association Tests were developed to assess safety, efficacy and overall attitude. They were used to examine preference associations between conventional versus complementary and alternative medicine among 186 participants. A structural equation model suggested only a single implicit association, rather than three separate domains. However, this single implicit association predicted additional variance in anticipated future use of complementary and alternative medicine beyond explicit. Implicit measures should give further insight into motivation for complementary and alternative medicine use.
Keywords complementary and alternative medicines, Implicit Association Test, implicit measures, medicines
Introduction The use of complementary and alternative medicine (CAM) has increased over the past 30 years with 12 month prevalence in many countries between one-third and a half of the population (Harris et al., 2012). It is increasingly integrated in primary care settings (Gray and Orrock, 2014); however, physicians are often uncertain about the safety of herbal medicines (Clement et al., 2005), and people who take herbal medicines do not always tell their healthcare professionals (e.g., Faith et al., 2013; Jong et al., 2012; Shelley et al., 2009). Perceived advantages of herbal medicines include being seen as natural and holistic,
containing no chemicals, having little or no side effects and allowing personal control and instant access (Vickers et al., 2006). CAM is assumed to be more natural (Lynch and Berry, 2007), and natural may be associated with safer. CAM usage has been associated with non-smoking status and higher satisfaction with medical 1University 2Charité
of Otago, New Zealand - Universitätsmedizin Berlin, Germany
Corresponding author: James A Green, School of Pharmacy, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand. Email: [email protected]
Downloaded from hpq.sagepub.com at UNIVERSITE LAVAL on September 29, 2015
2
Journal of Health Psychology
advice (Borneman, 2007), delaying seeking medical care (Ayers and Kronenfeld, 2012) and various personality traits (e.g. Agnieszka, 2013; Sarris et al., 2011; Wheeler and Hyland, 2008), as well as influenced by culture and social contacts (Patterson et al., 2008). The first aim of this research was to develop implicit measures for CAM and conventional medicine. Implicit measures assess automatic associations between different mental representations which contribute to decision making alongside explicit ‘deliberative’ processes (see, for example, Conner et al., 2007; Olson and Fazio, 2009). For medicines, conscious explicit choice will contribute, but so might unconscious habit and past experience. Furthermore, CAM use is less likely to be revealed to healthcare professionals (e.g. Jong et al., 2012; Shelley et al., 2009), and implicit measures may be more resistant to social desirability (e.g. Brunel et al., 2004). The Implicit Association Test (IAT; Greenwald et al., 1998) is the most popular and frequently used reaction time instrument to assess automatic associations. The IAT and other implicit measures have been adapted from social cognition to investigating health behaviours, including drug abuse (Field et al., 2004), sexual risk behaviour (Czopp et al., 2004), fruit consumption (De Bruijn et al., 2012) and attitudes towards dermatological conditions (Grandfield et al., 2005), smoking (Glock et al., 2014), exercise (Berry and Shields, 2014) and body shape (Roddy et al., 2010). The second aim was to determine whether the medicines IATs would predict anticipated future medicine use, in addition to the variance explained by explicit measures. Following prior research, low-to-moderate correlations were expected between implicit and explicit measures, with additive prediction.
Method Participants A convenience sample was recruited via a student work agency and participants were reimbursed NZ$15 for travel costs; 196 participants were
recruited, with 195 completing the study. Nine participants were excluded for getting less than 80% correct in the IAT. Of the 186 remaining, 46% were male and 53% female (2 not stated), aged between 18 and 48 years (M = 21.7 years, standard deviation (SD) = 3.9 years). This project was approved by the School of Pharmacy as a low risk study and reviewed by the University of Otago Human Ethics Committee.
Measures Explicit measures. A questionnaire surveyed explicit attitude as well as current and anticipated future medicine use. Each scale included questions on each of the following types of treatment: acupuncture, homeopathy, osteopathy, chiropractic and herbal medicines (representing CAM); prescription medicines, pharmacy medicines, antibiotics and physiotherapy (conventional). Participants were first asked about their current behaviour: when they last used the treatments above on a 4-point Likert scale (‘used in the last month’, ‘used in the last year’, ‘used, but not in the last year’, ‘never’). Their intended future behaviour was recorded by asking how likely future use of the treatment was on a 7-point Likert scale. Sources of advice were evaluated on a 7-point Likert scale: friends and family, health food store, pharmacist, doctor and other health practitioner. Attitude was measured by a scale anchored at 1 = very negative to 7 = very positive. The aggregated scale for conventional medicine had coefficient omega of .79, 95% confidence interval (CI) of (.72, .85); CAM ω = .76 (.69, .82). Efficacy was evaluated on Likert scales from 1 = not at all effective to 7 = very effective; conventional ω = .74 (.67, .79), CAM ω = .70 (.62, .76). Safety was evaluated on Likert scales from 1 = very unsafe to 7 = very safe; CAM treatments ω = .74 (.66, .80); conventional treatments ω = .76 (.68, .82). Implicit measures. After the explicit measures, three IATs measured associations between safety, efficacy and overall attitudes towards
Downloaded from hpq.sagepub.com at UNIVERSITE LAVAL on September 29, 2015
3
Green et al. herbal and conventional medicines. In each IAT, the concept classifications herbal (Echinacea, St John’s Wort, Fish Oil, Herbal, Ginseng) and conventional (Panadol, Nurofen, Antibiotics, Conventional, Codral) were presented alongside attribute classifications representing the categories safe (safe, harmless, healthy, risk free, nontoxic) versus unsafe (unsafe, harmful, unhealthy, dangerous, toxic), effective (effective, proven, useful, helpful, powerful) versus ineffective (ineffective, hopeless, useless, unhelpful, pointless) or positive (positive, good, fun, happy, love) versus negative (negative, sad, bad, boring, hate). Participants sorted the presented stimuli into the two categories using ‘e’ and ‘i’ keys on the computer keyboard. Participants were randomly assigned to complete the IATs in one of two orders (safety, efficacy, overall; or overall, efficacy, safety), and presentation side was counterbalanced across participants. Each IAT consisted of five blocks. Initially, in a 12-trial block, participants practised sorting concept items (herbal vs conventional). For each IAT then, participants started with a 12-trial block practising sorting attribute categories (e.g. safe vs unsafe). Then, participants had to categorise attributes interspersed with herbal and conventional words for 26 trials. In block 3, the concept categories were practised again for 12 trials, except that the response keys were reversed. A further 26 trials in the fifth block with re-paired concept classifications and attribute classifications followed. The time between the trials was 250 ms; after a false classification, an error message occurred immediately, which required a correction before the onset of the next trial. The IAT script is available at http://osf.io/5buh7/. Statistical approach. Inquisit 2.0 was used to run the IAT task. Data were cleaned following Greenwald et al. (2003), deleting trials with very long response times (>10,000 m). IAT D scores were calculated such that high values indicated a positive association towards conventional medicines or a negative association towards herbal medicines (or both). Similarly, low IAT scores indicated a negative association
towards conventional medicines or a positive association towards herbal medicines (or both). For convenience, however, high IAT scores were referred to as positive associations towards conventional medicines and low IAT scores as positive associations towards herbal medicines. Analyses were conducted with R3.10/ RStudio 0.98 and AMOS 21.0. Maximum likelihood estimation was used for the structural model. Due to the nature of the IAT, no missing values occurred. For the other data, missing values were excluded listwise. Explicit and implicit instruments were piloted with 20 participants to check for ambiguous stimuli.
Results Reported current and future medication use Participants had used more conventional than CAM treatments during the past year or past month, with 66% having taken prescription medicines, 64% pharmacy medicines, 52% antibiotics and 31% having had physiotherapy. Herbal medicine was the most common CAM treatment with 37% use in the last year, while acupuncture (4%), homeopathy (9%), osteopathy (3%) and chiropractic treatment (3%) were rare. Aligned to their past use, participants expressed a greater likelihood for future use of conventional medicines M = 5.3 (SD = 1.3) than for CAM M = 2.7 (SD = 1.2), t(185) = −25.0, p
