LITERATURE REVIEW (12-16 pages) This essay is basically synthesizing 10 sources to support the new research im proposing that needs to be done. The sources needs to be categorized into 3/4 subheadings
Drug information–seeking intention and behavior after exposure to direct-to-consumer advertisement of prescription drugs Yifei Liu, M.S. a,* , William R. Doucette, Ph.D. a, Karen B. Farris, Ph.D. a, Dhananjay Nayakankuppam, Ph.D. b aPharmaceutical Socioeconomics, S-532 PHAR, College of Pharmacy, University of Iowa, Iowa City, IA 52242, USA bDepartment of Marketing, Henry B. Tippie College of Business, University of Iowa, Iowa City, IA 52242, USA Abstract Background:Concerns about direct-to-consumer advertisement’s (DTCA’s) in- formation quality have raised interest in patients’ drug information–seeking after DTCA exposure.Objective:To identify predictors of patients’ intentions and behaviors to seek drug information from physicians, pharmacists, and the Internet after DTCA exposure, using theories of planned behavior and self-efficacy.
Methods:One thousand patients were randomly selected from 3,000 nationwide osteoarthritic patients. A self-administered survey examined predictors of intention including measurements of attitude toward behavior, subjective norm, perceived difficulty, self-efficacy, controllability, self-identity, intention, exposure to ads, and control variables. After 6 weeks, another survey measured respondents’ information- seeking behavior. For patients exposed to DTCA, 6 multiple regressions were * Corresponding author. Tel.:C1 319 335 7960; fax:C1 319 353 5646.
E-mail address:[email protected](Y. Liu).
1551-7411/$ - see front matter 2005 Elsevier Inc. All rights reserved.
doi:10.1016/j.sapharm.2005.03.010Research in Social and Administrative Pharmacy 1 (2005) 251–269 performed for information-seeking intention and behavior for 3 information sources:
physicians, pharmacists, and the Internet.
Results:The response rates were 61.9% and 80.1% for the first survey and the second survey, respectively. Four hundred and fifty-four participants reported exposure to DTCA about arthritis prescription medicines in the previous month.
Over 41% of the variance in intention and over 18% of the variance in behavior were explained by the regression procedures. The consistent positive predictors of intention were attitude toward behavior, self-identity, attitude toward DTCAs of arthritis medication, and osteoarthritis pain; while the consistent positive predictors of behavior were intention and osteoarthritis pain. The strongest predictors of intention were self-identity for physicians, subjective norm for pharmacists, and attitude toward behavior for the Internet. Perceived difficulty and self-efficacy did not predict intention, and self-efficacy and controllability did not predict behavior.
Conclusions:DTCA-prompted drug information–seeking may be under patients’ complete volitional control. To promote information searching, efforts could be made to affect factors predicting intention. Interventions could address patients’ attitude toward behavior, the influence of their important others, and their role as information seeker, respectively, for information sources like the Internet, pharmacists, and physicians.
2005 Elsevier Inc. All rights reserved.
Keywords:DTCA; Drug information; Self-care; Theory of planned behavior; Self-efficacy 1. Introduction The last decade is witness to rapid growth of direct-to-consumer advertisement (DTCA) of prescription drugs. The pharmaceutical industry’s annual investment in DTCA in the Unites States grew from $965 million in 1997 to $2.7 billion in 2001. 1,2 In 1999, it was estimated that on average Americans saw 9 prescription drug ads per day. 3In addition, approximately 86% of all US consumers recalled seeing or hearing a DTCA during a 6-month period in late 2001. 4 One concern with DTCA is that its information quality may be poor, especially regarding risks and benefits. 5-7 Between January 1997 and November 2002, the Food and Drug Administration issued 564 regulatory letters to pharmaceutical manufacturers, with regard to false or misleading DTCA. 6The main violations included minimizing the advertised drug’s risks, inadequate labeling information, misleading efficacy claims, and misleading superiority claims. Concerns regarding DTCA’s information quality raise interest in consumers’ drug information–seeking following exposure to DTCAs. That is, what types of consumers and to what extent 252Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 are consumers seeking additional drug information to supplement and perhaps clarify information included in DTCA?
We know that around 40% to 50% of patients seek further drug information from any information source after DTCA exposure. 8Physi- cians are the most common information sources for patients, pharmacists are the second most common source, and the Internet is the fastest growing source. 8Despite the prevalence of consumers’ information-seeking after DTCA exposure, there has been little theory-guided investigation of it. The extant literature does not identify key influences on consumers’ information- seeking after viewing advertisements about prescription drugs. Further- more, no study has proposed or applied a theoretical model to address DTCA information–seeking from any information source. A theoretical approach is important to use because efforts to stimulate the performance of this behavior beyond 40% of exposed individuals may be inefficient and fail to overcome barriers until the causal factors are revealed.
The objective of this study was to identify predictors of patients’ intentions to seek drug information and their self-reported drug in- formation–seeking behavior after DTCA exposure. We conducted this study in patients with osteoarthritis considering information-seeking from physicians, pharmacists, and the Internet, using the theories of planned behavior (TPB) and self-efficacy.
2. Theoretical approach Patients’ drug information–seeking after seeing/hearing DTCA can be regarded as a health behavior. One of the most influential theories to examine health behaviors is the TPB. 9According to TPB, the principal determinant of a person’s behavior is behavioral intention. 10,11 The individual’s intention to perform a behavior is determined by attitude toward performing the behavior and subjective norm. In addition TPB is used to explain behavior which is not under an individual’s complete volitional control by including the construct perceived behavioral control (PBC). 10,11 Perceived behavioral control is important in DTCA-prompted drug information–seeking because factors such as time, source availability, and patients’ inability to articulate questions and understand feedback may be barriers to this health behavior. 12,13 Another influential theory in the field of health behavior is self-efficacy theory. 9Its tenet is that psychological and behavioral change operate through the alteration of the individual’s self-efficacy. 14-16 Self-efficacy is ‘‘beliefs in one’s capabilities to organize and execute the courses of action required to produce given levels of attainment.’’ 17 This theory is important to DTCA-prompted drug information–seeking because low self-efficacy may be a barrier to this behavior. 253 Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 Self-efficacy is the original template for PBC and is most compatible with PBC. 11 Perceived behavioral control was theorized to account for situations in which people do not have full volitional control over the behavior of interest.
11But PBC was also defined as ‘‘the person’s belief as to how easy or difficult performance of the behavior is likely to be.’’ 18 However, perceptions of ‘‘under my control/not under my control’’ and ‘‘ease/ difficulty’’ are not necessarily the same. 19 There are 2 ways to measure PBC, indirect measures and direct measures.20 Perceived behavioral control has been directly measured in 3 ways including self-efficacy, perceived difficulty, and controllability. 19,21-29 It is found that self-efficacy and perceived difficulty predicted intentions as well as behaviors, and controllability predicted behaviors. 19,22,24,26,28,30 In this study, self-efficacy and perceived difficulty were hypothesized to impact intention. 19,21-28,30 Self-efficacy and controllability were hypothesized to impact behavior. 21-26,29,30 In addition to constructs from the TPB and self-efficacy, self-identity was added to the explanatory model in this study. Self-identity is the extent to which individuals perceive themselves to assume a particular societal role. 21 We added this concept because it provided additional explanatory power for intentions in previous studies using TPB. 31It may be especially important to consider in this context because patients might perceive themselves to have the role of information seeker due to influence of DTCA. Therefore, the final explanatory model included constructs from the TPB, self-efficacy, and self-identity (Figure 1). Based on the openness of TPB to include additional variables, 11 control variables were regarded as ‘‘individual difference Intention Perceived difficulty Controllability Self-efficacy (confidence) Subjective norm Self-identity Behavior H5 H3 H6H7 H8 H4H2H1* Attitude toward behavior Figure 1. Proposed model for consumer information search. *H1 means Hypothesis 1. In this figure, arrows represented the study hypotheses. Each hypothesis was positive unless otherwise indicated. Perceived difficulty was reversely measured as perceived ease (Appendix A), so H3 was also positive. 254Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 variables’’ to account for variance in intention and behavior due to differences in demographic and other characteristics among individuals. 32 3. Methods 3.1. Study design The study consisted of 2 self-administered mail surveys. Two surveys were conducted to establish temporal relationship between intention and behavior for causal explanation. The first 35-item survey assessed attitude toward behavior, subjective norm, self-efficacy, perceived difficulty, con- trollability, self-identity, intention, exposure to ads, and control variables.
In the second 2-item mail survey, information-seeking behavior was measured for respondents to the first survey. The time interval between the 2 surveys was 6 weeks. 33,34 A pilot test of the first survey was conducted with 100 subjects randomly chosen from the sample frame. The results of the pilot test were used to refine sample size, study procedures, and the measurements.
3.2. Study population and sample Osteoarthritis is a common condition for DTCA-associated visits. 1,35 The study population comprised osteoarthritic patients. The sampling frame was a mailing list purchased from KM Lists Inc. of patients taking Celebrex , Tylenol , Ibuprofen, Advil , Motrin , and Aleve . A list of 3000 osteoarthritic patients was provided, with 500 patients per antiarthritis medication. The sample size, 1000, was estimated by the calculation method for simple random sampling. 36 The formula is nRz 2!(1 P)/ (3 2!P), where n is the sample size,zis the reliability coefficient (zZ1.96 for a 95% confidence level),3is set by investigator to make sure that sample estimatedshould not differ in absolute value from the true unknown population parameterDby more than3!D(3was set to be 0.15), andPis the proportion of respondents who would be prompted by DTCA to seek further information (Pwas 46%). 4,36 3.3. Measures and data collection Both surveys applied the tailored design method by Dillman 37 and included 4 contacts with subjects: a precontact letter, a survey package, a reminder card, and another survey package for nonrespondents. Each survey package included a personalized cover letter, a survey, and a stamped return envelope. All survey items were derived from reliable and valid multi- item instruments (Appendix A). 8,21-23,25-27,38,39 Direct measures for con- structs of the TPB such as attitude toward behavior, subjective norm, and 255 Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 PBC were used with 7-point response scales. 21-23,25-27 In most instances, concepts were measured with multiple items to improve reliability. All concepts were collected separately for the 3 information sources of interest in this study: physicians, pharmacists, and the Internet. Control variables were attitude toward DTCAs of arthritis medication, age, sex, ethnicity, education, use of antiarthritic medicines, use of gastric medicines, and osteoarthritis pain. 8,38,40-42 3.4. Data analysis Overall measures of the TPB constructs, self-identity, attitude toward DTCAs of arthritis medication, and osteoarthritis pain were calculated for each information source for each person exposed to DTCA by taking the mean of the corresponding items. The frequency, means, standard deviations, and bivariate correlations for measured variables were calcu- lated. Reliability analysis was performed for multiple-item measurements.
There were 8 theory-driven hypotheses tested in this study (Figure 1).
Each arrow in the model represents a specific hypothesis. For example, H1 (Hypothesis 1) stated that there was a significant positive association between attitude toward behavior and intention. Six multiple regressions were conducted for those exposed to DTCA to test the hypotheses and identify predictors of intention and behavior for each information source.
Subjective norm, controllability, and self-identity had low item-item correlations (!0.7) (Table 1). To run the regression analyses with these variables, a single item with the largest variability was selected to capture more variance of intention and behavior (Appendix A). Cases with missing data were not included in the regression analyses. All analyses were conducted by Statistical Package for the Social Sciences (SPSS) 12.0 (SPSS Inc., Chicago, Ill).
4. Results Among the 1000 first surveys, there were 607 (61.9%) usable surveys returned, with 20 undeliverable or unusable surveys. Among the 607 second surveys, 486 (80.1%) usable surveys were returned. The average respondent was more than 45 years old, female, white, high school educated, and had regular Internet access (Table 2). Three quarters (454) of the respondents to the first survey were exposed to ads about arthritis prescription medicines in the previous month. Nearly 70% of first survey respondents took antiarthritic medications in the previous 2 weeks. Among those who took antiarthritic medicines, 15% took medications to relieve gastric pain by nonsteroidal anti-inflammatory drugs.
256Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 The participants exposed to DTCA who had a score above 4.0 on a 1 to 7 scale for measurement of intention were regarded as intenders, and those who had a score above 4.0 on a 1 to 7 scale for measurement of behavior were regarded as information seekers. Compared with the percentage of information seekers in national surveys (40%-50%), 8the percentage of intenders and information seekers was low (Table 3). In addition, respondents had low intention to seek information across information sources.
All 6 regression models with participants exposed to DTCA were significant (Table 4), accounting for 41% to 50% of the variance in intentions and 18% to 20% of the variance in information-seeking behavior.
For information-seeking from physicians, the significant positive predictors of intentions were attitude toward behavior, subjective norm, self-identity, attitude toward DTCAs of arthritis medication, and osteoarthritis pain; while the significant negative predictor was Internet access. Self-identity was the strongest predictor for intention. For behavior, the significant positive predictors were intention and osteoarthritis pain, and intention was the strongest predictor. Table 1 Reliability analysis of multiple-item measurements N items Internal consistency Behavior survey Behavior for physicians 2 0.825 Behavior for pharmacists 2 0.816 Behavior for the Internet 2 0.862 Intention survey Intention for physicians 2 0.810 Intention for pharmacists 2 0.770 Intention for the Internet 2 0.827 Attitude for physicians 2 0.842 Attitude for pharmacists 2 0.810 Attitude for the Internet 2 0.869 Subjective norm for physicians 2 0.380 Subjective norm for pharmacists 2 0.380 Subjective norm for the Internet 2 0.604 Perceived difficulty for physicians 3 0.772 Perceived difficulty for pharmacists 3 0.724 Perceived difficulty for the Internet 3 0.765 Self-efficacy for physicians 3 0.825 Self-efficacy for pharmacists 3 0.832 Self-efficacy for the Internet 3 0.896 Controllability for physicians 2 0.370 Controllability for pharmacists 2 0.435 Controllability for the Internet 2 0.723 Self-identity for physicians 2 0.517 Self-identity for pharmacists 2 0.490 Self-identity for the Internet 2 0.435 Attitude toward DTCAs of arthritis medication 2 0.748 Item-item correlation for 2-item measurements; Cronbach’safor 3-item measurements.257 Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 For information-seeking from pharmacists, the significant positive predictors of intention were attitude toward behavior, subjective norm, self-identity, attitude toward DTCAs of arthritis medication, and osteo- arthritis pain, with subjective norm being the strongest predictor. For information-seeking behavior, the significant positive predictors were intention and osteoarthritis pain, with intention being the strongest predictor. Table 2 Participants’ demographic characteristics Demographic characteristics Valid response (N) a Percent Age 18-24 11 1.82 25-34 23 3.80 35-44 72 11.90 45-54 167 27.60 55-64 171 28.26 O64 161 26.61 Total 605 100 Sex Male 165 27.18 Female 442 72.82 Total 607 100 Ethnicity White 555 91.89 Nonwhite 49 8.11 Asian 4 0.66 Latino 4 0.66 American Indian 7 1.16 Black 28 4.64 Other 6 1.00 Total 604 100 Education Below or some high school 37 6.12 Graduated high school 215 35.60 Some post high school 182 30.13 Graduated 4-year college 110 18.21 Masters, PhD or professional degree 60 9.93 Total 604 100 Access to the Internet No 168 28.00 Yes 432 72.00 Total 600 100 aValid response varied because of missing data. 258Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 For information-seeking from the Internet, the significant positive predictors of intention were attitude toward behavior, self-identity, attitude toward DTCAs of arthritis medication, and osteoarthritis pain, with attitude toward behavior being the strongest predictor. For behavior the significant positive predictors were intention and osteoarthritis pain, with intention being the strongest predictor.
5. Discussion In the proposed models, 41% to 50% of the variance in intention and 18% to 20% of the variance in information-seeking behavior were explained by regression models across information sources. This is consistent with previous research findings. On average, TPB accounted for 40% to 50% of the variance in intention and 19% to 38% of the variance in behavior in previous studies. 43 Predicting behavior appears more difficult because intention may change before performance of behavior. 43 For information-seeking from physicians, self-identity was the strongest predictor of intention. Individuals who view themselves as information seekers are more likely to have intentions to seek information from physicians. In DTCA, patients are encouraged to talk with their physicians about the advertised medication. In fact, physicians are the most common information source for patients. 8It is likely that patients’ roles as information seekers get strengthened through frequent interactions with physicians, and this role also stimulates them to intend to seek information from physicians.
In addition, others’ expectations and appraisals with this information- seeking role may support a person’s self-identity about that role. 44 For information-seeking from pharmacists, subjective norm was the strongest predictor of intention. Pharmacists have a good reputation as health professionals, and informing patients about the benefits and risks of advertised drugs could be viewed as one part of pharmaceutical care. Their advice is also less costly to patients compared with obtaining advice from physicians. When patients ask their reference group about information sources, especially for drug costs, 41 pharmacists are typically the most likely source to be recommended. Table 3 Descriptive statistics of the dependent variables for participants exposed to DTCA Dependent variables N Item mean a Standard deviation % (O4.0) Intention for physicians 445 3.57 2.14 33.9 Intention for pharmacists 426 3.04 1.95 23.5 Intention for the Internet 405 3.08 2.06 24.2 Behavior for physicians 359 2.57 1.76 13.1 Behavior for pharmacists 352 2.48 1.66 12.8 Behavior for the Internet 338 2.48 1.84 14.5 a1 to 7 response scale.259 Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 For information-seeking from the Internet, attitude toward behavior was the strongest predictor of intention. Instead of contacting physicians and pharmacists, patients could search information anytime when they were on the Web. Actually, information-seeking from the Internet involves the least social interaction with others, which means more privacy for patients. In addition, most study subjects were over 45 years, and older patients have increasingly used the Internet for self-education. 45 Once patients regard information-seeking on the Internet as enjoyable, they are very likely to have the intention to perform the behavior.
Table 4 Prediction of intention and behavior to seek drug information following DTCA exposure Intention Behavior Dependent variablePhysicians (nZ375)Pharmacists (nZ362)Internet (nZ341)Physicians (nZ311)Pharmacists (nZ294)Internet (nZ279) Regression model AdjustedR 2 0.504 0.479 0.406 0.180 0.188 0.200 df14, 360 14, 347 14, 326 12, 298 12, 281 12, 266 F 28.186 a 24.669 a 17.631 a 6.674 a 6.637 a 6.781 a Standardizedb Construct measurements Attitude toward behavior.146 a .192 a .286 a Subjective norm .253 a .366 a .027 Perceived difficulty .047 .056 .012 Self-efficacy .034 .001 .118 .077 .134 .045 Self-identity .269 a .164 a .258 a Intention .327 a .390 a .417 a Controllability .013 .065 .027 Control variables Attitude toward DTCAs of arthritis medication0.199 a 0.166 a 0.135 a 0.063 0.031 0.010 Age 0.022 0.037 0.074 0.022 0.004 0.041 Sex 0.009 0.034 0.018 0.063 0.038 0.055 Ethnicity (white or not) 0.019 0.012 0.039 0.032 0.001 0.040 Education 0.037 0.022 0.039 0.024 0.044 0.054 Arthritis medications use 0.004 0.016 0.058 0.057 0.007 0.088 Gastric medications use0.031 0.005 0.082 0.066 0.052 0.044 Osteoarthritis pain0.153 a 0.113 b 0.106 b 0.207 a 0.174 a 0.192 a Access to the Internet 0.080 b 0.059 0.065 0.062 0.006 0.089 Number of cases varied because of missing data. aSignificant at .01.bSignificant at .05. 260Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 Across information sources, attitude toward DTCAs of arthritis medication had a significant positive influence on intention. This variable was about the usefulness of information in DTCA (Appendix A). When patients think the information in DTCA is useful, they might want to confirm its usefulness by seeking more information from physicians, pharmacists, and the Internet. Although Fishbein and Ajzen 46 stated that attitude toward an object was a less powerful predictor for behavior than attitude toward the object-related behavior, this study suggests that attitude toward object could be categorized as one individual difference variable to account for more variance in intention under TPB. 32 Osteoarthritis pain had a significant positive impact on both intention and behavior. This finding suggests that DTCA has a role in a patient’s self- care activities if the patient has symptoms. When osteoarthritic patients feel pain, they are more likely to be driven by DTCA to seek drug information.
Seventy-two percent of first survey respondents had Internet access, which is comparable to 75%, the percentage of Internet access in US homes. 47 Internet access had a negative effect on patients’ intentions to seek information from physicians. This suggests that the Internet could be a substitute for physicians as an information source, because it is less expensive and more convenient. However, there was no effect of Internet access on actually seeking information from physicians. In addition, the substitution effect contradicts previous research indicating that the Internet is complementary to physicians as an information source. 48 Future research is needed to better understand the relationship between the Internet and physicians as information sources.
There was no significant relationship between intention and PBC measurements like self-efficacy and perceived difficulty. There was no significant relationship between behavior and PBC measurements such as self-efficacy and controllability. These findings suggest that information- seeking is a behavior under patients’ complete volitional control, which means that patients do not have barriers for the behavior. Another possible reason for these findings is that the barriers associated with information- seeking did not appear concrete or imminent to participants given the time interval between the 2 surveys. Intention was the strongest predictor for behavior for each information source. Because there were no apparent barriers for the behavior, it is likely that osteoarthritic patients will search for more information once they have an intention to do so.
The participants exposed to DTCA in this study had low intentions to seek drug information after DTCA exposure. They also showed a favor- able attitude toward DTCAs of arthritis medication, with a mean of 4.78 on a 7-point scale. These findings indicate that they may believe that information quality of DTCA is good or may be unaware of risk information of cox-2 inhibitors or other nonsteroidal anti-inflammatory drugs. Actually, 20% to 50% of the public believes DTCA quality is guaranteed by regulation, and 33% are unaware of drug ‘‘brief 261 Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 summary’’ information, which includes risks and side effects. 49,50 In fact, promotion campaigns for Vioxx and Celebrex were warned by the Food and Drug Administration for providing incomplete, unbalanced, and misleading information. 51-53 Concerns about cardiovascular risk associated with cox-2 inhibitors were raised. 54 Yet, Merck omitted the increased cardiac danger in its DTCA promotion. 51 The issues of cardiovascular safety led Merck to withdraw the drug from the market on September 30, 2004. 55 Later, it was reported that patients taking large doses of Celebrex daily also had a 2.5 times increased cardiovascular risk compared with placebo users. Although Pfizer maintained Celebrex on the market, it stopped all DTCA of Celebrex .56 The consistent positive predictors of intention were attitude toward behavior, self-identity, attitude toward DTCAs of arthritis medication, and osteoarthritis pain, and the consistent positive predictors of behavior were intention and osteoarthritis pain. To promote information search, efforts could be made to affect factors predicting intention. Health professionals could personalize their service to make patients feel that the experience of information-seeking after DTCA exposure is pleasant.
Health-related Web sites could make their sites more user friendly to allow patients to enjoy information searching on the Internet. Pharma- ceutical industry, health plans, and health professionals could encourage osteoarthritic patients to take the role as information seeker, especially those who suffer from pain. Particularly they could emphasize the message that ‘‘patients are drug information seekers’’ or ‘‘patients are expected to search for more information after seeing this ad.’’ Besides in DTCA, they could put the message where the patients have easy access such as Web sites, physician’s offices, and pharmacies.
Future research to examine DTCA-prompted behaviors could borrow this study’s theoretical models as a framework. Self-identity should be highlighted because DTCA might give the public different societal roles.
For example, driven by DTCA, besides the role of drug information seeker, consumers might also think they have the roles to ask about advertised medical conditions and request prescriptions. For studies to address DTCA-prompted drug information–seeking, the theory of reasoned action instead of TPB could be applied, because this behavior appears to be under patients’ complete volitional control.
6. Limitations Subjects were a convenience sample and were willing to be contacted by any party associated with KM Lists Inc. These patients might have different attributes from other osteoarthritic patients in the population, which limits the generalizability of the study results. First, these patients might be more open and more responsive to any contact. Second, affiliation with the list 262Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 company might give them more exposure to pharmaceutical commercials/ drug information. On one hand, they might have a more favorable attitude toward DTCA. Conversely, they might have already performed informa- tion-seeking and might not do it in the future.
The 2-item measures of subjective norm, controllability, and self-identity were derived from previous multi-item instruments but exhibited low reliability in this study. Therefore, a single item with the largest variability was chosen to represent them in the regression analyses. However, this procedure raises concern about construct validity for these theoretical constructs, ie, the operationalization of the single item might not represent the corresponding construct well. Future studies measuring these constructs need to use more items from reliable and valid instruments.
There were 2 other limitations in this study. First, the 2 surveys were self- administered, so there might be recall bias for some measurement items capturing events in the past, such as behavior, use of antiarthritic drugs, use of gastric drugs, and exposure to ads. Second, nonresponse bias might occur. Those who suffered from severe/extreme pain while filling out the surveys might have not responded. In addition, this study only focused on 3 information sources, physicians, pharmacists, and the Internet, while ignoring other information sources like relatives, friends, neighbors, nurses, and reference books. Those who depended on other information sources might not have responded, either.
7. Conclusions The TPB was useful in examining patients’ drug information–seeking from physicians, pharmacists, and the Internet after DTCA exposure. The strongest predictors of intention to seek information from various sources were self-identity with respect to physicians, subjective norm with respect to pharmacists, and attitude toward the behavior with respect to the Internet.
The difference in findings reflected unique attributes of each information source in patients’ information-seeking. There were reportedly few barriers to performing the behavior. Intention was the strongest predictor of behavior. Therefore, interventions to stimulate information-seeking could focus on patients’ role as information seekers for physicians, the influence of patients’ important others for pharmacists, and the user-friendliness of the Web sites for the Internet. Patients’ information-seeking after exposure to DTCA could be prompted by a mix of actions.
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56. Pfizer pulling ads for Celebrex . CNN news. New York, NY. December 20, 2004.!http:// money.cnn.com/2004/12/20/news/celebrex/index.htmO. Accessed 12.21.04. Appendix A: Survey items Part 1. Construct measures Attitude toward behavior (1) ‘‘For me, trying to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources during the next six weeks is’’ (‘‘1Zunpleasant’’ and ‘‘7Zpleasant’’); (2) ‘‘For me, trying to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources during the next six weeks is’’ (‘‘1Zunenjoyable’’ and ‘‘7Zenjoyable’’).
Subjective norm (1) ‘‘People who are important to me would disapprove/approve of my trying to get more information about a medication from each of the following sources during the next six weeks’’ (‘‘1Zdisapprove’’ and ‘‘7Zapprove’’); [Item used in intention regressions for the Internet] (2) ‘‘People who are important to me think I should/should not try to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources during the next six weeks’’ (‘‘1Zshould not’’ and ‘‘7Zshould’’).
[Item used in intention regressions for physicians and pharmacists] 266Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 Perceived difficulty (1) ‘‘For me, during the next six weeks, trying to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources would be’’ (‘‘1Zextremely difficult’’ and ‘‘7Zextremely easy’’); (2) ‘‘If I wanted to, during the next six weeks, it would be easy for me to try to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources’’ (‘‘1Zstrongly disagree’’ and ‘‘7Zstrongly agree’’); (3) ‘‘If I wanted to, during the next six weeks, it would be difficult for me to try to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources’’ (‘‘1Zstrongly disagree’’ and ‘‘7Zstrongly agree’’).
Controllability (1) ‘‘During the next six weeks, it is entirely up to me whether or not I try to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources?
(‘‘1Zstrongly disagree’’ and ‘‘7Zstrongly agree’’); (2) ‘‘How much personal control do you feel you have over trying to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources during the next six weeks’’ (‘‘1Zno control’’ and ‘‘7Zcomplete control’’); [Item used in behavior regressions for physicians, pharmacists and the Internet] Self-efficacy (1) ‘‘If you wanted to, how confident are you that you will be able to try to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources during the next six weeks?’’ (‘‘1Znot very confident’’ and ‘‘7Zvery confident’’); (2) ‘‘If you wanted to, to what extent do you see yourself as being capable of trying to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources during the next six weeks?’’ (‘‘1Zvery incapable’’ and ‘‘7Zvery capable’’); (3) ‘‘If I wanted to, I believe I am able to try to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources during the next six weeks’’ (‘‘1Zdefinitely do not’’ and ‘‘7Zdefinitely do’’).
Self-identity (1) ‘‘To what extent does trying to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources during the next six weeks affect you’’ (‘‘1Zdoes not affect’’ and ‘‘7Zdoes affect’’); [Item used in intention regressions for physicians and pharmacists] 267 Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 (2) ‘‘Quite frankly, I don’t care about trying to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources during the next six weeks’’ (‘‘1Zstrongly disagree’’ and ‘‘7Zstrongly agree’’); [Item used in intention regressions for the Internet] Intention (1) ‘‘I intend to try to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources during the next six weeks’’ (‘‘1Zdefinitely do not’’ and ‘‘7Zdefinitely do’’); (2) ‘‘I plan to try to get more information about a medication for an advertised antiarthritic prescription medicine from each of the following sources during the next six weeks’’ (‘‘1Zdefinitely do not’’ and ‘‘7Zdefinitely do’’).
Behavior (1) ‘‘Within six weeks after completing the first survey, I got more information about a medication for an advertised antiarthritic pre- scription drug from each of the following sources’’ (‘‘1Zstrongly disagree’’ and ‘‘7Zstrongly agree’’); (2) ‘‘Within six weeks after completing the first survey, how frequently did you get more information about a medication for an advertised antiarthritic prescription drug from each of the following sources’’ (‘‘1Znever’’ and ‘‘7Zextremely frequently’’).
Part 2. Control variables Attitude towards DTC arthritis medication ads ‘‘Advertisements for antiarthritic prescription medicines help me have better discussions with my health providers about my health’’ (‘‘1Zstrongly disagree’’ and ‘‘7Zstrongly agree’’); ‘‘Advertisements for antiarthritic prescription medicines help me make better decisions about my health’’ (‘‘1Zstrongly disagree’’ and ‘‘7Zstrongly agree’’).
Arthritis medication use ‘‘In the past two weeks, did you take any medicine to relieve arthritis pain?’’ Gastric medication use ‘‘In the past two weeks, did you take any medicine to relieve stomach upset caused by medications like Naproxen, Ibuprofen, Diclofenac?’’ 268Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269 Access to the Internet ‘‘Do you have regular access to the Internet?’’ Osteoarthritis pain* ‘‘Please put the number in the blank to best describe the pain you currently experience in a typical day during that activity: (a) Walking on flat surface; (b) Going up or down stairs; (c) At night while in bed; (d) Sitting or lying; (e) Standing upright’’ (0Znone, 1Zslight, 2Z moderate, 3Zsevere and 4Zextreme).
Note: *Pain of activities was measured by the 5-item subscale of The Western Ontario and McMaster University Osteoarthritis Index (WO- MAC).
Part 3. Exposure to ads ‘‘In the past month, did you see or hear any advertisement for antiarthritic prescription drug?’’ 269 Y. Liu et al. / Research in Social and Administrative Pharmacy 1 (2005) 251–269