Psychological Assessment 2015, Vol. 27, No. 3, 1053–1059
© 2015 American Psychological Association 1040-3590/15/$12.00 http://dx.doi.org/10.1037/a0038699
Communication Preferences of Chronically Ill Adolescents: Development of an Assessment Instrument Matthias G. Klosinski and Erik Farin
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University of Freiburg The purpose of this study was to develop and psychometrically test a patient-oriented, theory-based questionnaire to capture the communication preferences of chronically ill adolescents in provider–patient interaction. In a qualitative prestudy, patients were asked to express their preferences in focus groups. From those results and relying on previous research findings, we generated questionnaire items and in a second pretest, examined them in 1-to-1 cognitive interviews for comprehensibility and acceptance. The resultant questionnaire was then psychometrically tested in the main study on 423 chronically ill inpatient adolescents aged 12 to 17 years in 14 rehabilitation clinics in Germany. Numerous preferences were extractable from the focus-group interviews and transferred into 106 Items. Psychometric testing of the questionnaire resulted in 3 scales encompassing 27 items. These we describe as the emotional-affective communication component (EAC), instrumental communication component (IC), and adolescentspecific communication component (ASC). Confirmatory factor analysis revealed the scales EAC und IC to be good to very good, and the ASC scale as satisfactory regarding unidimensionality. The participants gave the questionnaire high marks for comprehensibility, acceptance, and relevance. The 3 scales’ Cronbach’s alpha falls between .78 and .92. A questionnaire with 27 items is now available for application as a psychometrically tested and simple-to-use measuring instrument. Research is still needed concerning the generalizability to other patient groups (e.g., the acutely ill or outpatients) and whether it can be tailored for use by different types of care providers or to accommodate the communication preferences of parents. Keywords: assessment instrument development, communication preferences, patient participation, provider–patient communication
and interindividually (Rodin et al., 2009). To accommodate optimally a patient’s current preferences, they should be assessed individually, for there is evidence that providers often fail to accurately assess those of adolescents (Britto et al., 2007). Such assessment can also contribute to meeting the demands for more patient-centered therapy (Institute of Medicine, 2001). This is especially true of children and adolescents, whose participation rights (WMA Declaration of Ottawa on Child Health, 1998) in terms of patient-provider interaction are often rather neglected (Coyne, 2008). Many investigations have addressed the communication preferences of ill adolescents. In their review, Freake, Barley, and Kent (2007) described numerous preferences, for example, the desire on the part of children and adolescents for confidentiality, information, to be listened to, and that their providers be kind, caring, and understanding. Others report preferences such as the wish for greater participation (Britto et al., 2004; Dunsmore & Quine, 1996; Moules, 2009; Quinn et al., 2011; Schaeuble, Haglund, & Vukovich, 2010; Van Staa, Jedeloo, van der Stege, & the On Your Own Feet Research Group, 2011; Zwaanswijk et al., 2011), equality (Ginsburg, Forke, Cnaan, & Slap, 2002; Woodgate, 1998), and humor (Britto et al., 2004; Woodgate, 1998). There are several assessment instruments that partially capture communication preferences while primarily attempting to incorporate other constructs such as compliance, satisfaction, or health care preferences (Bethell, Klein, & Peck, 2001; Britto et al., 2004; Garland, Saltzman, & Aarons, 2000; Ginsburg et al., 1995; Gins-
Successful communication between patient and provider plays a key role in endpoints such as satisfaction, adherence and therapeutic success (Knopf, Hornung, Slap, DeVellis, & Britto, 2008; Kyngäs, 2000; Swedlund, Schumacher, Young, & Cox, 2012). It is characterized by the best possible fit between the patient’s communication preferences and the provider’s expectations (Epstein et al., 2005). Yet communication preferences vary profoundly intra-
This article was published Online First March 16, 2015. Matthias G. Klosinski and Erik Farin, Institute for Quality Management and Social Medicine, University of Freiburg Medical Center. The manuscript is based on data of a not yet published doctoral dissertation. We thank the participating rehabilitation centers: Klinik Bad Gottleuba GmbH & Co, Bad Gottleuba; Viktoriastift Bad Kreuznach, Bad Kreuznach; Spessart-Klinik Bad Orb GmbH, Bad Orb; Kinderkurklinik Bad Sassendorf, Bad Sassendorf; AHG Klinik für Kinder und Jugendliche Beelitz-Heilstätten, Beelitz-Heilstätten; Edelsteinklinik Fachklinik für Kinder- und Jugendrehabilitation, Bruchweiler; Ostseestrand-Klinik “Klaus Störtebeker,” Kölpinsee; Klinik Hochried Fachklinik für Kinder und Jugendliche, Murnau; Fachklinik Satteldüne, Nebel/Amrum; Kinderkurheim Arnsberg, Norderney; Seehospiz Norderney GmbH Rehabilitationsklinik für Kinder und Jugendliche, Norderney; Fachklinik Prinzregent Luitpold, Scheidegg/Allgäu; Fachklinik Sylt für Kinder und Jugendliche, Westerland/Sylt; Waldburg-Zeil Kliniken Fachkliniken Wangen, Wangen/Allgäu. Correspondence concerning this article should be addressed to Matthias G. Klosinski, Gärtnerstraße 10, 86153 Augsburg, Germany. E-mail: [email protected] 1053
KLOSINSKI AND FARIN
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burg et al., 2002; Jedeloo, van Staa, Latour, & van Exel, 2010; Kyngäs, Skaar-Chandler, & Duffy, 2000; Litt & Cuskey, 1984; Shapiro, Welker, & Jacobson, 1997). To the best of our knowledge, there is currently no psychometrically tested instrument that was developed using confirmatory factor analysis and that focuses particularly on the communication preferences of chronically ill adolescents during patient-provider interaction. When using the term chronic illness, we refer to diseases of at least 6 months’ duration requiring continuous adaptation to the life conditions caused by a given disease (Kyngäs, Kroll, & Duffy, 2000; LeBlanc, Goldsmith, & Patel, 2003; Perrin et al., 1993). The aim of this investigation was to develop and psychometrically test just such an assessment instrument incorporating a theoretical basis and patient-oriented aspects. We also planned to describe the communication preferences thus identified.
Method Developmental Methodology This project used a mixed-method research design (Tashakkori & Creswell, 2007) combining qualitative and quantitative substudies (Britten, 2011). This approach is both considered appropriate for studying communication (van Staa & the On Your Own Feet Research Group, 2011) and for instrument development (O’Cathain, Murphy, & Nicholl, 2007). In an initial qualitative prestudy, we queried adolescents about their communication preferences in focus-group interviews (Agan, Koch, & Rumrill Jr, 2008; Rich & Ginsburg, 1999). On the basis of those results and incorporating findings from previous research, an initial questionnaire was generated, which was then subjected to comprehensibility and acceptance testing in a second pretest via individual cognitive interviews (Collins, 2003). In the quantitative main study, the questionnaire was psychometrically tested in a larger cohort. This developmental methodology resembles that used in devising an analogous instrument for adults (the KOPRA questionnaire; Farin, Gramm, & Kosiol, 2011). We therefore called the new questionnaire KOPRA-A (KOPRA standing for Kommunikationspräferenzen which means communication preferences in German, A standing for adolescents). Inclusion criteria for all three substudies were an age between 12 and 17 years and a chronic illness (for at least 6 months). All subjects were at study enrollment inpatients in German pediatric rehabilitation clinics. In Germany, patients with chronic conditions can be admitted to rehabilitation centers where multidisciplinary therapy is provided by various professional groups. Patients were not paid for their participation. 90.8% of the subjects reported German citizenship. All the prestudy subjects and their parents received study information and provided informed consent. Subjects and parents received study information and provided implied consent for the main study. All study protocols were submitted to our institutional ethics commission and granted approval (No. 320/11). All substudies were conducted by Matthias G. Klosinski.
Sample The five focus-group interviews were carried out with four to five patients per group leading to a total of 22 adolescents (M ⫽ 13.59 years; SD ⫽ 1.74); 36% of the participants were female.
Five adolescents (ages 12, 13, 14, 14, and 17; 60% female) took part in the cognitive interviews. In the main study, 14 institutions returned a total of 430 completed questionnaires. Seven responders were excluded for being under- or overage. Participants were questioned during inpatient rehabilitation. Table 1 illustrates the characteristics of the final 423 study participants.
Instruments Instrument development. In the focus-group interviews, the adolescents were motivated to express their communication preferences via an opening question: “What is important for you in conversation with a provider, what are your wishes and expectations, how would you like the provider to behave?” The interview proceeded in a semistructured format in which topics the participants had not yet discussed were mentioned by the interviewer to capture further preferences. The interviews were tape-recorded, transcribed, and submitted for content analysis. The verbal communication preferences were extracted by paying particular attention to capturing the subject’s actual choice of words as literally as possible. We then ranked these preferences by their frequency and categorized them into preference groups according to their content. Items were then generated depending on the frequency of mentions per preference group: a preference mentioned more than 10 times was transferred into three items; ⱖ5 and ⬍10 mentions: two items; ⱖ1 and ⬍5 mentions: one item. The idea behind using the mentions frequency to determine the number of items included in the initial instrument was our assumption that the more often the adolescents named a communication preference in the interviews, the more important they consider it, and that this sort of ranking should therefore be reflected in the number of items presented. This questionnaire draft was then given to five adolescents in individual cognitive interviews (Collins, 2003). Collins pointed out that “Cognitive testing should be a standard part of the development process of any survey instrument” (Collins, 2003, p. 229).
Table 1 Sample of the Main Study Sample
Results
Gender % female Average age (SD) German citizenship % Diagnosis % Primarily organic disease(s) At least one psychological illness Obesity No answer IIlness duration % ⬍1 year 1–2 years 2–5 years 5–10 years ⬎10 years Don’t know No answer Duration of stay % ⱕ8 days 8–21 days ⬎21 days No answer
57.4 14.3 (1.3) 90.8 27.4 21.7 47.0 3.9 6.9 12.5 22.5 15.8 16.5 22.2 3.5 33.1 32.6 31.7 2.6
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He suggested the technique of “thinking aloud” while reading and filling in the items, as well as the “probing technique” allowing the interviewer to ask the participant to explain his or her thoughts and considerations. These individual interviews were also taperecorded and led us to reformulate several items to enhance the questionnaire’s comprehensibility and acceptance. We were able to extract numerous preferences from the focus groups, forming 42 categories leading to 106 items. The most prominent communication preference expressed by the participants was the provider’s kindness, followed by the wish to be taken seriously as well as listened to. Final version of the instrument. The final questionnaire consisted of 106 statements graded according to their subjective importance (response categories: 1 ⫽ not that important; 2 ⫽ somewhat important; 3 ⫽ important; 4 ⫽ very important; 5 ⫽ extremely important), as well as three additional items at the end addressing overall acceptance (response categories stimulating, informative, boring, rather annoying), comprehensibility (entirely understandable, for the most part understandable, hardly understandable, not at all understandable) and subjective relevance (not that important, somewhat important, important, very important, extremely important) of the questionnaire to obtain a rough evaluation of the instrument. Analyses. We planned to remove all the items displaying more than 5% missing values (Schafer, 1999) or floor or ceiling effects (more than 50% of values in the extreme categories) and to exclude those who answered less than 85% of the items. We then performed an exploratory factor analysis with list-wise case exclusion with SPSS (version 20). To test the method’s assumptions, we carried out the Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett’s test of sphericity. We employed a principal components analysis with varimax rotation. In deciding on the number of extracted factors, we considered the Scree test results, interpretability and explained variance of at least 40% (Farin et al., 2011). We kept those items whose factor loading was ⱖ.55 on exactly one factor and ⬍.40 on all other factors, ⱖ.55 being a stricter standard than that often used (Cheung, Garratt, Russell, & Williams, 2000), in order to obtain high reliability and discriminative factors. In case we observed questions that were too similar in content among the extracted items, we only kept the item that loaded higher and most unequivocally on the given factor. In so doing, we aimed to avoid content redundancies and correlated error terms. Nunnally and Bernstein (1994) suggested that item-total correlations should not be ⬍.30. To increase the scales’ reliability, we were even stricter in the next step and removed all the items that revealed a corrected-item-scale correlation of ⬍.45. Missing data were fairly equally distributed across items (range 0.9%-3.3%). Only 29 adolescents (6.9%) had missing answers in more than 5% of the items. To determine the influence of missing data on our results, we compared the participants who had left out at least one item with those who showed one or more missing values. These two groups were subsequently tested for differences regarding age, gender, and nationality (German vs. not German), diagnosis and illness duration. Participants whose data were complete were significantly older (14.4 vs. 14.0 years) and more often German nationals (94% vs. 87%). There were no significant differences in the other variables. To investigate the influence of these differences on the scales’ creation, we performed a multiple
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imputation (iterative Markov Chain Monte Carlo method) and compared the assignment of the items to the factors in the exploratory factor analysis with the assignment in the five imputed data sets. There were discrepancies in only 8 of the 106 items. These discrepancies concerned only items whose factor loadings were ⬍.50 already in the original data set, so that none of those eight items appeared in the questionnaire’s final version. Thus there seems to be only a small influence of the missing data on the development of the questionnaire. The final step was to test each scale and a model with all factors via AMOS software (version 20) via the maximum likelihood method and confirmatory factor analysis for unidimensionality. To determine global model fit, we applied the Tucker-Lewis Index (TLI; Tucker & Lewis, 1973), Comparative Fit Index (CFI), standardized root mean residual (SRMR) and root mean square error of approximation (RMSEA). A good global model fit is revealed in TLI and CFI values of ⬎.95 and RMSEA values of ⬍.06. The SRMR value should lie ⬍.08 (Hu & Bentler, 1999). The scales’ reliability was assessed using Cronbach’s alpha. Values ⬎.70 are considered acceptable, values ⬎.80 good, and values ⬎.90 as an excellent indication of internal consistency (George & Mallery, 2002). To test construct validity we conducted the t test for mean value comparison in line with literature findings stating that girls rate being listened to as more important than boys, who rate the provider’s competence and honesty as being more relevant (van Staa et al., 2011).
Results Main Study The questionnaire was considered “entirely understandable” or “for the most part understandable” by 88.2% of our respondents; 46.8% found it “informative” or “stimulating.” In responding to the question “How important is it to you to be consulted about your preferences in such a format?”, 68.1% answered “important,” “very important,” or “extremely important.” The missing data at the item level ranged from 0.7% to 4.3%, thus none had to be excluded. Ten of the items revealed over 50% of values in the extreme categories—thus six items had to be excluded due to ceiling effects and four because of floor effects. No participant answered less than 85% of the items, so none had to be excluded for that reason. Our final data set thus comprised 423 study subjects and 96 preference items. Factor analysis. After conducting list-wise case exclusion, 286 subjects remained included. At .94, the Kaiser-Meyer-Olkin value was well above the .5 value required and is thus very good. The Bartlett test’s highly significant result demonstrates that the assumption of sphericity was also fulfilled (Bühner, 2010). The exploratory factor analysis result indicates a three-factor resolution; 41.8% of the total variance can be attributed to those three factors. The first factor explains 19.3% of the variance, the second another 14.9%, and the third 7.6%. Factor (i.e., Scale) 1 can be interpreted as the affective, emotionally supportive, and empathetic aspect of communication, that is, preferences such as the desire for friendliness, courtesy, understanding, being taken seriously, no prying, for patience, helpfulness, respect, and equal treatment (to that of other patients and the
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other gender), as well as preferences for being listened to closely without interruption, having enough time for a talk, and that the provider introduce him or herself at the first meeting. This scale will subsequently be characterized as the EAC, or emotionalaffective communication component. Factor (that is, Scale) 2 refers to the instrumental communication component, that is, the extensive, factual disclosure and retrieval of information about the disease and its afflictions, expectations with respect to the therapy and clinic stay, the patient’s own disease model, the taking of notes and consistent opportunity for consultation. This scale is referred to as the IC, or instrumental communication component. Factor/Scale 3 addresses the particular preferences about the provider’s interpersonal style and nature of interpersonal relationships that characterize adolescents: their wish for a provider whose personal approach is youthful in manner and easy-going,
one who is young and whose dress style is casual, and one who is familiar with what’s “in.” This scale will henceforth be referred to as the ASC, or adolescent-specific communication component, because this dimension reveals no correspondence in findings on adult communication preferences. After eliminating all the items that failed to attain a ⱖ.55 factor loading on exactly one factor and ⬍.40 on all other factors, we ended up with 30 items, three of which were excluded because their content had already been accounted for in other items. Table 2 illustrates the 27 items we retained, in addition to their allocation to scales and mean values, standard deviations and the corrected item-scale correlations. The latter lay consistently over .47, which is evidence of high internal consistency. Table 2 also illustrates the unidimensionality test results via confirmatory factor analysis and the global model-fit indices of the three scales as well as for the model with all three scales. Both
Table 2 Scales With Their Respective Items, Means, Standard Deviations, Corrected Item-Scale Correlations, Global Model-Fit Indices of Each Single Scale, and Complete Model as Well as Cronbach’s Alpha Scale EAC
IC
1 2 3 4 5 6
. . . . . .
7 8 9 10 11 12 13 14
. . . . . . . .
M 15 16 17 18 19 20 21 22
ASC
M 23 24 25 26 27 M
Complete model
My provider . . .
M
SD
CISC
TLI
CFI
RMSEA
SRMR
␣
␣SI
. is polite. . makes me feel I’m being listened to. . shows understanding of my situation. . doesn’t interrupt me when I’m speaking. . takes care to treat all patients fairly. . is patient with me when I’ve done something wrong. . . makes me feel I’m being taken seriously. . . is eager to help me. . . treats me with respect. . . is not pressed for time. . . is kind to me. . . sees that boys and girls are treated equally. . . introduced him or herself at our first meeting. . . doesn’t pry when I’d rather not share something.
3.96 3.99 3.99 3.94 3.92 3.98
0.93 0.97 0.94 1.07 1.07 0.95
.63 .70 .68 .60 .63 .71
.97
.97
.05
0.031
.92
.92
4.15 3.85 4.20 3.87 4.10 3.94 3.99 3.90
0.91 0.96 0.89 1.14 0.93 1.18 1.05 1.16
.73 .65 .72 .67 .74 .57 .59 .50
3.98 3.82
0.71 1.05
.53
3.56
1.11
.57
.97
.98
.05
0.032
.83
.83
3.66 3.26 2.53 3.02 3.78
1.07 1.17 1.30 1.20 1.08
.61 .58 .49 .49 .60
3.93
1.09
.61
3.44 2.47 2.35 2.52 2.65
0.76 1.36 1.30 1.36 1.40
.47 .58 .61 .59
.93
.96
.10
0.036
.78
.78
2.08 2.42
1.39 1.00
.91
.91
.05
0.072
Item no. . . . . . .
. . . lets me know what my therapy will be like once I’m released. . . . is not afraid of being open and honest with me when telling me something troubling about my disease. . . . asks me to describe all of my symptoms. . . . gives me the opportunity for regular talks. . . . takes notes during our talks. . . . asks me how I explain my disease to myself. . . . gives me factual information on my disease and its treatment. . . . gives me lots of information on my disease and its consequences. . . . .
. . . .
. is relaxed and dresses casually. . acts young in my presence. . treats me like a buddy. . is up to date on what’s in and what we kids like. . . . isn’t too old.
.55
Note. EAC ⫽ emotional-affective communication component; IC ⫽ instrumental communication component; ASC ⫽ adolescent-specific communication component; CISC ⫽ corrected item-scale correlations; TLI ⫽ Tucker-Lewis Index; CFI ⫽ Comparative Fit Index; RMSEA ⫽ root mean square error of approximation; SRMR ⫽ standardized root mean residual; ␣ ⫽ Cronbach’s alpha; ␣SI ⫽ Cronbach’s alpha for standardized items. Response categories: 1 ⫽ not that important; 2 ⫽ somewhat important; 3 ⫽ important; 4 ⫽ very important; 5 ⫽ extremely important.
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scales 1 and 2 reveal good global model fits with .97 and .98, respectively, as TLI and CFI values and RMSEA .05. Results for Factor 3 are slightly worse, however the CFI value is still .96 and SRMR .04, so that all factors can be considered unidimensional. Confirmatory factor analysis for a complete model with all three scales reveals a good model fit with RMSEA .05 and SRMR .07. TLI and CFI values are slightly worse with .91. Table 2 illustrates the three scales’ reliability analysis results: Scale 1 demonstrates excellent internal consistency (␣ ⫽ .92), Scale 2 good (␣ ⫽ .83), and Scale 3 acceptable (␣ ⫽ .78) reliability. T test for mean value comparison showed that the girls’ Factor 1 mean value is higher than that of boys (p ⫽ .009). Because Factor 1 contains the preference of being listened to (mentioned in previous findings as being preferred more by girls than by boys), our result can be considered an initial indication of construct validity for Scale 1. Factor 2 contains aspects like honesty and competence, however there were no significant differences between boys and girls in that aspect.
Discussion The chronically ill adolescents in the focus-group interviews named many communication preferences that enabled us to develop a questionnaire. These largely correspond to the preferences other investigators have mentioned (Britto et al., 2004; Dunsmore & Quine, 1996; Freake et al., 2007; Ginsburg et al., 2002; Moules, 2009; Quinn et al., 2011; Schaeuble et al., 2010; van Staa et al., 2011; Woodgate, 1998; Zwaanswijk et al., 2011), thus we have reasonable grounds to assume that a measuring instrument based on them will adequately reflect the substantial spectrum of communication preferences that adolescents express. Our evaluation of the questionnaires yielded 27 items in the main study that fall into three scales: the EAC, the IC, and the ASC. We are aware that the items in the EAC scale cover a rather heterogeneous field that could be subdivided in different aspects. We believe that calling it the “emotional-affective communication component” does not exclude any of them. The fact that these items could also represent the adolescent’s wish to be recognized and treated as a legitimate or equal partner in his or her own medical care does not question this designation but provides one possible explanation. Of course, many others might be valid as well, such as the need for emotional support (instead of the wish for equality and “partnership”), or incorporated cognitive concepts of patient-provider interactions. Because we do not know for certain, we believe that “emotional-affective communication component” is a suitable term by which to summarize and describe the category without “nailing” it excessively to specific underlying reasons. Although the first two scales address communication aspects that are covered in part in other questionnaires, the ASC scale addresses something that has, to the best of our knowledge, never been addressed in a psychometrically tested assessment instrument before, as such revealing that the desires of patients in this age group present providers with particular challenges. Our results show that adolescents rank highest in importance the items on the emotional-affective scale with a mean of 3.98 (4 meaning “very important”), followed by the instrumental components of communication with a mean of 3.44. In contrast, our patients considered
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less important the adolescent-specific communication component items, leading to a mean of only 2.42. As the EAC and IC scales yielded a good fit and the ASC scale a satisfactory fit in the confirmatory factor analyses, these scales can be considered unidimensional. The complete model integrating the three factors shows satisfactory fit in the confirmatory analysis. The EAC reveals excellent, the IC good, and the ASC acceptable internal consistence. We thus obtained good psychometric values from the KOPRA-A questionnaire. Our questionnaire displayed high comprehensibility and its relevance was judged at least as “important” by over two thirds of respondents. The fact that only 46.8% of the participants found the questionnaire to be “good” may have to do with its length, as the test version, with its 106 items, was obviously very long. We anticipate much better acceptance of the present form, which contains just 27 items. We are thus the first to have, via confirmatory factor analysis, devised a reliable, psychometrically tested assessment instrument to capture the communication preferences of chronically ill adolescents that is economical, easy to understand and use, and which responders judge to be relevant. Of course, the new questionnaire’s role is not to replace the personal contact between the patient and provider, but we believe it will contribute to simplifying and improving the communication between them by revealing the patients’ current communication preferences, thus giving the provider an impression of those wishes he or she will we be confronted with when dealing with a given patient. The questionnaire therefore may be especially helpful for adolescents who find it difficult to articulate their needs and communication preferences in one-to-one talks with clinicians. It is also conceivable that by filling out the questionnaire, some young people will be made conscious for the first time of their personal likes and dislikes, and will thus be better able to describe their goals and hopes not just concerning the interaction with their providers but regarding their entire contact with the clinic as well. Despite the strengths associated with mixed-method designs, our study has some limitations regarding the representativeness of our main study’s cohort. Although it consisted of roughly equal numbers of male and female patients at all the ages within the target range, and the disease durations and inpatient stays were also quite similar, the high percentage (47%) of young obese participants is conspicuous. This may have biased certain results, as some answers may have been diagnosis-specific. Furthermore, the generalizability of our results is diminished, as our patients originated only from rehabilitation clinics in Germany. Besides, we know neither why some adolescents did not participate, nor about the conditions under which the questionnaires were filled out. Finally, a severe limitation is the lack of evidence of construct validity. The significant difference between boys and girls concerning Factor 1 mean values are to be considered only a very preliminary hint of validity. The problem is that there are not yet any psychometrically tested, reliable scales and instruments assessing the communication preferences of chronically ill adolescents that could serve as adequate criteria of construct validity. Moreover, theory-based predictions are very difficult to extract from the current literature because of limited previous findings and the lack of precise conceptualization in this research domain. The goal of this article was to take an initial step in this direction, while
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stating clearly that we need to do more research to establish validity. Future investigations could also address the extent to which our questionnaire applies to other patient groups (e.g., the acutely ill or outpatients) and whether it requires diagnosis-specific adaptations. Other promising approaches worth pursuing in optimizing this questionnaire would be to differentiate according to the type of provider (physicians, nursing staff, therapists), its application in the e-health field, or the inclusion of the communication preferences of parents and providers. Better understanding the complex communication and roles in the adolescent–parent–provider triade seems to be a challenging and promising target of future investigation, particularly because adolescents seem to be often neglected in this constellation (Tates, Elbers, Meeuwesen, & Bensing, 2002; Tates, Meeuwesen, Bensing, & Elbers, 2002; Tates, Meeuwesen, Elbers, & Bensing, 2002).
References Agan, J., Koch, L. C., & Rumrill, P. D., Jr. (2008). The use of focus groups in rehabilitation research. Work (Reading, Mass.), 31, 259 –269. Bethell, C., Klein, J., & Peck, C. (2001). Assessing health system provision of adolescent preventive services: The Young Adult Health Care Survey. Medical Care, 39, 478 – 490. http://dx.doi.org/10.1097/00005650200105000-00008 Britten, N. (2011). Qualitative research on health communication: What can it contribute? Patient Education and Counseling, 82, 384 –388. http://dx.doi.org/10.1016/j.pec.2010.12.021 Britto, M. T., DeVellis, R. F., Hornung, R. W., DeFriese, G. H., Atherton, H. D., & Slap, G. B. (2004). Health care preferences and priorities of adolescents with chronic illnesses. Pediatrics, 114, 1272–1280. Britto, M. T., Slap, G. B., DeVellis, R. F., Hornung, R. W., Atherton, H. D., Knopf, J. M., & DeFriese, G. H. (2007). Specialists understanding of the health care preferences of chronically ill adolescents. Journal of Adolescent Health, 40, 334 –341. http://dx.doi.org/10.1016/j.jadohealth .2006.10.020 Bühner, M. (2010). Einführung in die Test- und Fragebogenkonstruktion (3., aktualisierte und erweiterte Auflage). München, Germany: Pearson Studium. Cheung, W. Y., Garratt, A. M., Russell, I. T., & Williams, J. G. (2000). The UK IBDQ: A British version of the inflammatory bowel disease questionnaire. Journal of Clinical Epidemiology, 53, 297–306. http://dx.doi .org/10.1016/S0895-4356(99)00152-3 Collins, D. (2003). Pretesting survey instruments: An overview of cognitive methods. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care & Rehabilitation, 12, 229 – 238. http://dx.doi.org/10.1023/A:1023254226592 Coyne, I. (2008). Children’s participation in consultations and decisionmaking at health service level: A review of the literature. International Journal of Nursing Studies, 45, 1682–1689. http://dx.doi.org/10.1016/j .ijnurstu.2008.05.002 Dunsmore, J., & Quine, S. (1996). Information, support, and decisionmaking needs and preferences of adolescents with cancer. Journal of Psychosocial Oncology, 13, 39 –56. http://dx.doi.org/10.1300/ J077V13N04_03 Epstein, R. M., Franks, P., Fiscella, K., Shields, C. G., Meldrum, S. C., Kravitz, R. L., & Duberstein, P. R. (2005). Measuring patient-centered communication in patient-physician consultations: Theoretical and practical issues. Social Science & Medicine, 61, 1516 –1528. http://dx.doi .org/10.1016/j.socscimed.2005.02.001 Farin, E., Gramm, L., & Kosiol, D. (2011). Development of a questionnaire to assess communication preferences of patients with chronic illness.
Patient Education and Counseling, 82, 81– 88. http://dx.doi.org/ 10.1016/j.pec.2010.02.011 Freake, H., Barley, V., & Kent, G. (2007). Adolescents’ views of helping professionals: A review of the literature. Journal of Adolescence, 30, 639 – 653. http://dx.doi.org/10.1016/j.adolescence.2006.06.001 Garland, A. F., Saltzman, M. D., & Aarons, G. A. (2000). Adolescent satisfaction with mental health services: Development of a multidimensional scale. Evaluation and Program Planning, 23, 165–175. http://dx .doi.org/10.1016/S0149-7189(00)00009-4 George, D., & Mallery, P. (2002). SPSS for windows step by step: A simple guide and reference, 11.0 update (4th ed.). Boston, MA: Allyn & Bacon. Ginsburg, K. R., Forke, C. M., Cnaan, A., & Slap, G. B. (2002). Important health provider characteristics: The perspective of urban ninth graders. Journal of Developmental and Behavioral Pediatrics, 23, 237–243. http://dx.doi.org/10.1097/00004703-200208000-00008 Ginsburg, K. R., Slap, G. B., Cnaan, A., Forke, C. M., Balsley, C. M., & Rouselle, D. M. (1995). Adolescents’ perceptions of factors affecting their decisions to seek health care. Journal of the American Medical Association, 273, 1913–1918. http://dx.doi.org/10.1001/jama.1995 .03520480033036 Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. http://dx.doi.org/10.1080/ 10705519909540118 Institute of Medicine. (2001). Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press. Jedeloo, S., van Staa, A., Latour, J. M., & van Exel, N. J. (2010). Preferences for health care and self-management among Dutch adolescents with chronic conditions: A Q-methodological investigation. International Journal of Nursing Studies, 47, 593– 603. http://dx.doi.org/ 10.1016/j.ijnurstu.2009.10.006 Knopf, J. M., Hornung, R. W., Slap, G. B., DeVellis, R. F., & Britto, M. T. (2008). Views of treatment decision making from adolescents with chronic illnesses and their parents: A pilot study. Health Expectations: An International Journal of Public Participation in Health Care & Health Policy, 11, 343–354. http://dx.doi.org/10.1111/j.1369-7625.2008 .00508.x Kyngäs, H. (2000). Compliance of adolescents with chronic disease. Journal of Clinical Nursing, 9, 549 –556. http://dx.doi.org/10.1046/j.13652702.2000.00368.x Kyngäs, H. A., Kroll, T., & Duffy, M. E. (2000). Compliance in adolescents with chronic diseases: A review. Journal of Adolescent Health, 26, 379 –388. Kyngäs, H. A., Skaar-Chandler, C. A., & Duffy, M. E. (2000). The development of an instrument to measure the compliance of adolescents with a chronic disease. Journal of Advanced Nursing, 32, 1499 –1506. http://dx.doi.org/10.1046/j.1365-2648.2000.01611.x LeBlanc, L. A., Goldsmith, T., & Patel, D. R. (2003). Behavioral aspects of chronic illness in children and adolescents. Pediatric Clinics of North America, 50, 859 – 878. http://dx.doi.org/10.1016/S0031-3955(03)00072-5 Litt, I. F., & Cuskey, W. R. (1984). Satisfaction with health care. A predictor of adolescents’ appointment keeping. Journal of Adolescent Health Care, 5, 196 –200. http://dx.doi.org/10.1016/S0197-0070(84)80042-X Moules, T. (2009). “They wouldn’t know how it feels . . .”: characteristics of quality care from young people’s perspectives: A participatory research project. Journal of Child Health Care, 13, 322–332. http://dx.doi .org/10.1177/1367493509344824 Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill. O’Cathain, A., Murphy, E., & Nicholl, J. (2007). Why, and how, mixed methods research is undertaken in health services research in England: A mixed methods study. BMC Health Services Research, 7, 85. http://dx .doi.org/10.1186/1472-6963-7-85
This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
COMMUNICATION PREFERENCES Perrin, E. C., Newacheck, P., Pless, I. B., Drotar, D., Gortmaker, S. L., Leventhal, J., . . . Weitzman, M. (1993). Issues involved in the definition and classification of chronic health conditions. Pediatrics, 91, 787–793. Quinn, G. P., Murphy, D., Knapp, C., Stearsman, D. K., Bradley-Klug, K. L., Sawczyn, K., & Clayman, M. L. (2011). Who decides? Decision making and fertility preservation in teens with cancer: A review of the literature. Journal of Adolescent Health, 49, 337–346. http://dx.doi.org/ 10.1016/j.jadohealth.2011.01.005 Rich, M., & Ginsburg, K. R. (1999). The reason and rhyme of qualitative research: Why, when, and how to use qualitative methods in the study of adolescent health. Journal of Adolescent Health, 25, 371–378. http://dx .doi.org/10.1016/S1054-139X(99)00068-3 Rodin, G., Mackay, J. A., Zimmermann, C., Mayer, C., Howell, D., Katz, M., . . . Brouwers, M. (2009). Clinician–patient communication: A systematic review. Supportive Care in Cancer, 17, 627– 644. http://dx .doi.org/10.1007/s00520-009-0601-y Schaeuble, K., Haglund, K., & Vukovich, M. (2010). Adolescents’ preferences for primary care provider interactions. Journal for Specialists in Pediatric Nursing, 15, 202–210. http://dx.doi.org/10.1111/j.1744-6155 .2010.00232.x Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8, 3–15. http://dx.doi.org/10.1191/096228099671525676 Shapiro, J. P., Welker, C. J., & Jacobson, B. J. (1997). The Youth Client Satisfaction Questionnaire: Development, construct validation, and factor structure. Journal of Clinical Child Psychology, 26, 87–98. http://dx .doi.org/10.1207/s15374424jccp2601_9 Swedlund, M. P., Schumacher, J. B., Young, H. N., & Cox, E. D. (2012). Effect of communication style and physician-family relationships on satisfaction with pediatric chronic disease care. Health Communication, 27, 498 –505. http://dx.doi.org/10.1080/10410236.2011.616632 Tashakkori, A., & Creswell, J. W. (2007). Editorial: The new era of mixed methods. Journal of Mixed Methods Research, 1, 3–7. http://dx.doi.org/ 10.1177/2345678906293042 Tates, K., Elbers, E., Meeuwesen, L., & Bensing, J. (2002). Doctor–parent– child relationships: A “pas de trois.” Patient Education and Counseling, 48, 5–14. http://dx.doi.org/10.1016/S0738-3991(02)00093-9
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Tates, K., Meeuwesen, L., Bensing, J., & Elbers, E. (2002). Joking or decision-Making? Affective and instrumental behaviour in doctor– parent– child communication. Psychology & Health, 17, 281–295. http://dx.doi.org/10.1080/08870440290029548 Tates, K., Meeuwesen, L., Elbers, E., & Bensing, J. (2002). “I’ve come for his throat”: Roles and identities in doctor-parent-child communication. Child: Care, Health and Development, 28, 109 –116. http://dx.doi.org/ 10.1046/j.1365-2214.2002.00248.x Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10. http://dx.doi.org/ 10.1007/BF02291170 van Staa, A., Jedeloo, S., & van der Stege, H., & the On Your Own Feet Research Group. (2011). “What we want”: Chronically ill adolescents’ preferences and priorities for improving health care. Patient Preference and Adherence, 5, 291–305. http://dx.doi.org/10.2147/PPA.S17184 van Staa, A., & the On Your Own Feet Research Group. (2011). Unraveling triadic communication in hospital consultations with adolescents with chronic conditions: The added value of mixed methods research. Patient Education and Counseling, 82, 455– 464. http://dx.doi.org/ 10.1016/j.pec.2010.12.001 WMA declaration of Ottawa on child health. (1998). Retrieved from http://www.wma.net/en/30publications/10policies/c4/ Woodgate, R. L. (1998). Health professionals caring for chronically ill adolescents: Adolescents’ perspectives. Journal for Specialists in Pediatric Nursing, 3, 57– 68. http://dx.doi.org/10.1111/j.1744-6155.1998 .tb00029.x Zwaanswijk, M., Tates, K., van Dulmen, S., Hoogerbrugge, P. M., Kamps, W. A., Beishuizen, A., & Bensing, J. M. (2011). Communicating with child patients in pediatric oncology consultations: A vignette study on child patients’, parents’, and survivors’ communication preferences. Psycho-Oncology, 20, 269 –277. http://dx.doi.org/10.1002/pon.1721
Received June 10, 2014 Revision received November 3, 2014 Accepted December 4, 2014 䡲
© 2015 American Psychological Association 1040-3590/15/$12.00 http://dx.doi.org/10.1037/a0038699
Communication Preferences of Chronically Ill Adolescents: Development of an Assessment Instrument Matthias G. Klosinski and Erik Farin
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University of Freiburg The purpose of this study was to develop and psychometrically test a patient-oriented, theory-based questionnaire to capture the communication preferences of chronically ill adolescents in provider–patient interaction. In a qualitative prestudy, patients were asked to express their preferences in focus groups. From those results and relying on previous research findings, we generated questionnaire items and in a second pretest, examined them in 1-to-1 cognitive interviews for comprehensibility and acceptance. The resultant questionnaire was then psychometrically tested in the main study on 423 chronically ill inpatient adolescents aged 12 to 17 years in 14 rehabilitation clinics in Germany. Numerous preferences were extractable from the focus-group interviews and transferred into 106 Items. Psychometric testing of the questionnaire resulted in 3 scales encompassing 27 items. These we describe as the emotional-affective communication component (EAC), instrumental communication component (IC), and adolescentspecific communication component (ASC). Confirmatory factor analysis revealed the scales EAC und IC to be good to very good, and the ASC scale as satisfactory regarding unidimensionality. The participants gave the questionnaire high marks for comprehensibility, acceptance, and relevance. The 3 scales’ Cronbach’s alpha falls between .78 and .92. A questionnaire with 27 items is now available for application as a psychometrically tested and simple-to-use measuring instrument. Research is still needed concerning the generalizability to other patient groups (e.g., the acutely ill or outpatients) and whether it can be tailored for use by different types of care providers or to accommodate the communication preferences of parents. Keywords: assessment instrument development, communication preferences, patient participation, provider–patient communication
and interindividually (Rodin et al., 2009). To accommodate optimally a patient’s current preferences, they should be assessed individually, for there is evidence that providers often fail to accurately assess those of adolescents (Britto et al., 2007). Such assessment can also contribute to meeting the demands for more patient-centered therapy (Institute of Medicine, 2001). This is especially true of children and adolescents, whose participation rights (WMA Declaration of Ottawa on Child Health, 1998) in terms of patient-provider interaction are often rather neglected (Coyne, 2008). Many investigations have addressed the communication preferences of ill adolescents. In their review, Freake, Barley, and Kent (2007) described numerous preferences, for example, the desire on the part of children and adolescents for confidentiality, information, to be listened to, and that their providers be kind, caring, and understanding. Others report preferences such as the wish for greater participation (Britto et al., 2004; Dunsmore & Quine, 1996; Moules, 2009; Quinn et al., 2011; Schaeuble, Haglund, & Vukovich, 2010; Van Staa, Jedeloo, van der Stege, & the On Your Own Feet Research Group, 2011; Zwaanswijk et al., 2011), equality (Ginsburg, Forke, Cnaan, & Slap, 2002; Woodgate, 1998), and humor (Britto et al., 2004; Woodgate, 1998). There are several assessment instruments that partially capture communication preferences while primarily attempting to incorporate other constructs such as compliance, satisfaction, or health care preferences (Bethell, Klein, & Peck, 2001; Britto et al., 2004; Garland, Saltzman, & Aarons, 2000; Ginsburg et al., 1995; Gins-
Successful communication between patient and provider plays a key role in endpoints such as satisfaction, adherence and therapeutic success (Knopf, Hornung, Slap, DeVellis, & Britto, 2008; Kyngäs, 2000; Swedlund, Schumacher, Young, & Cox, 2012). It is characterized by the best possible fit between the patient’s communication preferences and the provider’s expectations (Epstein et al., 2005). Yet communication preferences vary profoundly intra-
This article was published Online First March 16, 2015. Matthias G. Klosinski and Erik Farin, Institute for Quality Management and Social Medicine, University of Freiburg Medical Center. The manuscript is based on data of a not yet published doctoral dissertation. We thank the participating rehabilitation centers: Klinik Bad Gottleuba GmbH & Co, Bad Gottleuba; Viktoriastift Bad Kreuznach, Bad Kreuznach; Spessart-Klinik Bad Orb GmbH, Bad Orb; Kinderkurklinik Bad Sassendorf, Bad Sassendorf; AHG Klinik für Kinder und Jugendliche Beelitz-Heilstätten, Beelitz-Heilstätten; Edelsteinklinik Fachklinik für Kinder- und Jugendrehabilitation, Bruchweiler; Ostseestrand-Klinik “Klaus Störtebeker,” Kölpinsee; Klinik Hochried Fachklinik für Kinder und Jugendliche, Murnau; Fachklinik Satteldüne, Nebel/Amrum; Kinderkurheim Arnsberg, Norderney; Seehospiz Norderney GmbH Rehabilitationsklinik für Kinder und Jugendliche, Norderney; Fachklinik Prinzregent Luitpold, Scheidegg/Allgäu; Fachklinik Sylt für Kinder und Jugendliche, Westerland/Sylt; Waldburg-Zeil Kliniken Fachkliniken Wangen, Wangen/Allgäu. Correspondence concerning this article should be addressed to Matthias G. Klosinski, Gärtnerstraße 10, 86153 Augsburg, Germany. E-mail: [email protected] 1053
KLOSINSKI AND FARIN
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burg et al., 2002; Jedeloo, van Staa, Latour, & van Exel, 2010; Kyngäs, Skaar-Chandler, & Duffy, 2000; Litt & Cuskey, 1984; Shapiro, Welker, & Jacobson, 1997). To the best of our knowledge, there is currently no psychometrically tested instrument that was developed using confirmatory factor analysis and that focuses particularly on the communication preferences of chronically ill adolescents during patient-provider interaction. When using the term chronic illness, we refer to diseases of at least 6 months’ duration requiring continuous adaptation to the life conditions caused by a given disease (Kyngäs, Kroll, & Duffy, 2000; LeBlanc, Goldsmith, & Patel, 2003; Perrin et al., 1993). The aim of this investigation was to develop and psychometrically test just such an assessment instrument incorporating a theoretical basis and patient-oriented aspects. We also planned to describe the communication preferences thus identified.
Method Developmental Methodology This project used a mixed-method research design (Tashakkori & Creswell, 2007) combining qualitative and quantitative substudies (Britten, 2011). This approach is both considered appropriate for studying communication (van Staa & the On Your Own Feet Research Group, 2011) and for instrument development (O’Cathain, Murphy, & Nicholl, 2007). In an initial qualitative prestudy, we queried adolescents about their communication preferences in focus-group interviews (Agan, Koch, & Rumrill Jr, 2008; Rich & Ginsburg, 1999). On the basis of those results and incorporating findings from previous research, an initial questionnaire was generated, which was then subjected to comprehensibility and acceptance testing in a second pretest via individual cognitive interviews (Collins, 2003). In the quantitative main study, the questionnaire was psychometrically tested in a larger cohort. This developmental methodology resembles that used in devising an analogous instrument for adults (the KOPRA questionnaire; Farin, Gramm, & Kosiol, 2011). We therefore called the new questionnaire KOPRA-A (KOPRA standing for Kommunikationspräferenzen which means communication preferences in German, A standing for adolescents). Inclusion criteria for all three substudies were an age between 12 and 17 years and a chronic illness (for at least 6 months). All subjects were at study enrollment inpatients in German pediatric rehabilitation clinics. In Germany, patients with chronic conditions can be admitted to rehabilitation centers where multidisciplinary therapy is provided by various professional groups. Patients were not paid for their participation. 90.8% of the subjects reported German citizenship. All the prestudy subjects and their parents received study information and provided informed consent. Subjects and parents received study information and provided implied consent for the main study. All study protocols were submitted to our institutional ethics commission and granted approval (No. 320/11). All substudies were conducted by Matthias G. Klosinski.
Sample The five focus-group interviews were carried out with four to five patients per group leading to a total of 22 adolescents (M ⫽ 13.59 years; SD ⫽ 1.74); 36% of the participants were female.
Five adolescents (ages 12, 13, 14, 14, and 17; 60% female) took part in the cognitive interviews. In the main study, 14 institutions returned a total of 430 completed questionnaires. Seven responders were excluded for being under- or overage. Participants were questioned during inpatient rehabilitation. Table 1 illustrates the characteristics of the final 423 study participants.
Instruments Instrument development. In the focus-group interviews, the adolescents were motivated to express their communication preferences via an opening question: “What is important for you in conversation with a provider, what are your wishes and expectations, how would you like the provider to behave?” The interview proceeded in a semistructured format in which topics the participants had not yet discussed were mentioned by the interviewer to capture further preferences. The interviews were tape-recorded, transcribed, and submitted for content analysis. The verbal communication preferences were extracted by paying particular attention to capturing the subject’s actual choice of words as literally as possible. We then ranked these preferences by their frequency and categorized them into preference groups according to their content. Items were then generated depending on the frequency of mentions per preference group: a preference mentioned more than 10 times was transferred into three items; ⱖ5 and ⬍10 mentions: two items; ⱖ1 and ⬍5 mentions: one item. The idea behind using the mentions frequency to determine the number of items included in the initial instrument was our assumption that the more often the adolescents named a communication preference in the interviews, the more important they consider it, and that this sort of ranking should therefore be reflected in the number of items presented. This questionnaire draft was then given to five adolescents in individual cognitive interviews (Collins, 2003). Collins pointed out that “Cognitive testing should be a standard part of the development process of any survey instrument” (Collins, 2003, p. 229).
Table 1 Sample of the Main Study Sample
Results
Gender % female Average age (SD) German citizenship % Diagnosis % Primarily organic disease(s) At least one psychological illness Obesity No answer IIlness duration % ⬍1 year 1–2 years 2–5 years 5–10 years ⬎10 years Don’t know No answer Duration of stay % ⱕ8 days 8–21 days ⬎21 days No answer
57.4 14.3 (1.3) 90.8 27.4 21.7 47.0 3.9 6.9 12.5 22.5 15.8 16.5 22.2 3.5 33.1 32.6 31.7 2.6
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COMMUNICATION PREFERENCES
He suggested the technique of “thinking aloud” while reading and filling in the items, as well as the “probing technique” allowing the interviewer to ask the participant to explain his or her thoughts and considerations. These individual interviews were also taperecorded and led us to reformulate several items to enhance the questionnaire’s comprehensibility and acceptance. We were able to extract numerous preferences from the focus groups, forming 42 categories leading to 106 items. The most prominent communication preference expressed by the participants was the provider’s kindness, followed by the wish to be taken seriously as well as listened to. Final version of the instrument. The final questionnaire consisted of 106 statements graded according to their subjective importance (response categories: 1 ⫽ not that important; 2 ⫽ somewhat important; 3 ⫽ important; 4 ⫽ very important; 5 ⫽ extremely important), as well as three additional items at the end addressing overall acceptance (response categories stimulating, informative, boring, rather annoying), comprehensibility (entirely understandable, for the most part understandable, hardly understandable, not at all understandable) and subjective relevance (not that important, somewhat important, important, very important, extremely important) of the questionnaire to obtain a rough evaluation of the instrument. Analyses. We planned to remove all the items displaying more than 5% missing values (Schafer, 1999) or floor or ceiling effects (more than 50% of values in the extreme categories) and to exclude those who answered less than 85% of the items. We then performed an exploratory factor analysis with list-wise case exclusion with SPSS (version 20). To test the method’s assumptions, we carried out the Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett’s test of sphericity. We employed a principal components analysis with varimax rotation. In deciding on the number of extracted factors, we considered the Scree test results, interpretability and explained variance of at least 40% (Farin et al., 2011). We kept those items whose factor loading was ⱖ.55 on exactly one factor and ⬍.40 on all other factors, ⱖ.55 being a stricter standard than that often used (Cheung, Garratt, Russell, & Williams, 2000), in order to obtain high reliability and discriminative factors. In case we observed questions that were too similar in content among the extracted items, we only kept the item that loaded higher and most unequivocally on the given factor. In so doing, we aimed to avoid content redundancies and correlated error terms. Nunnally and Bernstein (1994) suggested that item-total correlations should not be ⬍.30. To increase the scales’ reliability, we were even stricter in the next step and removed all the items that revealed a corrected-item-scale correlation of ⬍.45. Missing data were fairly equally distributed across items (range 0.9%-3.3%). Only 29 adolescents (6.9%) had missing answers in more than 5% of the items. To determine the influence of missing data on our results, we compared the participants who had left out at least one item with those who showed one or more missing values. These two groups were subsequently tested for differences regarding age, gender, and nationality (German vs. not German), diagnosis and illness duration. Participants whose data were complete were significantly older (14.4 vs. 14.0 years) and more often German nationals (94% vs. 87%). There were no significant differences in the other variables. To investigate the influence of these differences on the scales’ creation, we performed a multiple
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imputation (iterative Markov Chain Monte Carlo method) and compared the assignment of the items to the factors in the exploratory factor analysis with the assignment in the five imputed data sets. There were discrepancies in only 8 of the 106 items. These discrepancies concerned only items whose factor loadings were ⬍.50 already in the original data set, so that none of those eight items appeared in the questionnaire’s final version. Thus there seems to be only a small influence of the missing data on the development of the questionnaire. The final step was to test each scale and a model with all factors via AMOS software (version 20) via the maximum likelihood method and confirmatory factor analysis for unidimensionality. To determine global model fit, we applied the Tucker-Lewis Index (TLI; Tucker & Lewis, 1973), Comparative Fit Index (CFI), standardized root mean residual (SRMR) and root mean square error of approximation (RMSEA). A good global model fit is revealed in TLI and CFI values of ⬎.95 and RMSEA values of ⬍.06. The SRMR value should lie ⬍.08 (Hu & Bentler, 1999). The scales’ reliability was assessed using Cronbach’s alpha. Values ⬎.70 are considered acceptable, values ⬎.80 good, and values ⬎.90 as an excellent indication of internal consistency (George & Mallery, 2002). To test construct validity we conducted the t test for mean value comparison in line with literature findings stating that girls rate being listened to as more important than boys, who rate the provider’s competence and honesty as being more relevant (van Staa et al., 2011).
Results Main Study The questionnaire was considered “entirely understandable” or “for the most part understandable” by 88.2% of our respondents; 46.8% found it “informative” or “stimulating.” In responding to the question “How important is it to you to be consulted about your preferences in such a format?”, 68.1% answered “important,” “very important,” or “extremely important.” The missing data at the item level ranged from 0.7% to 4.3%, thus none had to be excluded. Ten of the items revealed over 50% of values in the extreme categories—thus six items had to be excluded due to ceiling effects and four because of floor effects. No participant answered less than 85% of the items, so none had to be excluded for that reason. Our final data set thus comprised 423 study subjects and 96 preference items. Factor analysis. After conducting list-wise case exclusion, 286 subjects remained included. At .94, the Kaiser-Meyer-Olkin value was well above the .5 value required and is thus very good. The Bartlett test’s highly significant result demonstrates that the assumption of sphericity was also fulfilled (Bühner, 2010). The exploratory factor analysis result indicates a three-factor resolution; 41.8% of the total variance can be attributed to those three factors. The first factor explains 19.3% of the variance, the second another 14.9%, and the third 7.6%. Factor (i.e., Scale) 1 can be interpreted as the affective, emotionally supportive, and empathetic aspect of communication, that is, preferences such as the desire for friendliness, courtesy, understanding, being taken seriously, no prying, for patience, helpfulness, respect, and equal treatment (to that of other patients and the
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other gender), as well as preferences for being listened to closely without interruption, having enough time for a talk, and that the provider introduce him or herself at the first meeting. This scale will subsequently be characterized as the EAC, or emotionalaffective communication component. Factor (that is, Scale) 2 refers to the instrumental communication component, that is, the extensive, factual disclosure and retrieval of information about the disease and its afflictions, expectations with respect to the therapy and clinic stay, the patient’s own disease model, the taking of notes and consistent opportunity for consultation. This scale is referred to as the IC, or instrumental communication component. Factor/Scale 3 addresses the particular preferences about the provider’s interpersonal style and nature of interpersonal relationships that characterize adolescents: their wish for a provider whose personal approach is youthful in manner and easy-going,
one who is young and whose dress style is casual, and one who is familiar with what’s “in.” This scale will henceforth be referred to as the ASC, or adolescent-specific communication component, because this dimension reveals no correspondence in findings on adult communication preferences. After eliminating all the items that failed to attain a ⱖ.55 factor loading on exactly one factor and ⬍.40 on all other factors, we ended up with 30 items, three of which were excluded because their content had already been accounted for in other items. Table 2 illustrates the 27 items we retained, in addition to their allocation to scales and mean values, standard deviations and the corrected item-scale correlations. The latter lay consistently over .47, which is evidence of high internal consistency. Table 2 also illustrates the unidimensionality test results via confirmatory factor analysis and the global model-fit indices of the three scales as well as for the model with all three scales. Both
Table 2 Scales With Their Respective Items, Means, Standard Deviations, Corrected Item-Scale Correlations, Global Model-Fit Indices of Each Single Scale, and Complete Model as Well as Cronbach’s Alpha Scale EAC
IC
1 2 3 4 5 6
. . . . . .
7 8 9 10 11 12 13 14
. . . . . . . .
M 15 16 17 18 19 20 21 22
ASC
M 23 24 25 26 27 M
Complete model
My provider . . .
M
SD
CISC
TLI
CFI
RMSEA
SRMR
␣
␣SI
. is polite. . makes me feel I’m being listened to. . shows understanding of my situation. . doesn’t interrupt me when I’m speaking. . takes care to treat all patients fairly. . is patient with me when I’ve done something wrong. . . makes me feel I’m being taken seriously. . . is eager to help me. . . treats me with respect. . . is not pressed for time. . . is kind to me. . . sees that boys and girls are treated equally. . . introduced him or herself at our first meeting. . . doesn’t pry when I’d rather not share something.
3.96 3.99 3.99 3.94 3.92 3.98
0.93 0.97 0.94 1.07 1.07 0.95
.63 .70 .68 .60 .63 .71
.97
.97
.05
0.031
.92
.92
4.15 3.85 4.20 3.87 4.10 3.94 3.99 3.90
0.91 0.96 0.89 1.14 0.93 1.18 1.05 1.16
.73 .65 .72 .67 .74 .57 .59 .50
3.98 3.82
0.71 1.05
.53
3.56
1.11
.57
.97
.98
.05
0.032
.83
.83
3.66 3.26 2.53 3.02 3.78
1.07 1.17 1.30 1.20 1.08
.61 .58 .49 .49 .60
3.93
1.09
.61
3.44 2.47 2.35 2.52 2.65
0.76 1.36 1.30 1.36 1.40
.47 .58 .61 .59
.93
.96
.10
0.036
.78
.78
2.08 2.42
1.39 1.00
.91
.91
.05
0.072
Item no. . . . . . .
. . . lets me know what my therapy will be like once I’m released. . . . is not afraid of being open and honest with me when telling me something troubling about my disease. . . . asks me to describe all of my symptoms. . . . gives me the opportunity for regular talks. . . . takes notes during our talks. . . . asks me how I explain my disease to myself. . . . gives me factual information on my disease and its treatment. . . . gives me lots of information on my disease and its consequences. . . . .
. . . .
. is relaxed and dresses casually. . acts young in my presence. . treats me like a buddy. . is up to date on what’s in and what we kids like. . . . isn’t too old.
.55
Note. EAC ⫽ emotional-affective communication component; IC ⫽ instrumental communication component; ASC ⫽ adolescent-specific communication component; CISC ⫽ corrected item-scale correlations; TLI ⫽ Tucker-Lewis Index; CFI ⫽ Comparative Fit Index; RMSEA ⫽ root mean square error of approximation; SRMR ⫽ standardized root mean residual; ␣ ⫽ Cronbach’s alpha; ␣SI ⫽ Cronbach’s alpha for standardized items. Response categories: 1 ⫽ not that important; 2 ⫽ somewhat important; 3 ⫽ important; 4 ⫽ very important; 5 ⫽ extremely important.
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scales 1 and 2 reveal good global model fits with .97 and .98, respectively, as TLI and CFI values and RMSEA .05. Results for Factor 3 are slightly worse, however the CFI value is still .96 and SRMR .04, so that all factors can be considered unidimensional. Confirmatory factor analysis for a complete model with all three scales reveals a good model fit with RMSEA .05 and SRMR .07. TLI and CFI values are slightly worse with .91. Table 2 illustrates the three scales’ reliability analysis results: Scale 1 demonstrates excellent internal consistency (␣ ⫽ .92), Scale 2 good (␣ ⫽ .83), and Scale 3 acceptable (␣ ⫽ .78) reliability. T test for mean value comparison showed that the girls’ Factor 1 mean value is higher than that of boys (p ⫽ .009). Because Factor 1 contains the preference of being listened to (mentioned in previous findings as being preferred more by girls than by boys), our result can be considered an initial indication of construct validity for Scale 1. Factor 2 contains aspects like honesty and competence, however there were no significant differences between boys and girls in that aspect.
Discussion The chronically ill adolescents in the focus-group interviews named many communication preferences that enabled us to develop a questionnaire. These largely correspond to the preferences other investigators have mentioned (Britto et al., 2004; Dunsmore & Quine, 1996; Freake et al., 2007; Ginsburg et al., 2002; Moules, 2009; Quinn et al., 2011; Schaeuble et al., 2010; van Staa et al., 2011; Woodgate, 1998; Zwaanswijk et al., 2011), thus we have reasonable grounds to assume that a measuring instrument based on them will adequately reflect the substantial spectrum of communication preferences that adolescents express. Our evaluation of the questionnaires yielded 27 items in the main study that fall into three scales: the EAC, the IC, and the ASC. We are aware that the items in the EAC scale cover a rather heterogeneous field that could be subdivided in different aspects. We believe that calling it the “emotional-affective communication component” does not exclude any of them. The fact that these items could also represent the adolescent’s wish to be recognized and treated as a legitimate or equal partner in his or her own medical care does not question this designation but provides one possible explanation. Of course, many others might be valid as well, such as the need for emotional support (instead of the wish for equality and “partnership”), or incorporated cognitive concepts of patient-provider interactions. Because we do not know for certain, we believe that “emotional-affective communication component” is a suitable term by which to summarize and describe the category without “nailing” it excessively to specific underlying reasons. Although the first two scales address communication aspects that are covered in part in other questionnaires, the ASC scale addresses something that has, to the best of our knowledge, never been addressed in a psychometrically tested assessment instrument before, as such revealing that the desires of patients in this age group present providers with particular challenges. Our results show that adolescents rank highest in importance the items on the emotional-affective scale with a mean of 3.98 (4 meaning “very important”), followed by the instrumental components of communication with a mean of 3.44. In contrast, our patients considered
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less important the adolescent-specific communication component items, leading to a mean of only 2.42. As the EAC and IC scales yielded a good fit and the ASC scale a satisfactory fit in the confirmatory factor analyses, these scales can be considered unidimensional. The complete model integrating the three factors shows satisfactory fit in the confirmatory analysis. The EAC reveals excellent, the IC good, and the ASC acceptable internal consistence. We thus obtained good psychometric values from the KOPRA-A questionnaire. Our questionnaire displayed high comprehensibility and its relevance was judged at least as “important” by over two thirds of respondents. The fact that only 46.8% of the participants found the questionnaire to be “good” may have to do with its length, as the test version, with its 106 items, was obviously very long. We anticipate much better acceptance of the present form, which contains just 27 items. We are thus the first to have, via confirmatory factor analysis, devised a reliable, psychometrically tested assessment instrument to capture the communication preferences of chronically ill adolescents that is economical, easy to understand and use, and which responders judge to be relevant. Of course, the new questionnaire’s role is not to replace the personal contact between the patient and provider, but we believe it will contribute to simplifying and improving the communication between them by revealing the patients’ current communication preferences, thus giving the provider an impression of those wishes he or she will we be confronted with when dealing with a given patient. The questionnaire therefore may be especially helpful for adolescents who find it difficult to articulate their needs and communication preferences in one-to-one talks with clinicians. It is also conceivable that by filling out the questionnaire, some young people will be made conscious for the first time of their personal likes and dislikes, and will thus be better able to describe their goals and hopes not just concerning the interaction with their providers but regarding their entire contact with the clinic as well. Despite the strengths associated with mixed-method designs, our study has some limitations regarding the representativeness of our main study’s cohort. Although it consisted of roughly equal numbers of male and female patients at all the ages within the target range, and the disease durations and inpatient stays were also quite similar, the high percentage (47%) of young obese participants is conspicuous. This may have biased certain results, as some answers may have been diagnosis-specific. Furthermore, the generalizability of our results is diminished, as our patients originated only from rehabilitation clinics in Germany. Besides, we know neither why some adolescents did not participate, nor about the conditions under which the questionnaires were filled out. Finally, a severe limitation is the lack of evidence of construct validity. The significant difference between boys and girls concerning Factor 1 mean values are to be considered only a very preliminary hint of validity. The problem is that there are not yet any psychometrically tested, reliable scales and instruments assessing the communication preferences of chronically ill adolescents that could serve as adequate criteria of construct validity. Moreover, theory-based predictions are very difficult to extract from the current literature because of limited previous findings and the lack of precise conceptualization in this research domain. The goal of this article was to take an initial step in this direction, while
KLOSINSKI AND FARIN
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stating clearly that we need to do more research to establish validity. Future investigations could also address the extent to which our questionnaire applies to other patient groups (e.g., the acutely ill or outpatients) and whether it requires diagnosis-specific adaptations. Other promising approaches worth pursuing in optimizing this questionnaire would be to differentiate according to the type of provider (physicians, nursing staff, therapists), its application in the e-health field, or the inclusion of the communication preferences of parents and providers. Better understanding the complex communication and roles in the adolescent–parent–provider triade seems to be a challenging and promising target of future investigation, particularly because adolescents seem to be often neglected in this constellation (Tates, Elbers, Meeuwesen, & Bensing, 2002; Tates, Meeuwesen, Bensing, & Elbers, 2002; Tates, Meeuwesen, Elbers, & Bensing, 2002).
References Agan, J., Koch, L. C., & Rumrill, P. D., Jr. (2008). The use of focus groups in rehabilitation research. Work (Reading, Mass.), 31, 259 –269. Bethell, C., Klein, J., & Peck, C. (2001). Assessing health system provision of adolescent preventive services: The Young Adult Health Care Survey. Medical Care, 39, 478 – 490. http://dx.doi.org/10.1097/00005650200105000-00008 Britten, N. (2011). Qualitative research on health communication: What can it contribute? Patient Education and Counseling, 82, 384 –388. http://dx.doi.org/10.1016/j.pec.2010.12.021 Britto, M. T., DeVellis, R. F., Hornung, R. W., DeFriese, G. H., Atherton, H. D., & Slap, G. B. (2004). Health care preferences and priorities of adolescents with chronic illnesses. Pediatrics, 114, 1272–1280. Britto, M. T., Slap, G. B., DeVellis, R. F., Hornung, R. W., Atherton, H. D., Knopf, J. M., & DeFriese, G. H. (2007). Specialists understanding of the health care preferences of chronically ill adolescents. Journal of Adolescent Health, 40, 334 –341. http://dx.doi.org/10.1016/j.jadohealth .2006.10.020 Bühner, M. (2010). Einführung in die Test- und Fragebogenkonstruktion (3., aktualisierte und erweiterte Auflage). München, Germany: Pearson Studium. Cheung, W. Y., Garratt, A. M., Russell, I. T., & Williams, J. G. (2000). The UK IBDQ: A British version of the inflammatory bowel disease questionnaire. Journal of Clinical Epidemiology, 53, 297–306. http://dx.doi .org/10.1016/S0895-4356(99)00152-3 Collins, D. (2003). Pretesting survey instruments: An overview of cognitive methods. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care & Rehabilitation, 12, 229 – 238. http://dx.doi.org/10.1023/A:1023254226592 Coyne, I. (2008). Children’s participation in consultations and decisionmaking at health service level: A review of the literature. International Journal of Nursing Studies, 45, 1682–1689. http://dx.doi.org/10.1016/j .ijnurstu.2008.05.002 Dunsmore, J., & Quine, S. (1996). Information, support, and decisionmaking needs and preferences of adolescents with cancer. Journal of Psychosocial Oncology, 13, 39 –56. http://dx.doi.org/10.1300/ J077V13N04_03 Epstein, R. M., Franks, P., Fiscella, K., Shields, C. G., Meldrum, S. C., Kravitz, R. L., & Duberstein, P. R. (2005). Measuring patient-centered communication in patient-physician consultations: Theoretical and practical issues. Social Science & Medicine, 61, 1516 –1528. http://dx.doi .org/10.1016/j.socscimed.2005.02.001 Farin, E., Gramm, L., & Kosiol, D. (2011). Development of a questionnaire to assess communication preferences of patients with chronic illness.
Patient Education and Counseling, 82, 81– 88. http://dx.doi.org/ 10.1016/j.pec.2010.02.011 Freake, H., Barley, V., & Kent, G. (2007). Adolescents’ views of helping professionals: A review of the literature. Journal of Adolescence, 30, 639 – 653. http://dx.doi.org/10.1016/j.adolescence.2006.06.001 Garland, A. F., Saltzman, M. D., & Aarons, G. A. (2000). Adolescent satisfaction with mental health services: Development of a multidimensional scale. Evaluation and Program Planning, 23, 165–175. http://dx .doi.org/10.1016/S0149-7189(00)00009-4 George, D., & Mallery, P. (2002). SPSS for windows step by step: A simple guide and reference, 11.0 update (4th ed.). Boston, MA: Allyn & Bacon. Ginsburg, K. R., Forke, C. M., Cnaan, A., & Slap, G. B. (2002). Important health provider characteristics: The perspective of urban ninth graders. Journal of Developmental and Behavioral Pediatrics, 23, 237–243. http://dx.doi.org/10.1097/00004703-200208000-00008 Ginsburg, K. R., Slap, G. B., Cnaan, A., Forke, C. M., Balsley, C. M., & Rouselle, D. M. (1995). Adolescents’ perceptions of factors affecting their decisions to seek health care. Journal of the American Medical Association, 273, 1913–1918. http://dx.doi.org/10.1001/jama.1995 .03520480033036 Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. http://dx.doi.org/10.1080/ 10705519909540118 Institute of Medicine. (2001). Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press. Jedeloo, S., van Staa, A., Latour, J. M., & van Exel, N. J. (2010). Preferences for health care and self-management among Dutch adolescents with chronic conditions: A Q-methodological investigation. International Journal of Nursing Studies, 47, 593– 603. http://dx.doi.org/ 10.1016/j.ijnurstu.2009.10.006 Knopf, J. M., Hornung, R. W., Slap, G. B., DeVellis, R. F., & Britto, M. T. (2008). Views of treatment decision making from adolescents with chronic illnesses and their parents: A pilot study. Health Expectations: An International Journal of Public Participation in Health Care & Health Policy, 11, 343–354. http://dx.doi.org/10.1111/j.1369-7625.2008 .00508.x Kyngäs, H. (2000). Compliance of adolescents with chronic disease. Journal of Clinical Nursing, 9, 549 –556. http://dx.doi.org/10.1046/j.13652702.2000.00368.x Kyngäs, H. A., Kroll, T., & Duffy, M. E. (2000). Compliance in adolescents with chronic diseases: A review. Journal of Adolescent Health, 26, 379 –388. Kyngäs, H. A., Skaar-Chandler, C. A., & Duffy, M. E. (2000). The development of an instrument to measure the compliance of adolescents with a chronic disease. Journal of Advanced Nursing, 32, 1499 –1506. http://dx.doi.org/10.1046/j.1365-2648.2000.01611.x LeBlanc, L. A., Goldsmith, T., & Patel, D. R. (2003). Behavioral aspects of chronic illness in children and adolescents. Pediatric Clinics of North America, 50, 859 – 878. http://dx.doi.org/10.1016/S0031-3955(03)00072-5 Litt, I. F., & Cuskey, W. R. (1984). Satisfaction with health care. A predictor of adolescents’ appointment keeping. Journal of Adolescent Health Care, 5, 196 –200. http://dx.doi.org/10.1016/S0197-0070(84)80042-X Moules, T. (2009). “They wouldn’t know how it feels . . .”: characteristics of quality care from young people’s perspectives: A participatory research project. Journal of Child Health Care, 13, 322–332. http://dx.doi .org/10.1177/1367493509344824 Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill. O’Cathain, A., Murphy, E., & Nicholl, J. (2007). Why, and how, mixed methods research is undertaken in health services research in England: A mixed methods study. BMC Health Services Research, 7, 85. http://dx .doi.org/10.1186/1472-6963-7-85
This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
COMMUNICATION PREFERENCES Perrin, E. C., Newacheck, P., Pless, I. B., Drotar, D., Gortmaker, S. L., Leventhal, J., . . . Weitzman, M. (1993). Issues involved in the definition and classification of chronic health conditions. Pediatrics, 91, 787–793. Quinn, G. P., Murphy, D., Knapp, C., Stearsman, D. K., Bradley-Klug, K. L., Sawczyn, K., & Clayman, M. L. (2011). Who decides? Decision making and fertility preservation in teens with cancer: A review of the literature. Journal of Adolescent Health, 49, 337–346. http://dx.doi.org/ 10.1016/j.jadohealth.2011.01.005 Rich, M., & Ginsburg, K. R. (1999). The reason and rhyme of qualitative research: Why, when, and how to use qualitative methods in the study of adolescent health. Journal of Adolescent Health, 25, 371–378. http://dx .doi.org/10.1016/S1054-139X(99)00068-3 Rodin, G., Mackay, J. A., Zimmermann, C., Mayer, C., Howell, D., Katz, M., . . . Brouwers, M. (2009). Clinician–patient communication: A systematic review. Supportive Care in Cancer, 17, 627– 644. http://dx .doi.org/10.1007/s00520-009-0601-y Schaeuble, K., Haglund, K., & Vukovich, M. (2010). Adolescents’ preferences for primary care provider interactions. Journal for Specialists in Pediatric Nursing, 15, 202–210. http://dx.doi.org/10.1111/j.1744-6155 .2010.00232.x Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8, 3–15. http://dx.doi.org/10.1191/096228099671525676 Shapiro, J. P., Welker, C. J., & Jacobson, B. J. (1997). The Youth Client Satisfaction Questionnaire: Development, construct validation, and factor structure. Journal of Clinical Child Psychology, 26, 87–98. http://dx .doi.org/10.1207/s15374424jccp2601_9 Swedlund, M. P., Schumacher, J. B., Young, H. N., & Cox, E. D. (2012). Effect of communication style and physician-family relationships on satisfaction with pediatric chronic disease care. Health Communication, 27, 498 –505. http://dx.doi.org/10.1080/10410236.2011.616632 Tashakkori, A., & Creswell, J. W. (2007). Editorial: The new era of mixed methods. Journal of Mixed Methods Research, 1, 3–7. http://dx.doi.org/ 10.1177/2345678906293042 Tates, K., Elbers, E., Meeuwesen, L., & Bensing, J. (2002). Doctor–parent– child relationships: A “pas de trois.” Patient Education and Counseling, 48, 5–14. http://dx.doi.org/10.1016/S0738-3991(02)00093-9
1059
Tates, K., Meeuwesen, L., Bensing, J., & Elbers, E. (2002). Joking or decision-Making? Affective and instrumental behaviour in doctor– parent– child communication. Psychology & Health, 17, 281–295. http://dx.doi.org/10.1080/08870440290029548 Tates, K., Meeuwesen, L., Elbers, E., & Bensing, J. (2002). “I’ve come for his throat”: Roles and identities in doctor-parent-child communication. Child: Care, Health and Development, 28, 109 –116. http://dx.doi.org/ 10.1046/j.1365-2214.2002.00248.x Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10. http://dx.doi.org/ 10.1007/BF02291170 van Staa, A., Jedeloo, S., & van der Stege, H., & the On Your Own Feet Research Group. (2011). “What we want”: Chronically ill adolescents’ preferences and priorities for improving health care. Patient Preference and Adherence, 5, 291–305. http://dx.doi.org/10.2147/PPA.S17184 van Staa, A., & the On Your Own Feet Research Group. (2011). Unraveling triadic communication in hospital consultations with adolescents with chronic conditions: The added value of mixed methods research. Patient Education and Counseling, 82, 455– 464. http://dx.doi.org/ 10.1016/j.pec.2010.12.001 WMA declaration of Ottawa on child health. (1998). Retrieved from http://www.wma.net/en/30publications/10policies/c4/ Woodgate, R. L. (1998). Health professionals caring for chronically ill adolescents: Adolescents’ perspectives. Journal for Specialists in Pediatric Nursing, 3, 57– 68. http://dx.doi.org/10.1111/j.1744-6155.1998 .tb00029.x Zwaanswijk, M., Tates, K., van Dulmen, S., Hoogerbrugge, P. M., Kamps, W. A., Beishuizen, A., & Bensing, J. M. (2011). Communicating with child patients in pediatric oncology consultations: A vignette study on child patients’, parents’, and survivors’ communication preferences. Psycho-Oncology, 20, 269 –277. http://dx.doi.org/10.1002/pon.1721
Received June 10, 2014 Revision received November 3, 2014 Accepted December 4, 2014 䡲
