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Abstract

Objective:

Early intervention services (EISs) for mental illness may improve outcomes, although treatment engagement is often a problem. Incorporating patients’ preferences in the design of interventions improves engagement. A discrete-choice conjoint experiment was conducted in Canada to identify EIS attributes that encourage treatment initiation.

Methods:

Sixteen four-level attributes were formalized into a conjoint survey, completed by patients, family members, and mental health professionals (N=562). Participants were asked which EIS option people with mental illness would contact. Latent-class analysis identified respondent classes characterized by shared preferences. Randomized first-choice simulations predicted which hypothetical options, based on attributes, would result in maximum utilization.

Results:

Participants in the conventional-service class (N=241, 43%) predicted that individuals would contact traditional services (for example, hospital location and staffed by psychologists or psychiatrists). Membership was associated with being a patient or family member and being male. Participants in the convenient-service class (N=321, 57%) predicted that people would contact services promoting easy access (for example, self-referral and access from home). Membership was associated with being a professional. Both classes predicted that people would contact services that included short wait times, direct contact with professionals, patient autonomy, and psychological treatment information. The convenient-service class predicted that people would use an e-health model, whereas the conventional-service class predicted that people would use a primary care or clinic-hospital model.

Conclusions:

Provision of a range of services may maximize EIS use. Professionals may be more apt to adopt EISs in line with their beliefs regarding patient preferences. Considering several perspectives is important for service design.
Mental illness is considered more burdensome than most general medical disorders (1). Its negative impact is compounded by high prevalence and early onset (1). Nearly 50% of Americans will experience a lifetime mental disorder, and about 75% of adult cases of psychiatric illness have their onset before age 24 (2). Poor outcomes are exacerbated the longer mental illnesses go undetected (3). However, promptly treating persons with emerging disorders can mitigate poor outcomes (4). Thus interest is growing in the potential for early intervention services (EISs) to improve functioning (5), reduce suicide and hospitalization rates (6), and prevent the full expression of disorders (2).
However, most patients do not receive treatment until many years after onset (3), when more severe symptoms emerge (2,7). Attitudinal and structural barriers, as well as poor insight into the need for treatment, present obstacles to the use of EISs (8). It is imperative, therefore, that mental health service planners investigate ways to facilitate patient engagement. An empirically demonstrated means of improving engagement is to incorporate patients’ preferences regarding service features (9,10). Including patient preferences in EIS design may increase the rates at which individuals use services. Matching patients to their preferred treatments reduces dropout rates and improves outcomes (9,11). Thus incorporating preferences in mental health EISs may increase rates of initial contact. However, valid methods for soliciting preferences are needed.
Discrete-choice conjoint experiments (DCEs) are designed to solicit preferences for the features or attributes of health interventions (12). DCEs use survey methods that pose forced choices between complex service options. By systematically varying the attributes or features of these options, DCEs can estimate the relative value of the individual levels of each attribute and quantify an attribute’s relative importance. DCEs mimic real-world decision making and offer advantages over traditional survey methods (13,14). Ratings often produce high scores across attributes, reflecting an individual’s desire for services that encompass all positive characteristics. Resource limitations, however, frequently necessitate trade-offs between attributes (13). DCEs can also limit the social desirability biases that may compromise preference research (14). Individuals with severe mental illnesses can complete DCE surveys and make meaningful trade-offs (15).
The purpose of this study was to use a DCE survey to elicit the views of patients, family members, and mental health professionals about the attributes of an EIS that they believed people with psychiatric illnesses would be most likely to contact. Because a successful program needs to balance the views of users and their families with the views of those who provide services, an understanding of multiple EIS perspectives can inform service design. To ensure uptake and continued engagement, services should incorporate patient preferences (9,11). In addition, professionals’ attitudes may affect whether they adopt new practices (16) and may therefore affect service planning decisions. Areas of agreement could form a foundation for treatment; discrepancies could inform policy changes (17). To ensure that responses were relevant to those who actually use the EISs, participants were asked to respond to our survey from the perspective of someone seeking mental health treatment for the first time.
The following research questions were addressed. Can respondents be represented by latent classes on the basis of similar EIS preferences, and if so, which attributes exert the most influence on the service use decisions of each class? Do the classes differ in regard to demographic covariates? Three covariates that could influence EIS preferences were examined: background (patients and family members versus professionals) (17), age (18), and sex (19) of the respondents. Moreover, demographic characteristics of each class were compared. Because of the emerging interest in locating mental health services in primary care settings (20) and the development of new e-health options (21), simulations were used to predict the percentage of survey respondents likely to use an EIS based in an e-health, primary care, or clinic or hospital setting.

Methods

The St. Joseph’s Healthcare Hamilton Research Ethics Board approved this study. Recruitment occurred between March and October 2011 from six clinics specializing in psychotic disorders, mood and anxiety disorders, and general psychiatric disorders. Participants were also recruited from four youth mental health and primary care clinics in the community
When patients and family members entered the clinic, they were informed of the survey by receptionists. Those who agreed to participate obtained access to the survey on a laptop. Further recruitment was conducted from family support groups organized through local patient advocacy organizations (Schizophrenia Society of Ontario and Mood Menders). Individuals recruited from these groups were not necessarily related to patient participants. Clinic staff were asked to complete the survey via an e-mailed link. All participants provided informed consent via a computer interface before responding to the survey. The survey took approximately 15 minutes to complete. Upon completion, participants were given a choice of $15 gift cards.
The study design was informed by the International Society for Pharmacoeconomics and Outcomes Research checklists (22,23). We reviewed literature to identify attributes that might influence EIS utilization. A multidisciplinary research team, including a peer support representative, reduced the list to 16 actionable four-level attributes with limited conceptual overlap (22).
Sawtooth Software’s SSI Web version 7.4 was used to administer the computerized survey (24). A partial-profile design was employed (Figure 1) in which forced-choice tasks include subsets of the attribute options (25,26). DCEs can be simplified so that individuals with fifth-grade reading skills can reliably complete tasks (27).
FIGURE 1. Sample choice task in the DCE surveya
aSixteen four-level attributes were experimentally varied into 18 choice tasks in a discrete-choice conjoint experiment (DCE) survey. In each task, participants selected attributes of a hypothetical early intervention service that they thought people with emerging psychiatric symptoms would be most likely to contact.
At the beginning of the survey, “mental health problems” were defined in plain language as “a lot of fear and anxiety, a lot of sadness and depression, hearing voices or seeing things that are not there, and/or using too much alcohol or drugs.” In 18 choice tasks, participants chose between three hypothetical EIS options with specific combinations of attribute levels. They were asked to select an option that people with mental health problems would be most likely to contact. The experimental design ensured that each attribute level was presented as close to an equal number of times as possible. Each respondent was randomly assigned one of 999 versions (22,24,28).
The survey was completed by 562 people (249 patients, 92 family members, and 221 professionals). Demographic data was incomplete for 14 participants. The participants ranged in age from 16 to over 55, 71% (N=391) were female, and 83% (N=455) were born in Canada.
Latent Gold Choice 4.5 software was used to analyze the DCE survey data (24,29). These tools identify classes with similar preferences and generate zero-centered utility coefficients to determine how strongly each attribute level is preferred. The software forms latent classes, with respondents grouped on the basis of preference similarities (29). Information criteria were used to determine which maximum-likelihood solution (that is, the ideal number of latent classes) to adopt (29). Latent-class solutions that included one to five solutions were modeled and replicated ten times from random starting seeds. The choice of a latent-class solution is a balance between fit—denoted by information criteria—and parsimony (30). The goal was to obtain distinct subgroups from a relatively large sample; thus Bayesian information criteria (BIC) were deemed the most appropriate, consistent with other literature (3032).
Background (professionals versus patient and family members) (17), age (16–35 versus ≥36) (18), and sex (19) were included as covariates. These variables were predicted to influence perceptions of the type of EISs participants would expect people to use (33). Importance scores, calculated by using the range in utility coefficients (13), reflect how much effect each attribute has on choice.
Randomized first-choice (RFC) simulations predict how participants would respond if new multiattribute service options were introduced (3436). The utility coefficients derived from the survey allowed RFC simulations to be computed (24). The goal of these simulations was to explore emerging health care options that might encourage early utilization (for example, e-health and primary care) and to determine the attractiveness of these options compared with existing health care models (for example, clinic- or hospital-based models).

Results

A two-class model yielded the lowest BIC (32). The utility coefficients in Table 1 denote preferences. Participants in both classes thought that people would contact an EIS that has no wait times, incorporates direct contact with mental health professionals, allows people to talk to a service provider from their own culture, and involves an assessment that takes one hour. Moreover, participants in both classes predicted that people would contact a service that provides information regarding psychological treatment and has been endorsed by those who have experienced mental health problems.
TABLE 1. Zero-centered utility coefficients and Z values for the conventional-service and convenient-service classes
Category and attributeLatent classWaldc
Conventional (N=241)Convenient (N=321)
UaZbUaZb
Making initial contact with the service     
 Early intervention service (EIS) advertising format    38.91***
  This service is not advertised–.11–.96–1.22–8.61 
  This service is advertised on television and radio–.02–.16.444.39 
  This service is advertised at public awareness events in the community.454.35.424.34 
  This service is advertised on Internet sites such as Google, Facebook, or Twitter–.32–2.50.363.47 
 Referral process    82.36***
  People can refer themselves–.07–.521.4411.97 
  People are referred by school counselors, family doctors, or mental health professionals.05.48–.02–.17 
  People must be referred by family doctors or mental health professionals.161.56–.22–1.92 
  People must be referred by a mental health professional–.14–1.17–1.20–8.34 
 Wait times    21.85***
  This service starts immediately1.2711.162.1214.04 
  People wait for 1 month for this service to start.232.07.836.04 
  People wait for 3 months for this service to start–.52–4.18–.44–2.92 
  People wait for 6 months for this service to start–.98–6.46–2.51–7.85 
 Appointment scheduling    12.90**
  Appointments at a convenient time for the service–.28–2.41–.87–6.86 
  Appointments at a convenient time for patients.121.15.171.69 
  Appointments at a convenient time for both patients and the service.262.42.343.55 
  No appointments needed; can be used anytime–.10–.90.363.65 
 Time demand for first contact    7.83
  First contact lasts 1 hour.564.94.535.46 
  First contact lasts 2 hours.211.90.363.75 
  First contact lasts 3 hours–.34–2.79.01.07 
  First contact lasts 4 hours–.43–3.45–.90–7.24 
Context of the EIS     
 Location    58.92***
  This service is used at home–.30–2.10.353.49 
  This service is at a shopping mall or storefront–1.57–7.01–.53–4.79 
  This service is at a family doctor's office.665.31.202.03 
  This service is at a clinic or hospital1.219.11–.01–.08 
 Atmosphere    9.43*
  This service feels like it is for people ages 12 to 18–.50–4.20–.91–7.23 
  This service feels like it is for people ages 19 to 25–.34–2.95–.39–3.66 
  This service feels like it is for people ages 18 and older.656.01.605.83 
  This service feels like it is for people ages 12 and older.181.54.706.70 
 Professional background of service providers    20.63***
  People talk to someone who has experienced a mental illness–.38–3.08–.57–4.49 
  People talk to a mental health nurse–.11–1.03.505.11 
  People talk to a social worker–.25–2.20–.20–1.91 
  People talk to a doctor (psychologist or psychiatrist).747.23.262.66 
 Cultural background of service providers    13.99**
  Culture is not considered when assigning service providers–.08–.73–.75–5.82 
  If they want, people talk to service providers from their culture.444.44.818.48 
  If the service decides, people talk to a service provider from their culture–.11–1.00.09.91 
  People always talk to service providers from their culture–.24–2.14–.15–1.48 
 Format of service contacts    7.51
  Contact is by text messages–.95–6.36–1.33–9.00 
  Contact is at an Internet site–.62–4.56–.25–2.28 
  Contact is by phone.383.53.586.04 
  Contact is face to face1.2010.651.009.65 
Service decision making     
 Decisions regarding anonymity    17.74***
  People don't give their name when contacting this service–.15–1.33.181.81 
  If they want, people give their name when contacting this service.292.79.686.82 
  The service decides whether people need to give their name when contacting service–.10–.94–.11–1.10 
  People must give their name when contacting this service–.03–.32–.75–5.57 
 Decisions regarding family involvement    43.07***
  Families are not involved–.52–4.30–1.23–7.54 
  If the service wants, families are involved.08.80.10.93 
  The service and people using the service decide if families are involved.343.021.4212.24 
  Families are always involved.09.82–.28–2.48 
Provided by the service     
 Information about treatment options    5.43
  Does not give information about treatment options–.98–6.77–1.23–8.61 
  Gives information about alternative treatments (for example, diet and exercise).434.10.525.21 
  Gives information about psychological treatments.474.44.827.78 
  Gives information about medications.08.68–.11–1.02 
 Internet social networking options    61.65***
  No Internet social networking options–.13–1.19–1.02–5.98 
  Unsupervised Internet site where people talk about mental health problems–.36–3.14–1.31–7.33 
  Professionally supervised Internet site where people talk about mental health problems.323.231.179.58 
  Internet site where professionals answer questions about mental health problems.171.561.169.61 
 Raising awareness within the community    19.68***
  This service does not educate the community about mental health–.57–4.39–1.30–8.91 
  Once a year this service educates the community about mental health–.37–3.06–.55–4.70 
  Once a month this service educates the community about mental health.373.63.737.57 
  Once a week this service educates the community about mental health.565.191.1211.22 
Evidence of service efficacy     
 Content evidence based    12.78**
  We do not know if this service is helpful–.77–5.72–1.32–9.07 
  People who have experienced mental health problems say this service is helpful.454.22.909.13 
  Mental health professionals say this service is helpful.393.87.252.56 
  Research says this service is helpful–.07–.65.171.66 
a
Utility coefficient values that have been zero-centered. Stronger preferences are indicated by higher utility coefficient values (13).
b
Z scores associated with the utility coefficients. Any Z scores outside the range of 1.96 are considered statistically significant and denote a significantly preferred (≥1.96) or not preferred (<1.96) attribute level.
c
Testing whether the utility coefficients differ significantly for the two classes
*
p<.05, **p<.01, ***p<.001
Results for the first class (N=241, 43% of the sample), which we termed the conventional-service class, suggested that people with mental health issues would be more likely to contact an EIS located at a clinic or hospital and staffed by psychiatrists or psychologists. Participants in this class also recommended that the service be available to anyone age 18 or older, advertise at weekly events, and offer appointment scheduling at convenient times for both the patient and the service. Importance scores showed that this class was sensitive to variations in the background of the service providers (favoring psychiatrists and psychologists), assessment format (favoring face-to-face contact), and the opinions of people who have experienced mental illness and who have deemed this service helpful (Table 2).
TABLE 2. Standardized importance scores for the conventional-service and convenient-service classes
Category and attributeLatent class
Conventional (N=241)Convenient (N=321)
RaIbRaIb
Making initial contact with the service    
 Wait times57114
 Referral process9538
 Appointment scheduling114144
 Early intervention service (EIS) advertising format14395
 Time demand for first contact162124
Context of the EIS    
 Professional background of service providers115153
 Format of service contacts21267
 Cultural background of service providers76115
 Location86163
 Atmosphere152105
Service decision making    
 Decisions regarding anonymity66134
 Decisions regarding family involvement10528
Provided by the service    
 Internet social networking options4848
 Information about treatment options12486
 Raising awareness within the community13457
Evidence of service efficacy    
 Content evidence based31277
a
Within each class, the importance of an attribute is ranked (R) from 1 to 16, with 1 being the most important. Within each category, attributes are organized from the most to least important for the conventional-service class.
b
Importance score (I) of each attribute for the two latent classes. Importance scores are expressed as percentages, which sum as close to 100% as possible within each class, although there may be slight rounding errors. Percentages are expressed as whole numbers per journal style; when percentages are reported to two decimal places, ranking is as displayed in the table. Attributes with higher importance scores are considered to have more influence on decision making in regard to EIS preferences (13).
Participants in the second class (N=321, 57%), termed the convenient-service class, predicted that people would be more likely to contact a service that is open to walk-in and self-referral and accessed from respondents' homes. Participants in this class recommended that the service be available to anyone age 12 or older, staffed by mental health nurses, and advertised on radio and television. Importance scores showed that this class was sensitive to variations in wait times (favoring none), level of family involvement (preferring collaborative decisions), and the referral process (favoring self-referral) (Table 2).
Demographic information about the participants and the classes is summarized in Table 3. Parameter estimates for the covariates (U) reflect the strength of the relationship between the covariates and membership within each class. Membership in the conventional-service class was associated with being either a patient or a family member (U=.40, Z=5.42, p<.001) and male (U=.23, Z=3.43, p<.001). Membership in the convenient-service class was associated with being a mental health professional and female.
TABLE 3. Demographic characteristics of the sample and of latent classes
Characteristic  Latent classχ2df
Total (N=562)Conventional (N=241)Convenient (N=321)
N%Na%Na%
Background      94.70***1
 Patient or family member341612025913941  
 Professional22139391818282  
Age      14.77**3
 16–2036726721028  
 21–351492757389262  
 36–55278511124016660  
 >55861638444856  
Sex      49.65***1
 Male15829104665434  
 Female391711293326267  
Education      46.25***1
 High school or less1021974732827  
 Greater than high school446811593628764  
Birth country      7.70**1
  Canada455831814027460  
 Another country941752554245  
Language      4.912
 English486892014128559  
 French61583117  
 Other571027473053  
a
A close approximation of the N of people in each category. Percentages are expressed as whole numbers per journal style; Ns were calculated by using percentages expressed to two decimal places.
**
p<.01, ***p<.001
Individual utility coefficients were used to create simulation models (24). Three EIS options were created for these simulations. The e-health option involved Internet advertising and service contacts at an Internet site where professionals answer questions about mental illness. This service would be used from home and involve self-referral, no appointments, and individual decisions about whether to provide self-identifying information. Primary care involved service at a family doctor's office, self-referral, and appointment scheduling convenient for both parties. The clinic-hospital option involved service at a clinic or hospital, referral by a family doctor or mental health professional, and appointments made on the basis of convenience to the service.
Among the 321 participants in the convenient-service class, 96% (N=308) thought that people would utilize the e-health option. The conventional-service class was split, with 44% (N=107) predicting that people would use primary care, and 42% (N=102) predicting that people would use the clinic-hospital. The remaining 13% of the conventional-service class (N=32) predicted that people would use the e-health option. Overall, 61% of the entire sample thought people would use an e-health option (Table 4).
TABLE 4. RFC simulations predicting responses to three simulated EIS modelsa
Service modelLatent class
TotalConventionalConvenient
%SEb%SEb%SEb
e-health60.71.813.51.296.1.4
Primary care21.1.944.2.43.8.3
Clinic or hospital18.21.042.31.1.2.03
a
The randomized first-choice (RFC) simulations predicted the percentage of participants who believed that people with mental health problems would be most likely to contact a hypothetical early intervention service (EIS) model (28).
b
Standard error (U/Z)

Discussion

A DCE was used to determine attributes of an EIS that individuals with emerging symptoms of mental illness would be most likely to contact. These methods have been borrowed from marketing research and health economics (35) and are relatively new to mental health care (12). This study demonstrated the ease of administering DCEs in a mental health setting. The input from patients, family members, and professionals provided a comprehensive view of service structure and delivery preferences (17). As expected, the analyses allowed us to determine the proportion of each respondent group within discrete latent classes. The covariate analyses enabled us to determine the independent contributions of demographic factors to class membership.
Two classes of respondents were identified. Respondents in the conventional-service class thought that people would be more likely to contact EISs that included mutually convenient appointments at a clinic or hospital. They were sensitive to variations in professional background. Those in the convenient-service class endorsed the view that people with mental illness would be apt to contact EISs that emphasize ease of access (for example, self-referral to a service that a person could use at home). Neither class prioritized receiving information about medication, consistent with previous research (37).
Wait times exerted a greater influence on the choices of the convenient-service class (most professionals were members of this class) than on those of the conventional-service class (most patients and family members were members of this class). A strong relationship between wait times and the likelihood of failing to attend one’s initial appointment has been previously described (38). In one study, each day of delay during the first week after contact had a significant impact on the rate of kept appointments (38), suggesting that those who obtain appointments for psychiatric care with short wait times are more likely to attend initial appointments (38,39).
Participants in both classes predicted that people would contact services with minimal wait times, increased patient autonomy, information regarding psychological treatment, and endorsements by other service users. The similarities between the two latent classes reinforce key EIS aspects that are strongly preferred. Incorporating the most important and universally preferred attributes would increase the likelihood of EIS use.
An important aspect of this study’s methodology was the RFC simulations. These predicted that providing only one type of EIS would not sufficiently meet the needs of the entire range of potential clients, even though the similarities in preferences between the two latent classes might suggest that approach. For example, the simulations predicted that 96% (N=308) of the respondents in the convenient-service class would most likely use an e-health option, compared with only 13% (N=32) of the conventional-service class. The differences in the classes here suggest that some participants may have been more inclined to use a primary care or clinic-hospital model, whereas the majority of one class saw the benefit of adopting an e-health option, consistent with previous research (21). Depending on the desired target population of an EIS, service design may need to incorporate different attributes to increase the range of choices available.
In addition to highlighting the need to provide a range of options for service users, the results also underscore the need to incorporate multiple perspectives in service design. Despite areas of agreement between respondent groups, many professionals were inaccurate in their prediction of the EIS design preferences of patients and family members, consistent with previous research (17). Both classes had high utility scores for service attributes that underscored autonomy, suggesting that people experiencing psychiatric symptoms may prefer a collaborative health care model in which they have input regarding which services will best suit their individual needs (10). Future EISs designed with a variety of perspectives in mind would provide service users with a range of choices.
Several limitations of this study should be noted. The survey was designed to determine what a broad sample of patients, family members, and professionals thought people with mental illness would prefer in an EIS. For the purposes of this survey, the number of family members was insufficient to obtain stable preference estimates. Thus we were unable to analyze patients and family members separately. The survey asked participants to respond from the perspective of someone accessing an EIS for the first time. Many participants were already involved in traditional service models. As a result, they may have been more favorably disposed to the existing model. Those who rejected existing services would not have been included in our sample. The inclusion of respondents across a broad age range might be considered a limitation. However, experienced service users have a knowledgeable perspective and are considered valuable informants (37). Furthermore, covariate analyses indicated that once a participant’s status (that is, patient, family member, or professional) and gender were accounted for, age did not predict class membership.
The sample was also limited in its cultural diversity—94 participants (17%) were immigrants. Participants born in another country were more likely to be members of the conventional-service class. According to 2006 census data, Hamilton had the third highest foreign-born population (24.4%) in Canada, higher than the national level of 19.8% (40). Therefore, it appears that the sample somewhat underrepresented the diversity expected from the study’s local environment. This may reflect a “healthy immigrant effect” (41) or underutilization of mental health services by this population. Similarly, family members recruited from family support groups and clinic waiting rooms are likely to represent only a subset of the entire population of family members of persons with mental illness (for example, they may have been more actively involved in their relative’s care than those who did not attend appointments or support groups).
Future studies should address the aforementioned limitations. For instance, DCE studies could focus on recruiting a larger number of family members in order to be able to analyze the three groups separately to determine whether preferences differ significantly from the results obtained here. In addition, studies should assess the preferences of individuals who are experiencing mental health symptoms but who are not yet involved in treatment services and considering using EISs for the first time. It would also be valuable for future research to determine whether preferences for mental health EISs vary as a function of cultural background or specific psychiatric diagnoses. Finally, attributes that influence whether people access a mental health service may not be the same as those that influence whether they remain in a treatment program. This question is the focus of a second study.

Conclusions

The results of this study underscore the importance of incorporating key attributes in designing an EIS to maximize use by people with emerging mental illnesses. First, EISs should minimize wait times. Second, given that the two classes in this study reported both similar and divergent preferences, there should be a focus on providing a range of service options. This may include e-health, community-based primary care models, and more traditional hospital-based approaches to EISs. This approach will enhance uptake and adherence by targeting a wider range of people and maximizing choice and patient autonomy. Finally, this study highlights the need for future EISs to adopt a collaborative approach that involves patients and family members in decision making.

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Information & Authors

Information

Published In

Go to Psychiatric Services
Go to Psychiatric Services

Cover: Print table, by Frank Lloyd Wright (maker: William E. Nemmers), 1902–1903. White oak. Purchase, Emily Crane Chadbourne Bequest, 1972, the Metropolitan Museum of Art, New York City. Image copyright © The Metropolitan Museum of Art. Image source: Art Resource, New York City.

Psychiatric Services
Pages: 184 - 191
PubMed: 26369880

History

Received: 10 July 2014
Revision received: 11 December 2014
Revision received: 11 April 2015
Accepted: 26 May 2015
Published online: 15 September 2015
Published in print: February 01, 2016

Authors

Details

Mackenzie P. E. Becker, B.Arts Sc., M.Sc.
Ms. Becker is with the Department of Psychology, Neuroscience and Behaviour, and Dr. Cunningham, Ms. Furimsky, Ms. Rimas, Ms. Wilson, Dr. Bieling, Ms. Chen, Ms. Mielko, and Dr. Zipursky are with the Department of Psychiatry and Behavioural Neurosciences, all at McMaster University, Hamilton, Ontario, Canada. Ms. Furimsky, Ms. Wilson, Dr. Bieling, and Dr. Zipursky are also with the Mental Health and Addiction Program, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, where Ms. Jeffs and Ms. Madsen are affiliated. Dr. Cunningham is also with the Department of Psychiatry, McMaster Children's Hospital, Hamilton, Ontario. Ms. Chen is also with the School of Business, University of Alberta, Edmonton, Alberta. Dr. Christensen is with the Research School of Psychology, Australian National University, Canberra, Australia. Send correspondence to Dr. Zipursky (e-mail: [email protected]).
Bruce K. Christensen, Ph.D.
Ms. Becker is with the Department of Psychology, Neuroscience and Behaviour, and Dr. Cunningham, Ms. Furimsky, Ms. Rimas, Ms. Wilson, Dr. Bieling, Ms. Chen, Ms. Mielko, and Dr. Zipursky are with the Department of Psychiatry and Behavioural Neurosciences, all at McMaster University, Hamilton, Ontario, Canada. Ms. Furimsky, Ms. Wilson, Dr. Bieling, and Dr. Zipursky are also with the Mental Health and Addiction Program, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, where Ms. Jeffs and Ms. Madsen are affiliated. Dr. Cunningham is also with the Department of Psychiatry, McMaster Children's Hospital, Hamilton, Ontario. Ms. Chen is also with the School of Business, University of Alberta, Edmonton, Alberta. Dr. Christensen is with the Research School of Psychology, Australian National University, Canberra, Australia. Send correspondence to Dr. Zipursky (e-mail: [email protected]).
Charles E. Cunningham, Ph.D.
Ms. Becker is with the Department of Psychology, Neuroscience and Behaviour, and Dr. Cunningham, Ms. Furimsky, Ms. Rimas, Ms. Wilson, Dr. Bieling, Ms. Chen, Ms. Mielko, and Dr. Zipursky are with the Department of Psychiatry and Behavioural Neurosciences, all at McMaster University, Hamilton, Ontario, Canada. Ms. Furimsky, Ms. Wilson, Dr. Bieling, and Dr. Zipursky are also with the Mental Health and Addiction Program, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, where Ms. Jeffs and Ms. Madsen are affiliated. Dr. Cunningham is also with the Department of Psychiatry, McMaster Children's Hospital, Hamilton, Ontario. Ms. Chen is also with the School of Business, University of Alberta, Edmonton, Alberta. Dr. Christensen is with the Research School of Psychology, Australian National University, Canberra, Australia. Send correspondence to Dr. Zipursky (e-mail: [email protected]).
Ivana Furimsky, B.Sc.N., M.N.
Ms. Becker is with the Department of Psychology, Neuroscience and Behaviour, and Dr. Cunningham, Ms. Furimsky, Ms. Rimas, Ms. Wilson, Dr. Bieling, Ms. Chen, Ms. Mielko, and Dr. Zipursky are with the Department of Psychiatry and Behavioural Neurosciences, all at McMaster University, Hamilton, Ontario, Canada. Ms. Furimsky, Ms. Wilson, Dr. Bieling, and Dr. Zipursky are also with the Mental Health and Addiction Program, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, where Ms. Jeffs and Ms. Madsen are affiliated. Dr. Cunningham is also with the Department of Psychiatry, McMaster Children's Hospital, Hamilton, Ontario. Ms. Chen is also with the School of Business, University of Alberta, Edmonton, Alberta. Dr. Christensen is with the Research School of Psychology, Australian National University, Canberra, Australia. Send correspondence to Dr. Zipursky (e-mail: [email protected]).
Heather Rimas, B.A.
Ms. Becker is with the Department of Psychology, Neuroscience and Behaviour, and Dr. Cunningham, Ms. Furimsky, Ms. Rimas, Ms. Wilson, Dr. Bieling, Ms. Chen, Ms. Mielko, and Dr. Zipursky are with the Department of Psychiatry and Behavioural Neurosciences, all at McMaster University, Hamilton, Ontario, Canada. Ms. Furimsky, Ms. Wilson, Dr. Bieling, and Dr. Zipursky are also with the Mental Health and Addiction Program, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, where Ms. Jeffs and Ms. Madsen are affiliated. Dr. Cunningham is also with the Department of Psychiatry, McMaster Children's Hospital, Hamilton, Ontario. Ms. Chen is also with the School of Business, University of Alberta, Edmonton, Alberta. Dr. Christensen is with the Research School of Psychology, Australian National University, Canberra, Australia. Send correspondence to Dr. Zipursky (e-mail: [email protected]).
Fiona Wilson, M.Sc.
Ms. Becker is with the Department of Psychology, Neuroscience and Behaviour, and Dr. Cunningham, Ms. Furimsky, Ms. Rimas, Ms. Wilson, Dr. Bieling, Ms. Chen, Ms. Mielko, and Dr. Zipursky are with the Department of Psychiatry and Behavioural Neurosciences, all at McMaster University, Hamilton, Ontario, Canada. Ms. Furimsky, Ms. Wilson, Dr. Bieling, and Dr. Zipursky are also with the Mental Health and Addiction Program, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, where Ms. Jeffs and Ms. Madsen are affiliated. Dr. Cunningham is also with the Department of Psychiatry, McMaster Children's Hospital, Hamilton, Ontario. Ms. Chen is also with the School of Business, University of Alberta, Edmonton, Alberta. Dr. Christensen is with the Research School of Psychology, Australian National University, Canberra, Australia. Send correspondence to Dr. Zipursky (e-mail: [email protected]).
Lisa Jeffs, M.Ed.
Ms. Becker is with the Department of Psychology, Neuroscience and Behaviour, and Dr. Cunningham, Ms. Furimsky, Ms. Rimas, Ms. Wilson, Dr. Bieling, Ms. Chen, Ms. Mielko, and Dr. Zipursky are with the Department of Psychiatry and Behavioural Neurosciences, all at McMaster University, Hamilton, Ontario, Canada. Ms. Furimsky, Ms. Wilson, Dr. Bieling, and Dr. Zipursky are also with the Mental Health and Addiction Program, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, where Ms. Jeffs and Ms. Madsen are affiliated. Dr. Cunningham is also with the Department of Psychiatry, McMaster Children's Hospital, Hamilton, Ontario. Ms. Chen is also with the School of Business, University of Alberta, Edmonton, Alberta. Dr. Christensen is with the Research School of Psychology, Australian National University, Canberra, Australia. Send correspondence to Dr. Zipursky (e-mail: [email protected]).
Peter J. Bieling, M.A., Ph.D.
Ms. Becker is with the Department of Psychology, Neuroscience and Behaviour, and Dr. Cunningham, Ms. Furimsky, Ms. Rimas, Ms. Wilson, Dr. Bieling, Ms. Chen, Ms. Mielko, and Dr. Zipursky are with the Department of Psychiatry and Behavioural Neurosciences, all at McMaster University, Hamilton, Ontario, Canada. Ms. Furimsky, Ms. Wilson, Dr. Bieling, and Dr. Zipursky are also with the Mental Health and Addiction Program, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, where Ms. Jeffs and Ms. Madsen are affiliated. Dr. Cunningham is also with the Department of Psychiatry, McMaster Children's Hospital, Hamilton, Ontario. Ms. Chen is also with the School of Business, University of Alberta, Edmonton, Alberta. Dr. Christensen is with the Research School of Psychology, Australian National University, Canberra, Australia. Send correspondence to Dr. Zipursky (e-mail: [email protected]).
Victoria Madsen, M.A.
Ms. Becker is with the Department of Psychology, Neuroscience and Behaviour, and Dr. Cunningham, Ms. Furimsky, Ms. Rimas, Ms. Wilson, Dr. Bieling, Ms. Chen, Ms. Mielko, and Dr. Zipursky are with the Department of Psychiatry and Behavioural Neurosciences, all at McMaster University, Hamilton, Ontario, Canada. Ms. Furimsky, Ms. Wilson, Dr. Bieling, and Dr. Zipursky are also with the Mental Health and Addiction Program, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, where Ms. Jeffs and Ms. Madsen are affiliated. Dr. Cunningham is also with the Department of Psychiatry, McMaster Children's Hospital, Hamilton, Ontario. Ms. Chen is also with the School of Business, University of Alberta, Edmonton, Alberta. Dr. Christensen is with the Research School of Psychology, Australian National University, Canberra, Australia. Send correspondence to Dr. Zipursky (e-mail: [email protected]).
Yvonne Y. S. Chen, M.Sc., M.B.A.
Ms. Becker is with the Department of Psychology, Neuroscience and Behaviour, and Dr. Cunningham, Ms. Furimsky, Ms. Rimas, Ms. Wilson, Dr. Bieling, Ms. Chen, Ms. Mielko, and Dr. Zipursky are with the Department of Psychiatry and Behavioural Neurosciences, all at McMaster University, Hamilton, Ontario, Canada. Ms. Furimsky, Ms. Wilson, Dr. Bieling, and Dr. Zipursky are also with the Mental Health and Addiction Program, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, where Ms. Jeffs and Ms. Madsen are affiliated. Dr. Cunningham is also with the Department of Psychiatry, McMaster Children's Hospital, Hamilton, Ontario. Ms. Chen is also with the School of Business, University of Alberta, Edmonton, Alberta. Dr. Christensen is with the Research School of Psychology, Australian National University, Canberra, Australia. Send correspondence to Dr. Zipursky (e-mail: [email protected]).
Stephanie Mielko, B.A.
Ms. Becker is with the Department of Psychology, Neuroscience and Behaviour, and Dr. Cunningham, Ms. Furimsky, Ms. Rimas, Ms. Wilson, Dr. Bieling, Ms. Chen, Ms. Mielko, and Dr. Zipursky are with the Department of Psychiatry and Behavioural Neurosciences, all at McMaster University, Hamilton, Ontario, Canada. Ms. Furimsky, Ms. Wilson, Dr. Bieling, and Dr. Zipursky are also with the Mental Health and Addiction Program, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, where Ms. Jeffs and Ms. Madsen are affiliated. Dr. Cunningham is also with the Department of Psychiatry, McMaster Children's Hospital, Hamilton, Ontario. Ms. Chen is also with the School of Business, University of Alberta, Edmonton, Alberta. Dr. Christensen is with the Research School of Psychology, Australian National University, Canberra, Australia. Send correspondence to Dr. Zipursky (e-mail: [email protected]).
Robert B. Zipursky, M.D.
Ms. Becker is with the Department of Psychology, Neuroscience and Behaviour, and Dr. Cunningham, Ms. Furimsky, Ms. Rimas, Ms. Wilson, Dr. Bieling, Ms. Chen, Ms. Mielko, and Dr. Zipursky are with the Department of Psychiatry and Behavioural Neurosciences, all at McMaster University, Hamilton, Ontario, Canada. Ms. Furimsky, Ms. Wilson, Dr. Bieling, and Dr. Zipursky are also with the Mental Health and Addiction Program, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, where Ms. Jeffs and Ms. Madsen are affiliated. Dr. Cunningham is also with the Department of Psychiatry, McMaster Children's Hospital, Hamilton, Ontario. Ms. Chen is also with the School of Business, University of Alberta, Edmonton, Alberta. Dr. Christensen is with the Research School of Psychology, Australian National University, Canberra, Australia. Send correspondence to Dr. Zipursky (e-mail: [email protected]).

Notes

Some of the results were presented at the Schizophrenia International Research Society Conference, Florence, Italy, April 14–18, 2012; the International Conference on Patient- and Family-Centered Care, Washington, D.C., June 4–6, 2012; the Canadian Psychological Association Convention, Halifax, Nova Scotia, June 14–16, 2012; and the annual meeting of the American Psychopathological Association, New York City, March 6–8, 2014.

Competing Interests

Dr. Cunningham receives salary support and is a shareholder in BCFPI Inc., which provides mental health screening and outcome measures for children and adolescents. Dr. Zipursky has received research support or speaking or consulting fees from Janssen Pharmaceuticals, Inc., Lundbeck, Otsuka Pharmaceutical, and Roche. The other authors report no financial relationships with commercial interests.

Funding Information

Canadian Health Services Research Foundation
This research was funded by the Canadian Health Services Research Foundation; a Joseph-Armand Bombardier Canada Graduate Scholarship–Master’s Scholarship from the Social Sciences and Humanities Research Council of Canada (Ms. Becker); and the Jack Laidlaw Chair in Patient-Centered Health Care, Faculty of Health Sciences at McMaster University (Dr. Cunningham).

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