Mental Health Clinicians’ Motivation to Invest in Training: Results From a Practice-Based Research Network Survey

Evidence-based treatments hold promise for meeting the mental health needs of children and youths, but only if clinicians implement them. A growing body of literature focuses on training professionals to deliver evidence-based treatments (1–3). The most consistent finding is that passive approaches to training, such as one-shot workshops or distribution of manuals, may increase provider knowledge and predispose clinicians toward the uptake of a treatment, but they do not consistently produce provider behavior change (1–5).

Effective training approaches seem to involve multicomponent packages of elements, such as a treatment manual, multiple days of intensive workshop training, expert consultation, live or taped review of client sessions, supervisor training sessions, booster training sessions, and completion of one or more training cases (2). Others assert that training should be dynamic, active, and targeted to meet the needs of individuals with different learning styles (5); utilize behavioral rehearsal (1); and include ongoing supervision, consultation, and feedback (1,2,4).

These multicomponent approaches require substantial investments of time and money. Although previous studies have noted that investments of time and money are a potential barrier to the receipt of training (6), a better understanding of the clinician-level factors that influence the receipt of training is needed (2,3,7). The purpose of this study was to examine what motivates and deters clinicians from participating in training and how much time and money they are willing to spend to learn new treatments. To address these questions, we utilized the Missouri Therapy Network (MTN), a practice-based research network of mental health clinicians who provide psychological assessment or psychotherapy services for children and who are reimbursed through Missouri Medicaid.

Methods

In fall 2009, MTN members (N=816) were contacted via e-mail and mail and invited to complete a Web-based survey related to clinical training. Of the 816 clinicians who were sent the survey, 364 (45%) responded. For this study, we used the responses of 318 clinicians (39%) who had also completed an earlier survey detailing their demographic characteristics, caseloads, and income.

Because of the paucity of studies on this topic, we developed a novel survey that was informed by emerging models of implementation research (8,9) and was reviewed for relevance by members of the MTN’s clinician advisory board. The survey consisted of 18 items regarding the factors that motivate a clinician to seek training, 22 items about potential “deal breakers,” and two questions about the amount of time and money a clinician would be willing to invest to learn a new therapy.

The items covering potential motivators were scored on a 4-point Likert scale that ranged from 1, very unlike me, to 4, very much like me. Identification of deal breakers was based on a dichotomous choice of yes or no.

All survey data were downloaded in Excel spreadsheets and converted to SAS, version 9.2. Demographic data were summarized by using frequencies and percentages, and differences between respondents and nonrespondents were calculated by using chi square and t tests. Simple descriptive statistics were used to report data for both motivators and deal breakers. Ordinary least-squares (OLS) regression was used to determine whether any of the demographic variables were predictive of the amount of time or money that clinicians would spend learning a new therapy.

Results

The 318 respondents had a mean±SD age of 48.35±11.13 and 14.14±8.82 years of practice experience. A total of 238 (75%) were female, 285 (90%) were Caucasian, and 27 (8%) were African American. They were primarily master’s- (N=226, 71%) and doctoral-level (N=81, 25%) clinicians holding licensure in counseling (N=124, 39%), social work (N=119, 37%), psychology (N=68, 21%), and nursing (N=7, 2%). They worked primarily in urban areas in agency (N=122, 38%), private (N=135, 42%), and both agency and private (N=61, 19%) settings. Approximately 41% (N=129) of the clinicians earned over $50,000 per year from their therapy practice, and 52%±33% of their clients were enrolled in Medicaid.

Respondents were older than nonrespondents (48.35 versus 45.42 years, t=–3.50, df=783, p<.001), had more years of professional experience (14.14 versus 11.91, t=−3.49, df=767, p<.001), and practiced in counties with a lower percentage of the population living in urban areas (10) (79% versus 85%, t=3.07, df=725, p<.01). There were no other significant differences between respondents and nonrespondents with respect to gender, income, race-ethnicity, discipline, percentage of clients enrolled in Medicaid, or type of practice setting.

Three of the most highly endorsed motivators related to whether clinicians thought the training would be a good fit for the kinds of clients that they see. In addition, the clinicians frequently endorsed the availability of continuing education credit as a motivator. Participants were not motivated to attend training aimed at beginning clinicians, nor were they motivated by the opportunity to charge more money for their services following training. [Descriptive statistics for the motivators and deal breakers are available online in a data supplement to this report.]

Approximately 25% of respondents indicated providing training (N=82) and supervision (N=77) solely over the Internet was a deal breaker. Twenty percent to 23% of clinicians indicated that their deal breakers included potential impingements upon their autonomy, such as incongruities between their theoretical orientation and the intervention covered by the training (N=63) and having to follow a session-by-session treatment manual (N=74). Clinicians were willing to invest a wide range of time to be trained (range 0 to 6,400 hours; median=24). The number of hours clinicians were willing to invest (58.69±369.08) was highly influenced by three outliers. Recoding the three highest values (720, 1,440, and 6,400 hours) to the next highest value (320 hours) reduced the mean to 34.79±45.20. We ran the OLS regression with these values transformed. Controlling for other variables, the regression showed that nonwhite clinicians were willing to spend 17.74 more hours in training than white clinicians. Further, clinicians working in both agency and private practice settings were willing to spend 16.24 fewer hours in training than clinicians working only in private practice (Table 1).

The amount of money clinicians would spend for training ($386.17±$503.53) was highly influenced by a small number of outliers. Recoding the four highest spenders to the next highest spender ($1,500, which four clinicians would pay) reduced the amount to $359.44±$343.76. The OLS model used the four transformed values (Table 1). For every 1% increase in the percentage of clients in a clinician’s caseload who were Medicaid recipients, there was a $1.86 reduction in the amount that they would pay for training. Conversely, moving from an annual salary of less than $50,000 to greater than $50,000 was associated with a willingness to pay $117.11 more for training. Finally, clinicians who worked in agency and private practice were willing to spend $117.35 less on training than clinicians who worked in private practice exclusively.

Discussion

Clinicians were willing to participate in training with only a few caveats, namely that it be relevant to their clients, that it offer continuing education credits, and that it not be aimed at beginning clinicians. The latter finding is consistent with research showing that psychologists are not interested in “basic” trainings (6) and comports with the age and experience of the respondents. Manualized treatments and required supervision were deal breakers for only a small number of clinicians.

The clinicians, however, were not willing to invest the time and money that many evidence-based treatments require. This was particularly true for clinicians who served a higher percentage of Medicaid recipients, had lower incomes, or who worked in both agency and private settings, perhaps due to financial necessity. Our results highlight the need for more thought about how to cover the costs of training. It may be unrealistic for clinicians to bear the full cost of training; the costs may need to be paid by employers or subsidized by governments and private foundations.

Our results also suggest a need to develop less expensive training initiatives that sacrifice little in terms of intensity. One option is to deliver training over the Internet (4,6). Didactic approaches could be supplemented with expert supervision and consultation, perhaps using a group format to enhance cost-effectiveness and efficiency. Our findings are consistent with other studies that suggest Web-based trainings are acceptable to a majority of clinicians (2).

Several limitations of this study should be considered. We obtained a modest response rate, and it is possible that response was related to the variables of interest. For instance, the respondents may have been more motivated than nonrespondents to invest in training, making the identified barriers to training all the more salient. The survey was conducted in a Midwestern state with no policy mandate or support for evidence-based treatments and may not be generalizable to other states with different policies. We were also unable to rely upon an established measurement protocol. It is possible that specifying a specific treatment or diagnosis would have influenced clinicians’ willingness to invest in training. Finally, there are limits associated with self-report, given that the extent to which attitudes predict practice behavior is not well established and that self-report has been shown to be discordant with observer ratings (11).

Conclusions

Further research should examine the amount of time and money that clinicians actually spend on training and use qualitative methods to examine factors that may enhance clinicians’ motivation to attend training. Ultimately, the successful implementation of evidence-based treatments will necessitate innovative approaches to financing and providing intensive training.