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

Abstract

Objectives:
Little is known about why clinicians seek training or about their willingness to invest in it.

Methods:
Results from a Web-based survey of 318 clinicians in a practice-based research network were used to examine factors that motivate clinicians to seek training or forgo training ("deal breakers") and their willingness to invest time and money in training.

Results:
Clinicians desired training that teaches advanced versus basic clinical skills, that covers an area they see as central to the needs of their clients, and that provides continuing education credit. Training that requires clinical supervision or the use of a manualized intervention was not a deal breaker for most clinicians. However, the amount of time and money most clinicians reported being willing to invest in training fell far short of the requirements for learning most evidence-based treatments.

Conclusions:
Training strategies that combine high intensity with lower cost may be needed.

Full text

Mental Health Clinicians’Motivation
to Invest in Training: Results From a
Practice-Based Research Network Survey
Byron J. Powell, A.M.
J. Curtis McMillen, Ph.D.
Kristin M. Hawley, Ph.D.
Enola K. Proctor, Ph.D.
Objectives: Little is known about
why clinicians seek training or
about their willingness to invest in
it. Methods: Results from a Web-
based survey of 318 clinicians in
a practice-based research network
were used to examine factors that
motivate clinicians to seek training
or forgo training (“deal breakers”)
and their willingness to invest time
and money in training. Results:
Clinicians desired training that
teaches advanced versus basic
clinical skills, that covers an area
they see as central to the needs of
their clients, and that provides
continuing education credit.
Training that requires clinical
supervision or the use of a man-
ualized intervention was not
a deal breaker for most clinicians.
However, the amount of time and
money most clinicians reported
being willing to invest in training
fell far short of the requirements
for learning most evidence-based
treatments. Conclusions: Train-
ing strategies that combine high
intensity with lower cost may be
needed. (Psychiatric Services 64:
816–818, 2013; doi: 10.1176/appi.
ps.003602012)
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 man-
uals, 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 man-
ual, multiple days of intensive work-
shop 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 feed-
back (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 pur-
pose 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 psycho-
therapy services for children and who
are reimbursed through Missouri
Medicaid.
Methods
In fall 2009, MTN members (N5816)
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 com-
pleted 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 break-
ers,”and two questions about the
amount of time and money a clinician
Mr. Powell and Dr. Proctor are affiliated
with the Brown School of Social Work,
Washington University in St. Louis, One
Brookings Drive, Campus Box 1196, St.
Louis, MO 63130 (e-mail: bjpowell@wustl.
edu). Dr. McMillen is with the School of
Social Service Administration, University
of Chicago, Chicago. Dr. Hawley is with
the Department of Psychological Sciences,
University of Missouri, Columbia.
816 PSYCHIATRIC SERVICES 'ps.psychiatryonline.org 'August 2013 Vol. 64 No. 8

would be willing to invest to learn
a new therapy.
The items covering potential moti-
vators were scored on a 4-point Likert
scale that ranged from 1, very unlike
me, to 4, very much like me. Identi-
fication 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 frequen-
cies and percentages, and differences
between respondents and nonre-
spondents were calculated by using
chi square and t tests. Simple de-
scriptive 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 vari-
ables were predictive of the amount of
time or money that clinicians would
spend learning a new therapy.
Results
The 318 respondents had a mean6
SD age of 48.35611.13 and 14.146
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- (N5226, 71%)
and doctoral-level (N581, 25%) clini-
cians holding licensure in counseling
(N5124, 39%), social work (N5119,
37%), psychology (N568, 21%), and
nursing (N57, 2%). They worked
primarily in urban areas in agency
(N5122, 38%), private (N5135, 42%),
and both agency and private (N561,
19%) settings. Approximately 41%
(N5129) of the clinicians earned over
$50,000 per year from their therapy
practice, and 52%633% of their clients
were enrolled in Medicaid.
Respondents were older than non-
respondents (48.35 versus 45.42
years, t5–3.50, df5783, p,.001),
had more years of professional expe-
rience (14.14 versus 11.91, t523.49,
df5767, p,.001), and practiced in
counties with a lower percentage of
the population living in urban areas
(10) (79% versus 85%, t53.07, df5725,
p,.01). There were no other significant
differences between respondents and
nonrespondents with respect to gen-
der, income, race-ethnicity, discipline,
percentage of clients enrolled in Med-
icaid, or type of practice setting.
Three of the most highly endorsed
motivators related to whether clini-
cians 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 moti-
vator. 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. [De-
scriptive statistics for the motivators and
deal breakers are available online in
a data supplement to this report.]
Approximately 25% of respondents
indicated providing training (N582)
and supervision (N577) 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 in-
congruities between their theoretical
orientation and the intervention cov-
ered by the training (N563) and
having to follow a session-by-session
treatment manual (N574). Clinicians
were willing to invest a wide range of
time to be trained (range 0 to 6,400
hours; median524). The number of
hours clinicians were willing to invest
(58.696369.08) was highly influ-
enced 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 34.79645.20. We ran the OLS
regression with these values trans-
formed. Controlling for other varia-
bles, the regression showed that
nonwhite clinicians were willing to
spend 17.74 more hours in training
than white clinicians. Further, clini-
cians working in both agency and
private practice settings were willing
to spend 16.24 fewer hours in train-
ing than clinicians working only in
privatepractice(Table1).
The amount of money clinicians
would spend for training ($386.176
$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 $359.446$343.76. The
OLS model used the four transformed
values (Table 1). For every 1% increase
in the percentage of clients in a clini-
cian’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.
Table 1
Association between characteristics of 318 clinicians and willingness to spend
time and money on training, by ordinary least-squares regression
Time (model 1)
a
Money (model 2)
b
Characteristic b SE (b) bb SE (b) b
Age –.41 .29 –.10 –1.79 2.15 –.06
Years of experience –.06 .37 –.01 2.51 2.76 .06
Medicaid-enrolled clients –.15 .08 .11 –1.86 .59 –.18**
Urban practice 6.73 9.16 .04 –101.45 68.55 –.08
Gender –1.59 6.27 –.02 –1.97 46.91 .00
Income .$50,000 3.58 5.93 .04 117.11 44.41 .17**
Nonwhite 17.74 8.36 .12*–17.37 62.52 –.02
Psychologist –5.08 7.43 –.05 18.02 55.63 .02
Counselor 9.96 5.95 .11 37.58 44.51 .05
Nurse –6.65 17.60 –.02 –61.28 131.70 –.03
Agency only –7.15 5.73 –.08 –47.98 42.86 –.07
Agency and private –16.24 6.95 –.14*–117.35 51.98 –.13*
a
R
2
5.07, adjusted R
2
5.04
b
R
2
5.10,adjusted R
2
5.07
*p,.05, **p,.01
PSYCHIATRIC SERVICES 'ps.psychiatryonline.org 'August 2013 Vol. 64 No. 8 817

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 educa-
tion 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 treat-
ments 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 per-
centage 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
bearthefullcostoftraining;thecosts
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 initia-
tives that sacrifice little in terms of
intensity. One option is to deliver
training over the Internet (4,6). Di-
dactic approaches could be supple-
mented with expert supervision and
consultation, perhaps using a group
format to enhance cost-effectiveness
and efficiency. Our findings are con-
sistent 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 identi-
fied 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 generaliz-
able 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 exam-
ine factors that may enhance clini-
cians’motivation to attend training.
Ultimately, the successful implemen-
tation of evidence-based treatments
will necessitate innovative approaches
to financing and providing intensive
training.
Acknowledgments and disclosures
This work was supported in part by a Doris
Duke Fellowship for the Promotion of Child
Well-Being, by the Washington University In-
stitute of Clinical and Translational Sciences
grants UL1 RR024992 and TL1 RR024995
from the National Center for Research Resources,
and by the National Institute of Mental Health
(P30 MH068579, T32 MH019960, and F31
MH098478).
The authors report no competing interests.
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